INTEGRATION OF LIFE CYCLE ASSESSMENT

INTEGRATION OF LIFE CYCLE ASSESSMENT

AND CONCEPTUAL BUILDING DESIGN

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF

CIVIL AND ENVIRONMENTAL ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

John Paul Basbagill

December 2013

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/vt683fq0708

© 2013 by John Paul Basbagill. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii



http://purl.stanford.edu/vt683fq0708

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Michael Lepech, Primary Adviser


Martin Fischer


Douglas Noble

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost for Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

iii

iv

Abstract

Conceptual building design involves decisions that have significant life cycle environmental

impact and life cycle cost implications. Designers are aware of the importance of creating

sustainable buildings, yet mechanisms are lacking that effectively inform designers of the

impacts of design decisions, specifically during the conceptual phase. Poor design choices

yielding carbon intensive, costly buildings are often discovered only late in the design process,

when design decisions are difficult and costly to modify.

A method is proposed that provides life cycle environmental impact and cost feedback to

designers during the conceptual design phase in a way that better enables designers to make

decisions leading to less carbon intensive and less costly buildings. Critical to the method is the

development of a set of heuristics which calculate the embodied impacts and costs of a full range

of building components. These heuristics require only three inputs typically known during the

conceptual design stage: gross floor area, building location, and building type. Multi-disciplinary

design optimization is then targeted as a point of departure for integrating the heuristics with

building design feedback using an automated approach. The heuristics are integrated specifically

with building information modeling, life cycle assessment, life cycle cost, and energy simulation

software and an automated feedback processor. The processor utilizes an algorithm to generate

life cycle impact feedback on many design alternatives, thereby allowing designers to understand

the full range of impacts possible for a given problem formulation. The method is also

configured to provide feedback specifically for design decisions made sequentially, as is typical

of the building design process, as opposed to providing feedback after all decisions have been

made, a limitation of design processes currently using multi-disciplinary design optimization. In

this way, designers understand the life cycle environmental impact and life cycle cost

v

implications of each decision for a range of building design alternatives and can easily modify

decisions in a way that better aligns with their desired performance objectives.

In summary, the proposed method’s primary contributions to knowledge are the reduction

of inputs required for life cycle assessment during the conceptual design stage to only three

inputs, the formalization of heuristics corresponding to the three inputs that approximate the life

cycle impact of conceptual design decisions, the integration of heuristics with automation in a

way that allows for rapid exploration of the full design space, and the sequential presentation of

life cycle environmental impact and life cycle cost feedback on conceptual building design

decisions.

vi

Acknowledgments

Gratitude is expressed to the numerous individuals and groups who have supported me

over the past four years. I would like to thank my family, including my parents, Pat and Paul

Basbagill, my sister, Marie Clark, my brother-in-law, Rob Clark, my nephew, Robby Clark, and

my niece and god-daughter, Emma Clark. I thank my advisors, Michael Lepech, Martin Fischer,

and Doug Noble, each a source of guidance and inspiration during my graduate studies at

Stanford University and the University of Southern California. I wish to thank the Leavell

Fellowship and the Center for Integrated Facility Engineering for supporting my research. I also

thank my friends and colleagues who have guided me and given me critical feedback throughout

the research process, in particular Forest Flager and members of my research group.

vii

Table of Contents

Abstract………………………………………………...…………………………………............iv

Acknowledgments…………………………………………..……………………………………vi

Table of Contents…………………………………………………………………………...…....vii

List of Figures……………………………………………………………….............……….......xii

List of Tables………………………………………………………………………….……......xvii

List of Appendices……………………………………………………………………………....xix

Chapter 1: Introduction………………………………………………………………………....…1

Observed Problem………………………………………………………………………...……1

Integration of Building Design and Performance Feedback…………………………...……3

Conceptual Design Stage…………………………………………………………...……….4

Life Cycle Assessment and Life Cycle Cost………………………...…………...………….6

Parametric Design……………………………………………………………..…………….9

Integration of Multi-Disciplinary Design Optimization and Sequential

Design Decision-Making Processes………………………………………………...…..13

Survey of Current Use of Life Cycle Assessment in the Building Design Industry…….....14

Defining the Theoretical Gap between LCA and LCC Feedback and Building Design.15

Defining the Practical Gap between LCA and LCC Feedback and Building Design.…17

Part I: Understanding General Trends of LCA and LCC Feedback and Design...…18

Part II: Understanding Firm-Specific Details of LCA and LCC Feedback and

Design……………………………………………………………………………21

Arup…………………………………………………………………………...…22

Atelier Ten……………………………………………………………………….24

Kohn Pedersen Fox………………………………………………………………26

viii

Summary of Key Themes……………………………………………………..…27

Reflection on Use of Life Cycle Assessment in Building Design……………….…...………28

Research Questions……………………………………………………………………..…….30

Organization of the Dissertation…………………………………………………………..….31

Chapter 2: Scope, Model Development, and Validation………………………………..……….33

Scope of the Method…………………………………………………………………..……...34

Building Life Cycle Phases……………………………………………………...………...34

Environmental Impact Indicators…………………………………………………….……35

Building Components, Materials, and Dimensions……………………………………….35

Software Integration Method…………………………………………………………………38

Embodied Impact Calculation……………………………………………………………..40

Operational Impact Calculation…………………………………………………………...42

Feedback Processor………………………………………………………………………..43

Software Used……………………………………………………………………………..43

Validation of Embodied Impact Heuristics…………………………………………….……..44

Chapter 3: Application of Life Cycle Assessment to Early Stage Building Design for Reduced

Embodied Environmental Impacts……………………………………………….…………...51

Abstract……………………………………………………………………………………….51

Introduction…………………………………………………………………………………...52

Literature Review……………………………………………………………………………..55

Methodology………………………………………………………………………………….57

Scope………………………………………………………………………………………57

Building Component Classification Framework………………………………………….60

Analysis Process…………………………………………………………………………..62

ix

Implementation……………………………………………………………………………….66

Problem Formulation……………………………………………………………………...66

Software Integration……………………………………………………………………….68

Results and Discussion…………………………………………………………………….…72

Conclusions…………………………………………………………………………………...78

Chapter 4: Evaluating Embodied Versus Operational Environmental Impact Trade-offs of

Conceptual Building Designs………………………………………………………………...80

Abstract……………………………………………………………………………………….80

Introduction…………………………………………………………………………………...81

Related Studies………………………………………………………………………………..84

Methodology………………………………………………………………………………….86

Case Study……………………………………………………………………………………91

Problem Formulation……………………………………………………………………...91

Results……………………………………………………………………………………..93

Comparison of Optimization Objectives……………………………………………....93

Analysis of Trade-off Variables………………………………………………………..97

Sensitivity Analysis…………………………………………………………………………103

Cladding Material………………………………………………………………………..104

Climate…………………………………………………………………………………...110

Building Size……………………………………………………………………………..117

Conclusions………………………………………………………………………………….124

Chapter 5: A Methodology for Providing Environmental Impact Feedback on Sequential

Conceptual Building Design Decisions……………………………………………………..126

Abstract……………………………………………………………………………………...126

Introduction………………………………………………………………………………….127

x

Related Studies………………………………………………………………………………131

Methodology…………………………………………………………………………….......133

Case Study…………………………………………………………………………………..138

Results……………………………………………………………………………………….140

Sequential Decision-Making Approach I: Minimization of Total Environmental

Impact………………………………………………………………………………...142

Sequential Decision-Making Approach II: Achievement of Carbon Performance

Value………………………………………………………………………………….144

Sequential Decision-Making Approach III: Maximization of Design Freedom………...146

Validation……………………………………………………………………………………148

Conclusions………………………………………………………………………………….150

Chapter 6: A Multi-Objective Feedback Approach to Evaluating Sequential Building Design

Decisions…………………………………………………………………………………….153

Abstract…………………………………………………………………………………...…153

Introduction………………………………………………………………………………….154

Related Studies………………………………………………………………………………158

Methodology………………………………………………………………………………...160

Scope……………………………………………………………………………………..160

Analysis Process…………………………………………………………………………162

Inspection of Results……………………………………………………………………..166

Software Implementation…………………………………………………………………167

Case Study…………………………………………………………………………………..167

Results…………………………………………………………………………………….....169

Conclusions………………………………………………………………………………….180

Chapter 7: Conclusions………………………………………………………………………....183

xi

Summary of Conclusions from Chapters 3 through 6………………………………………183

Contributions………………………………………………………………………..............185

Embodied Impact Heuristics……………………………………………………………..186

Integration of Automated Feedback and Sequential Decisions……………………….....188

Range of Control of Building Performance Alternatives………………………………..190

Answers to Research Questions…………………………………………………………......191

Challenges and Recommendations………………………………………………………….193

Limitations and Future Work……………………………………………………………..…194

Appendices……………………………………………………………………………………...198

References…………………………………………………………………………....................210

xii

List of Figures

Figure 1 – Design completion and uncertainty from pre-design through construction of a building

project (Struck and Hensen 2007)………………………………………………………...…....6

Figure 2 – Frequency that designers perform various sustainable design strategies on building

projects……………………………………………………………………………………..…19

Figure 3 – Stages during which designers make various building decisions compared with

American Institute of Architects guidelines……………………………………………….....20

Figure 4 – Barriers inhibiting design firms’ use of LCA on building projects…………………..21

Figure 5 – Building life cycle phases included in the BIM-enabled LCA-LCC feedback

method………………………………………………………………….……………………..35

Figure 6 – Architectural Design And Performance Tool (ADAPT): software integration tool

providing life cycle embodied impact, operational impact, and cost feedback on conceptual

building designs……………………………………………………………………………....38

Figure 7 – Comparison of embodied impact values generated from a detailed LCA case study

versus ADAPT for a 1,358-m2 building …………..…………………………………...…….47


versus ADAPT for a 2,500-m2 building …………..……………………...……………...…..47


versus ADAPT for a 2,900-m2 building …………………………………………..…..……..48


versus ADAPT for a 8,458-m2 building……………..………………………..……….....…..48


versus ADAPT for a 22,982-m2 building…………………………………...……..………....49

xiii

Figure 12 - Comparison of embodied impact values generated from a detailed LCA case study

versus ADAPT for a 34,910-m2 building…………………………………………...…….….49


versus ADAPT for a 47,250-m2 building………………………………………………....….50


versus ADAPT for a 85,000-m2 building…………………………………………...…….….50

Figure 15 – Building life cycle phases included in scope…..…………………………………....59

Figure 16 – Software integration for embodied impact feedback method…………….....………62

Figure 17 – Embodied impact reduction due to material decisions……………………………...76

Figure 18 – Embodied impact reduction due to thickness decisions………………………….....77

Figure 19 – Building life cycle phases included in scope………………………………......……87

Figure 20 – Software integration for optimized life cycle environmental impact feedback…….89

Figure 21 – Distribution of optimized design configurations………………………………...….94

Figure 22 – Distribution of life cycle environmental impacts……...……………………………95

Figure 23 – Distribution of window-to-wall ratio values………………………………………..98

Figure 24 – Distribution of glazing thickness values…………………………………………….99

Figure 25 – Distribution of presence of fins values……………………..……………………...100

Figure 26 – Distribution of presence of overhangs values………………..……………………101

Figure 27 – Distribution of fin depth values……………………………………...…………….102

Figure 28 – Distribution of overhang depth values…………………..……………………...…102

Figure 29 – Distribution of optimized design configurations for alternate cladding material….105

Figure 30 – Distribution of life cycle environmental impacts for alternate cladding material…106

Figure 31 – Distribution of window-to-wall ratio values for alternate cladding material…...…107

xiv

Figure 32 – Distribution of glazing thickness values for alternate cladding material………….108

Figure 33 – Distribution of presence of fins values for alternate cladding material……………108

Figure 34 – Distribution of presence of overhangs values for alternate cladding material…….109

Figure 35 – Distribution of fin depth values for alternate cladding material…………………...109

Figure 36 – Distribution of overhang depth values for alternate cladding material……………110

Figure 37 – Distribution of optimized design configurations for alternate climate……….…....112

Figure 38 – Distribution of life cycle environmental impacts for alternate climate….……...…113

Figure 39 – Distribution of window-to-wall ratio values for alternate climate…………..…….114

Figure 40 – Distribution of glazing thickness values for alternate climate………………….…115

Figure 41 – Distribution of presence of fins values for alternate climate……………………....115

Figure 42 – Distribution of presence of overhangs values for alternate climate…………….....116

Figure 43 – Distribution of fin depth values for alternate climate……………………….…..…116

Figure 44 – Distribution of overhang depth values for alternate climate……………………....117

Figure 45 – Distribution of optimized design configurations for alternate building size……....119

Figure 46 – Distribution of life cycle environmental impacts for alternate building size…...…119

Figure 47 – Distribution of window-to-wall ratio values for alternate building size……….….121

Figure 48 – Distribution of glazing thickness values for alternate building size………..….…..121

Figure 49 – Distribution of presence of fins values for alternate building size………...……....122

Figure 50 – Distribution of presence of overhangs values for alternate building size……........122

Figure 51 – Distribution of fin depth values for alternate building size………………......……123

Figure 52 – Distribution of overhang depth values for alternate building size………….…......123

Figure 53 – Three sequential decision-making approaches to which the environmental

impact feedback method may apply: (a) minimization of carbon footprint,

xv

(b) achievement of a carbon target value, and (c) maintenance of freedom and

flexibility…………………………………………………………………………………….130

Figure 54 – Building life cycle phases included in proposed method for providing environmental

impact feedback on sequential design decisions………….……..………………………..…134

Figure 55 – Three design alternatives generated by the building information modeling software

showing variations in several input parameters……………………………………………..135

Figure 56 – Method for providing probabilistic environmental impact feedback on sequential

building designs……………………………………………………………………………..137

Figure 57 – Probability mass function characterizing a design space size of 3.69x1023, showing

total environmental impacts for 8,689 selected designs prior to any design decisions……..141

Figure 58 – Probability impact distributions for four sequential decisions for the objective

minimizing total environmental impact.………………………………………...……...…...143

Figure 59 – Impact distributions for first four decisions for the objective achieving a carbon

performance value.………………………………………………………………...………...145

Figure 60 – Impact distributions for first four decisions for the objective maximizing

design freedom………………………………………...…………………….…………...….147

Figure 61 – Distribution of life cycle environmental impacts for alternate Latin hypercube

sampling algorithm…………………………………….……………………………..……..149

Figure 62 – Combined distribution of life cycle environmental impacts for original and

alternate sampling algorithms.…………………………………………………....………....150

Figure 63 – Three sequential decision-making design strategies to which designers might apply

the multi-objective feedback method: (a) minimization of carbon footprint,

(b) achievement of an environmental impact performance target, and (c) maintenance of

xvi

design freedom……………………………………………………………..……….……….156

Figure 64 – Building life cycle phases included in proposed method for providing multi-

objective feedback on sequential design decisions……………………………………….…162

Figure 65 – Automated method for providing life cycle environmental impact and life cycle

cost feedback on sequential building design decisions……………………...………………163

Figure 66 – Three design alternatives generated by the building information modeling

software showing variations in several input parameters………………………….………..164

Figure 67 – Distribution of building life cycle environmental impacts and life cycle costs

for a design space size of 6.07x1016

…………………….………………………….………171

Figure 68 – Distribution of life cycle environmental impacts and life cycle costs

after decision 1: number of buildings equals 3……………………………...………………173


after decision 2: low e-glazing……………………………...…………………………….…174


after decision 3: window-to-wall ratio equals 50……………………………………………176


after decision 3 (revised): window-to-wall ratio equals 15………………………..…..……177


after decision 4: orientation from 0° to 180°.……………………………………………….179

xvii

List of Tables

Table 1 – Building component classification framework………………….……..……………...37

Table 2 – Required inputs, variables, and assumptions for BIM-enabled automated feedback

method for life cycle assessment and life cycle cost…………………………………..……..39

Table 3 – Comparison of embodied impact values for eight building case studies: ADAPT

versus Arup (2013)……………………………………………...………………………....…45

Table 4 – Building component classification framework………...……………………………...61

Table 5 – Required inputs, variables, and assumptions for building information modeling-

enabled embodied impact feedback method………………………………………………….63

Table 6 – Problem formulation showing required inputs, variable values, and assumptions…....68

Table 7 – Impact allocation scheme and impact reduction scheme……………………………...74

Table 8 – Ranking scheme for material decisions achieving embodied impact reductions…......75

Table 9 – Ranking scheme for thickness decisions achieving embodied impact reductions…….75

Table 10 – Optimization problem formulation describing objectives and variable values……...92

Table 11 – Material, dimensional, and impact assumptions for building components with

embodied versus operational impact trade-offs………………………………………………93

Table 12 – Comparison of trade-off variable values and carbon impacts……………………….97

Table 13 – Comparison of trade-off variable values and carbon impacts for alternate

cladding material…………………..………………………………………………………...106


climate………………….………………………………………………………….……...…113


building size…………………………….…………………………………………………...120

xviii

Table 16 – Case study variables used to characterize building life cycle environmental

impacts………………………………………………………………………………………140

Table 17 – Metrics characterizing the design space for a design strategy minimizing

environmental impacts…………………………………….………………………………...144

Table 18 – Metrics characterizing the design space for a design strategy achieving a

performance value……………………………...………………………………………........146

Table 19 – Metrics characterizing the design space for a design strategy maximizing

design freedom………………………………………………...…………………………….148

Table 20 – Validation of impact distributions using alternate sampling method………………150

Table 21 – Required inputs and variables for automated life cycle environmental impact

and life cycle cost feedback method…………………………...……………………………168

Table 22 – Assumptions for automated life cycle environmental impact and life cycle cost

feedback method…………………………………………………………………………….168

Table 23 – Metrics characterizing the design space for two design strategies:

(a) achieving a life cycle environmental impact performance value and (b) minimizing life

cycle cost………………………………….………………………………………...……….180

xix

List of Appendices

Appendix 1 – Supporting Data for Chapter 1……………………………………………..……198

Survey Questions Provided to Design Firms to Gauge Trends in Use of LCA and LCC

Feedback in Design………………………………………………………………………199

Appendix 2 – Supporting Data for Chapters 2 and 3………………………………..……….....199

Material Alternatives Considered in Quantifying Embodied Impacts of Building

Components…………………………………………………………………………...…199

Material(s) Associated with Each Building Component and Material Properties Used to

Quantify Building Component Embodied Impacts……………………………………....200

Embodied Impact Heuristics Developed for Each Building Component…………………...201

Appendix 3 – Supporting Data for Chapter 5………………………………………..…………204

Variables and Variable Values Used as Inputs for Environmental Impact Feedback

Method…………………………………………………………………………………...204

Appendix 4 – Supporting Data for Chapter 6………………………………………..…………206

Variables and Variable Values Used as Inputs for Environmental Impact and Cost

Feedback………………………………………………………………………………....206

Sample Cost Formulas……………………………………………………………………….208

1

Chapter 1: Introduction

Observed Problem

Buildings consume considerable amounts of energy and materials and have significant

economic impacts. They account for 41% of the total energy consumption in the United States

(Fumo et al. 2010) and heavily contribute to greenhouse gas emissions levels (Sharma et al.

2011). In 2007, new residential and commercial construction expenditures in the United States

totaled $759 billion, and in 2010 the energy to operate new facilities in the United States

totaled over $431 billion (USDOE 2011a). Buildings also account for 72% of total electricity

consumption in the United States (USDOE 2011a), due for the most part to energy use during the

occupancy phase (Radhi 2010). Energy consumption during this phase typically accounts for 80-

85% of a building’s total life cycle energy (Adalberth et al. 2001). Embodied environmental

impacts generated by a building during its life cycle may also be significant (Fay et al. 2000,

Bribian et al. 2009) and, in cases where buildings have been designed for low- or net-zero

energy, can approach use phase impacts (Citherlet 2001, Thormark 2002, Winther and Hestnes

1999). Building lifetimes extend for many decades, and their environmental and economic

impacts occur at global, local, and indoor scales, suggesting the importance of considering

buildings’ entire life cycle when reducing their impacts (Tucker et al. 2003).

These impacts arise because buildings suffer from a host of poor performance issues.

Inefficient operation of building systems is common, such as wasteful conditioning of air, poor

air-tightness, lack of energy management, and a tendency for systems to default to “on” (Bordass

et al. 2001). Poor performance is also related to design flaws, such as redundant structural

elements, inefficient planning and circulation, ineffective shading devices, and other design

2

features that yield little reductions in energy use (Fay et al. 2000). Embodied energy

performance of buildings, which involves the energy required to mine raw materials,

manufacture building components, transport the components, and construct and demolish

buildings, may also be poor (Lawson et al. 1995). Strategies for reducing embodied impacts

include substituting low energy intensity materials for high energy intensity materials, reducing

construction waste, and using materials with a high recycled content (Fay et al. 2000).

In part, these performance issues are due to the lack of feedback designers receive,

particularly during the critical early stages of the design process (Chaszar 2003, Schlueter and

Thesseling 2009). Designers make many design decisions at this stage, and tools that provide

quantitative assessment of the impacts of these decisions are lacking. As a result, designers are

not fully informed of the life cycle impact implications of their design decisions, and this may

yield buildings with large carbon and cost footprints.

Fortunately, the architecture, engineering and construction (AEC) industry has shown

interest over the past few decades in improving the performance of buildings from a life cycle

impact standpoint (Bribian et al. 2009), in large part due to the many buildings that are designed,

built, renovated, and demolished each year. The total building stock in the United States is

approximately 300 billion square feet, and each year developers construct approximately five

billion square feet, demolish 1.75 billion square feet, and renovate five billion square feet of

commercial, residential, industrial, and institutional building space (Winter 2008). By the year

2035, approximately 75% of the built environment will be either new or renovated (USEIA

2010). Therefore, a tremendous opportunity exists for devising methods that provide designers

feedback on the life cycle performance of their building projects. The hope is that designers will

use this feedback to design less costly buildings with lower global warming potential, thereby

3

contributing towards the creation of a healthier and more sustainable global environment. The

challenge is to create a method that integrates life cycle assessment feedback – which typically

requires highly detailed inputs and may be difficult to interpret – with building design decisions

quickly, easily, and intuitively during the critical earliest stages of the design process.

Integration of building design and performance feedback

Large numbers of software tools integrating design with building performance have

existed in the AEC industry for over 30 years, and their shortcomings have been well

documented (Norton et al.1995, Augenbroe 1992, Augenbroe and Winkelmann 1990,

Papamichael 1991). The tools have been described as technically too complex for early stage

design, which has led to limited or ineffective use (van Hattem 1987). This is surprising, given

that architects’ involvement on projects typically precedes the involvement of other specialists,

such as lighting designers and mechanical, electrical, and plumbing engineers, and they would

therefore benefit from simple, intuitive feedback tools. Instead, the tools lack a designer-friendly

interface and have been more suitable for those with specialized knowledge of lighting,

structural, HVAC, or other building systems. In addition, the uniqueness and complexity of

building projects has prevented the creation of simplified design-feedback tools in the AEC

industry. This contrasts with such industries as aerospace or automotive, in which uniformity in

design of component parts has allowed design-feedback tools to more easily gain adoption

(AIAA 1991). Another reason performance feedback tools have lacked the ability to help create

low carbon, low cost buildings is their lack of analytical breadth: no one tool has integrated many

building performance aspects. Instead, each tool typically focuses on a single aspect of

improving building performance, yet often these aspects are related. In terms of environmental

4

performance feedback, the tools also typically quantify operational energy but often ignore

embodied energy considerations.

The US Department of Energy website currently lists 411 building software tools for

evaluating energy efficiency, each varying in terms of complexity and breadth (USDOE 2011b).

Yet only a few tools account for impacts due to both embodied and operational energy. Many of

the tools require detailed inputs and are more appropriate for use after the conceptual design

stage. This is problematic, since many building design parameters are determined by the late

design stages, and changes can be costly (Morbitzer et al. 2001). Designers have fewer design

alternatives to consider and designers’ role in the creation of sustainable buildings is weaker than

if tools existed that provided life cycle impact feedback specifically during the conceptual design

stage.

Conceptual design stage

The conceptual stage of the building design process has been recognized as a critical

stage for determining a building project’s life cycle impacts (Yohanis and Norton 2006).

Decisions made in early stages should be attended to with the most care, since early decisions –

such as shape, orientation, or distribution of glazing – largely determine a building’s

environmental impact or cost (Azhar et al. 2010). Unfortunately, energy simulation analysis is

often relegated to the post-design process (Kienzl et al. 2003), even though changing a building’s

shape, orientation, or envelope configuration during the early design stages has been shown to

reduce energy consumption by 30-40% at no extra cost (Baker and Steemers 2000, Cofaigh et al.

1999). Massing changes made in late design stages have also been shown to have considerable

environmental ramifications, but at high economic costs (Ellis et al. 2008).

5

Successful integration of life cycle feedback and building design requires designers and

engineers to collaborate during the conceptual design stage (Bribian et al. 2009), and this may

include owners, contractors, suppliers, building users, and design professionals (Alsamsam et al.

2008). This effort can be difficult to coordinate. In traditional architectural workflow, building

performance assessment is conducted by engineers or consultants subsequent to the design

(Schlueter and Thesseling 2009). This results in fragmented interactions and unnecessary lag

time between early design exploration and environmental feedback (Turpin 2007). Decisions

made in the early design stages are uninformed and result in potentially poor building

performance. Projects are divided into separate phases performed by different teams,

communication is often poor within the teams, exchange of information is limited, and when

information is dispersed it is often inaccurate and not current (Ospina-Alvarado and Castro-

Lacouture 2010). The transfer of information is cumbersome and prone to delays, and designers

often backtrack in their work, slowing design process momentum.

Another problem with current architectural workflow is that designers typically

investigate only a limited number of options during the early design stages before selecting one

for detailed consideration. Designers often fail to consider a breadth of options for a given

problem configuration, and this approach to conceptual design has been called an “exploitation”

rather than an “exploration” of design options (Grierson and Khajehpour 2002, p. 83). The lack

of consideration of many design alternatives is unfortunate, given the vast number of options

typically possible during the conceptual phase due to the openness of the problem definition.

Decisions are pushed to later design stages as a result of this lack of conceptual design feedback,

and this is likely to increase project impacts. The result is the creation of a building that is more

costly, both environmentally and economically, than if life cycle impact feedback on many

6

alternatives had been available at the conceptual design stage (Grierson and Khajehpour 2002).

Figure 1 describes the stages of the building design process in terms of uncertainty.

Opportunities for improving a building’s performance are greatest at the conceptual design stage,

when designers have the least understanding of, yet greatest ability to manipulate, the building

form. A large number of design scenarios are available to designers, and decisions made at the

early stages have the most significant environmental impact or cost implications. When these

decisions are made without performance feedback or deferred to later design stages,

environmental and cost impacts have the potential to be much greater (Schlueter and Thesseling

2009).

Figure 1 – Design completion and uncertainty from pre-design through construction of a building

project (Struck and Hensen 2007).

Life cycle assessment and life cycle cost

Efforts to improve building performance should consider the entire life cycle energy

requirements and costs of a building. Much research has focused on the operational phase, as

shown in the many simulation tools available on the US Department of Energy’s website.

Embodied energy has been largely neglected, even though embodied energy can comprise up to

7

40% of a building’s total impact (Winther and Hestnes 1999). In fact, research has shown that

integration of embodied impact feedback with design tools can be important to the creation of

sustainable architecture (Sandrolini and Franzoni 2009, Eštoková et al. 2011).

Life cycle assessment (LCA) and life cycle cost (LCC) are methods that account for a

building’s environmental, social, and economic impacts over its entire life cycle. LCA and LCC

have been recognized as important methods for improving a building’s energy and cost

performance during the early design stages (Bribian et al. 2009). Indicators are used to describe

where along an impact pathway a category has relevance: midpoint indicators relate to the

environmental mechanism, whereas endpoint indicators pertain to human health and natural

resources (LC-Impact 2013). Midpoint indicators used in LCA include global warming potential,

acidification potential, and carcinogens. Quantifying many building designs’ environmental

impacts using these indicators can help designers understand how a building compares to other

designs in terms of life cycle environmental impact performance.

The application of LCA and LCC methods to buildings is challenging. Reliable and

organized data on building materials and components is difficult to obtain, especially in the

United States (Yohanis and Norton 2006, Wang et al. 2005b). Material flows during building

construction, including purchasing, site storage, materials control, and wastage control, are

difficult to track, and construction waste is difficult to estimate (Yohanis and Norton 2006).

Accounting for energy used in the transportation of building materials can be especially

complex, since the country of origin of building materials may vary as designers modify material

choices for such building components as cladding, glazing, and structural members. Precise

material specifications for building components are typically unknown at the conceptual design

stage, yet current methods of performing LCAs and LCCs of conceptual building designs require

8

a high level of detail of information (Morbitzer et al. 2001).

Bribian et al. (2009) describe additional challenges to conducting LCA of buildings. LCA

was initially developed for designing products with low environmental impacts. In comparison to

most products, however, buildings have a relatively long life span, they often undergo

programmatic changes, spaces may have multiple functions, they contain many different

materials and components, they are typically unique, and system boundaries may be unclear. As

a result, performing LCA of buildings is less straightforward and more complex than for many

products. Presentation of LCA results to building designers in an easily understandable format is

another challenge.

The integration method described in chapter 2 does not attempt to address all of the

challenges of integrating LCA with conceptual building design. For example, chapter 2 describes

how the method assumes certain system boundaries, a building life span, and a building program;

the research does not intend to generalize broadly across these variables. The LCA data used by

the method also originates from two life cycle inventory databases, SimaPro and Athena, each of

which has a certain degree of uncertainty (SimaPro 2010, Athena 2011); the research is limited

in scope to this data. The method addresses other problems, in particular the fact that current

LCA methods require a high level of detail when evaluating building designs and therefore are

more suitable at later building design stages; do not incorporate sensitivity analysis of building

design parameters; and do not leverage automated processes to compare an initial design with

many design alternatives.

9

Parametric design

Parametric design tools are well suited for integration with LCA and LCC feedback of

buildings at the early design stages. Parametric design is a method of linking variables to

building geometry in such a way that when one parameter’s value changes, values for the linked

parameters automatically update. By defining dependencies between such parameters as

geometry, orientation, façade composition, and building materials, designers can significantly

reduce manual design modifications and quickly receive many design alternatives. Parametric

design’s integration with performance-based tools such as structural optimization (Shea et al.

2005), energy simulation (Schlueter and Thesseling 2009), and life cycle assessment and life

cycle costing (Wang et al. 2005a, Wang et al. 2005b) has been shown to rapidly increase the

number of analysis feedback loops, thereby allowing designers to evaluate many design

alternatives for a range of variables. These analyses have optimized a building’s performance in

terms of reduced structural weight, operational energy, or life cycle cost, resulting in lighter,

more energy efficient, and cheaper buildings.

The work of Wang et al. (2005b) is described as an example of how parametric design

and rapid generation of building design alternatives can be used to improve building

performance. The study presented a multi-objective optimization model, in which a genetic

algorithm varied eight conceptual design parameters in order to optimize life cycle cost and life

cycle environmental impact. By iterating across many design alternatives, the algorithm

generated a Pareto front defined by 29 non-dominated solutions, many of which improved upon

both the initial set of designs’ LCC and life cycle environmental impact. In terms of variable

performance, steel-frame walls were the cheapest option, and masonry walls achieved the lowest

environmental impact performance. The optimal building orientation converged to zero degrees

10

and the window-to-wall ratio converged to 20%, meaning the highest-performing designs

consistently contained these variable values. On the other hand, the building’s aspect ratio did

not converge to a single value, meaning several different shapes could be found in high-

performing designs. This example shows that parametric tools may be integrated with

optimization algorithms, thereby increasing the number of high-performing design options. By

specifying ranges for values of parameters, then combinatorially selecting from these values,

optimization algorithms can potentially evaluate millions of building designs. For example, a

designer may wish to determine which combination of building orientation and cladding material

yields the lowest life cycle environmental impact and cost. As Wang et al. (2005b) showed, an

optimization algorithm can evaluate all possible orientation angles and cladding materials, then

determine which combination(s) result in low values for both objectives. The method iterates

through many design possibilities in order to consider the environmental impact and cost

tradeoffs.

The example also illustrates the use of multi-disciplinary design optimization (MDO),

which is a method of evaluating many parametrically generated design alternatives when a

tradeoff exists between competing objectives, such as life cycle environmental performance and

cost. Pareto, or non-dominated, optimization performs many feedback loops in order to identify a

field of conceptual designs that are equal-rank optimal. The solution space is thoroughly

explored, and the designer is provided with a number of solutions that represent the set that best

satisfy both objectives. Increasing the number of feedback loops in this way can also show

designers relationships between the competing objectives, ranges of parameter values that

produce viable solutions, and the sensitivities of each of the parameters. Studies have used

MDO to computationally evaluate the tradeoffs between building materials’ thermal versus

11

lighting performance (Caldas 2008), buildings’ first cost versus operational energy cost

(Diakaki et al. 2008), and facades’ heat gains versus cost (Peippo et al. 1999).

Genetic algorithms are well suited for solving building MDO problems and have been

applied to many MDO studies optimizing environmental impact and cost performance (Wang

et al. 2005a, Wang et al. 2005b, Wang et al. 2006, Magnier and Haghighat 2010, Juan et al.

2010, Charron and Athienitis 2006, Geyer 2009). A distinguishing feature from other

optimization methods is that genetic algorithms operate on a population of solutions, which are

randomly generated in order to yield the first solution. This feature lends itself well to MDO

problems, since genetic algorithms can locate multiple Pareto optimal solutions in a single run.

Genetic algorithms also lend themselves well to single criterion building optimization problems

(Wright and Farmani 2001). Each individual in the population, or a chromosome, represents a

potential solution in the problem space. The chromosome is typically represented as a binary

string that can capture both discrete and continuous variables. This is advantageous for building

energy performance optimization problems, in which variables are both discrete (e.g., building

materials) and continuous (e.g., overhang angle). Conventional gradient-based optimization

methods, on the other hand, depend on initial guess values and are prone to being trapped in local

extrema (Deb 2001). Building optimization problems are often nonlinear, which leads to

discontinuous outputs, and gradient-based methods can only be applied to smooth and

continuous functions (Wetter and Wright 2004). Because genetic algorithms use a non-

dominated strategy to sample the solution space at many different points, they are well suited for

solving building energy optimization problems with many local minima (Coley and Schukat

2002). Genetic algorithms also process large quantities of data efficiently and can identify

solutions quickly (Rafiq et al. 2003), which is beneficial for building optimization problems with

12

complex search spaces. Despite genetic algorithms’ potential to optimize conceptual building

designs, only a few studies have done so for minimized life cycle environmental impacts and life

cycle costs (Wang et al. 2005a, Wang et al. 2005b, Wang et al. 2006). Other life cycle impact

building design optimization studies required detailed building inputs, typically known only at

late design stages, and focused on operational energy (Al-Homoud 1997, Wetter 2001, Coley and

Schukat 2002). A method is needed for integrating conceptual building design and life cycle

impact tools with a minimal number of inputs, so that designers can utilize the tool as early as

possible in the design process.

Sampling algorithms can be integrated with parametric design tools to show designers the

full range of outputs possible for a given set of discrete input parameters. Probability mass

functions, or functions describing the probability that a variable will achieve each of its discrete

values, can be used to show the range of impacts possible for a given problem configuration. For

example, de Wit and Augenbroe (2002) generated a probability mass function to show the range

of thermal comfort options possible for a set of input parameters related to wind, temperature,

and solar transmission factors. In the example presented at the beginning of this section, Wang et

al. (2005b) discovered that one single value for building orientation and window-to-wall ratio is

found in all high-performing designs across all objectives, whereas certain values for wall type

perform well for one objective but not another, and values for other variables such as aspect ratio

exhibit no strong trend among high-performing designs. However the graphical results of the

study did not visually display these relationships very clearly. Probability mass functions can

clarify these relationships by showing quickly and easily which variable values are consistently

present in high-performing designs, which values are likely to be found in high-performing

designs for one objective but not other objectives, and which values exhibit no consistent

13

presence in high- or low-performing designs. Studies are lacking that show how automated

processes and probability mass functions can be leveraged in these ways to provide sensitivity

analysis on conceptual building design life cycle impact feedback.

Integration of multi-disciplinary design optimization and sequential

design decision-making processes

A final aspect of the problem designers face regarding life cycle environmental impact

and cost feedback is that MDO and design space sampling methods do not currently integrate

well with conceptual building design decision-making processes. Decisions are typically made

by architects in sequential fashion, such that for example once the orientation of the building is

known, the placement of shading devices can be determined for each façade in order to minimize

cooling loads. Designers may also wish to understand the life cycle environmental impacts and

costs associated with the wall assembly system before deciding upon the cladding system. Such a

multi-objective sequential feedback approach is typical in the architecture, engineering, and

construction industry in that project stakeholders often need to evaluate design decision trade-

offs for competing objectives. For example, a designer wishing to minimize both environmental

impact and cost may find that a certain window-to-wall ratio lowers carbon footprint at the

expense of greatly increased life cycle cost.

Existing MDO methods do not accommodate sequential decision-making processes.

MDO requires all design decisions to be made in parallel, instead of allowing designers to define

variable values sequentially and thereby understand the impacts for each successive decision.

Consequently designers utilizing MDO must decide on all building decisions before receiving

feedback on any single design choice. MDO methods do not integrate well with the AEC

14

industry, which relies on flexible and often-changing decision-making processes, especially

during the conceptual design phase.

Survey of current use of life cycle assessment in the building design

industry

In the last few decades, the architecture/engineering/construction (AEC) industry has

significantly evolved in terms of its use of computers in building design. Intelligent computer-

aided design (CAD) systems were first developed in the early 1970s as a way to rapidly create

forms and easily modify designs (Eastman 2008). The methods gained industry acceptance in the

1980s, as architects shifted from manual pencil and drafting board methods to computer-driven

form-based design methods (Terzidis 2006). Parametric design, or the defining of a CAD model

through the use of parameters, emerged in the 1980s with Lin et al.’s concept of variational

geometry (1981). The method allows designers to add geometrical constraints within computer

models, which automatically update as designers change parameters’ values (Chen et al. 2004).

Parametric design was taken to a powerful new level in the 1990s, when CAD modeling was

used to quickly generate many spatially novel, complicated forms. Variations in building designs

were generated through an algorithm, extending the role of the computer to both draftsperson

and performance analyst (Shea et al. 2005). This use of parametric design, also called generative

design, allowed for the creation of many design alternatives with complex curves and patterns

not easily visualized or manually drawn. Optimization algorithms programmed into generative

design programs quickly searched for solutions based on user-defined objectives.

Today, industry adoption of sustainable design techniques lags behind advancements in

design methods. Complicated designs are now easily possible but “often with little regard to the

15

cost in energy and material resources” (Taylor and Carper 2007, p. 152). The following two-part

glimpse into industry practice delineates the lack of sustainability-focused methods integrated

with design. The first part highlights the theoretical gap between LCA and LCC feedback and

building design by drawing from literature, certification programs, and software tools. The

second part highlights the practical gap between LCA and LCC feedback and building design by

reporting feedback from industry professionals. Results show that firms lack a quick and easy

method of receiving quantitative environmental impact and cost feedback on their designs at

early design stages. Such feedback would be of considerable use to industry in meeting the

challenge for life cycle impact feedback to maintain a similar technological pace as building

design methods.

Defining the theoretical gap between LCA and LCC feedback and building design

Building designers have been aware of sustainable design principles for decades, and

early design stages have been well established as critical elements in producing green buildings

(Gruman 2003). In early representative literature such as Climate Design (Watson and Labs

1983), 50 sustainable design strategies – such as site selection, microclimate control, and

massing – are outlined to architects and building energy consultants specifically for

consideration during early design stages. Drivers for incorporation of these strategies include

marketing benefits, environmental labeling of buildings, environmental targets for buildings and

nations, and subsidies for environmental impact reduction (Bribian et al. 2009).

The green building movement gained considerable traction with the inception of LEED,

or Leadership in Energy and Environmental Design, in 1998. LEED provides building owners

with a framework for implementing green building design strategies, and incentives for earning

16

LEED certification include growth in the local economy, tax breaks and abatements, and creation

of a healthier indoor and outdoor environment (USGBC 2011b). As of September 2011, 24,444

projects in the United States were LEED certified, and about 22 projects are certified daily

(USGBC 2011a). The meteoric rise of building energy simulation tools is another indication of

strong interest in sustainable design practices. Four hundred eleven tools are listed on the US

Department of Energy’s website (USDOE 2011b), and several are added every year.

Despite the heightened awareness of sustainable building design strategies in the past few

decades, additional efforts are needed to further engage the AEC industry with the sustainable

design movement. As one architect recently put it, “Architects’ increasing awareness of the

relevance and importance of developing a sustainable built environment has for the past few

decades been inversely proportional to architects’ direct efforts to create this environment” (de

Graaf 2011). Current CAD tools poorly integrate feedback with conceptual building design

(Drogemuller et al. 2004). Computational methods such as generative design are rarely used in

practice (Terzidis 2006). Performance feedback needs to be better integrated at early design

stages, when designers have the least understanding of, yet greatest opportunity to manipulate,

the building form. Initial forays during early design are critical to gaining a sense of a form’s

environmental and cost impacts yet remain problematic “for the novice and experienced architect

alike” (Fawcett 2003, p. 2).

Among the reasons for lack of adoption of sustainable methods are owners’ and

managers’ perceived increased financial risk of higher initial capital costs and perceived lack of

tenant demand (Wilson and Tagaza 2005). Designers are also reluctant to use energy simulation

tools because of the steep learning curve and extensive required data inputs (Jacobs and

Henderson 2002), poor interoperability with design tools and lengthy processing time (Krygiel

17

and Nies 2008), lack of known data on which to base energy simulations (O’Donnell et al.

2004), and difficulty in interpreting results (Schlueter and Thesseling 2009). Integration of

design and analysis is also seen as requiring extensive time and effort (Wilson and Tagaza 2005),

particularly during early design stages when designers lack full conceptual clarity. LCA

feedback integration with design is perceived as economically costly, complicated, and

inaccurate, techniques for improving building designs based on LCA feedback are unclear, and

LCA and LCC databases lack computational links with energy simulation programs, creating an

inefficient workflow (Bribian et al. 2009).

The literature points out that industry adoption of a method of incorporating LCA and

LCC feedback into early stage design would favor several features. The method should rapidly

evaluate many options, select solutions based on user preferences, and incorporate optimization

techniques for building energy consumption (Crosbie et al. 2010). The method should also

integrate, rather than separate, the roles of designers, engineers, and consultants. Complicated

analyses tend to foster fragmentation and compartmentalization among design-analysis teams

(Krygiel and Nies 2008). Fragmentation in turn causes a decline in efficiency – within both the

design and analysis processes as well as the buildings created (Krygiel and Nies 2008). A

method that fluidly integrates parametric design and environmental and cost analysis during the

early design stages needs to bridge this gap between efficiency and design of the sustainable

built environment.

Defining the practical gap between LCA and LCC feedback and building design

Feedback from AEC firms was solicited to validate these theoretical findings and help

understand the practical issues of implementing LCA and LCC feedback in building design. The

18

firms surveyed and interviewed perform design and environmental analysis to varying degrees.

The feedback consisted of two parts, and sustainability personnel at AEC firms were surveyed in

each part. In the first part, six firms completed an online survey so that general trends about LCA

feedback in the architecture industry could be understood. In the second part, three firms

conducted phone interviews so that in depth, firm-specific details on industry use of LCA in

design could be assessed. The objectives were twofold: to understand the degree to which

designers currently receive environmental impact and cost feedback on their designs and the

practical issues preventing industry adoption of an early stage LCA feedback parametric design

tool.

Part I – Understanding General Trends of LCA and LCC Feedback and Design

In Part I, AEC firms completed an online survey to gauge general trends in the use of LCA

and LCC feedback in design. Appendix 1 presents the survey questions, and the results are

presented here. Results showed consensus as far as firms’ methods of receiving environmental

impact feedback. In response to the first question asking how frequently firms use certain

strategies to reduce buildings’ environmental impact, all firms received some fort of feedback

but to different degrees. Figure 2 shows that firms perform LCA on about 25% of projects, which

is less often than other sustainable design strategies. Most firms only implemented strategies to

reduce operational energy.

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Figure 2 – Frequency that designers perform various sustainable design strategies on building

projects.

As far as LCA software used in industry, Athena was the only program listed. Many firms

did not list any programs, and these firms acknowledged that in the rare case when a client

desired LCA feedback, the work is out-sourced to building energy consulting firms not involved

in the building design. Firms also did not list any database sources for conducting LCA. The

conclusion is that firms infrequently conduct LCA, both due to lack of client demand and out-

sourcing.

Firms were also asked at what stage in the design process they make design decisions.

Figure 3 shows that some decisions are made early in the design process and in some cases

earlier than American Institute of Architects’ (AIA) guidelines. Although it is well understood

the importance of making design decisions as early in the design process as possible (Struck and

Hensen 2007), this may be problematic if firms do so without understanding the life cycle

impacts associated with these choices. Changes to such decisions at later design stages can

greatly increase project impacts (Grierson and Khajehpour 2002). For example, Figure 3 shows

that the firms tend to make decisions on materials for finishes, partitions, cladding, and the

foundation, the window-to-wall ratio (“façade design”), the building form, as well as whether a

20

building will have shading (“envelope elements”) prior to AIA guidelines. Figure 2 showed that

firms tend not to receive LCA feedback on projects. These two results taken together suggest two

possible consequences associated with decisions made prior to AIA guidelines: 1) the decisions

may not have low life cycle impacts, 2) changes to these decisions at later designs stages may

exponentially increase project impacts. It would be beneficial if firms continued to make

decisions prior to AIA guidelines but with the guidance of a LCA feedback tool. In this way,

designers can make informed decisions of life cycle impacts during the conceptual design stage

with the aim of creating low-cost, low-carbon buildings.

Figure 3 – Stages during which designers make various building decisions compared with

American Institute of Architects guidelines.

Figure 4 shows that firms cited several barriers as far as implementing LCA feedback on

projects, including complexity and inaccessibility of the software and lack of clarity of results.

This suggests that firms would appreciate a tool that is simple to use, requires minimum inputs,

and presents results in an easy to understand fashion.

21

Figure 4 – Barriers inhibiting design firms’ use of LCA on building projects.

The final question asked about the usefulness of an early design-LCA feedback tool. All

responses were either “agree” or “agree strongly”, suggesting the high potential of providing

such a tool to the design industry.

Part II – Understanding firm-specific details of LCA and LCC feedback and design

In Part II, North American design personnel involved with sustainability initiatives at three

global AEC firms were interviewed on the phone for approximately 60 minutes each. Each firm

uses LCA and LCC in their design process to varying degrees. Objectives were to ascertain

challenges when using LCA and LCC to improve building designs as well as gauge opinions on

a tool that provides LCA and LCC feedback to generate optimal designs.

22

Arup

Founded in 1946, Arup is one of the world’s largest AEC firms and is comprised of

designers, planners, engineers, and technical specialists. The firm is headquartered in London

and has over 10,000 staff in 37 countries spanning southern Africa, Pacifica, and the Middle

East. Arup has designed such iconic buildings as the Sydney Opera House, Centre Pompidou in

Paris, the Bird’s Nest Stadium in Beijing, and the platinum LEED-rated California Academy of

Sciences in San Francisco. The latter solidified Arup’s reputation in green building design, as the

firm invests heavily in sustainability research and analysis. LCA and LCC have played an

increasingly valuable role in their design services, which range from optimizing the passive

performance of a building envelope to analyzing operational data in order to improve future

buildings’ life cycle performance.

Francis Yang, a structures and sustainability specialist in Arup’s San Francisco office,

provided the following insights on Arup’s use of LCA during a phone interview in May 2011.

The primary challenge Arup’s San Francisco office has encountered when integrating LCA

feedback with design is finding an LCA feedback tool appropriate for early stage design. The

office uses Athena’s Impact Estimator for Buildings (IE), since no other tool provides indicators

based on the Tool for the Reduction and Assessment of Chemical and Other Environmental

Impacts (TRACI). Arup has encountered three problems with the tool. First, Athena IE has

greatest utility at later design stages. The IE requires inputs, such as structural information,

known by engineers typically only at late design stages. Second, Athena IE is not very appealing

to architects, since its focus is on structure and materials rather than form generation,

visualization, and aesthetics. Form-generation capabilities and footprint shape options are absent

beyond basic rectangular configurations. Athena IE’s use of assemblies, or pre-defined wall,

23

window, and building envelope systems, is less useful than a method that makes use of material

quantities. More useful would be a way for designers and cost estimators to input quantities then

layer in environmental impacts. There are only a few choices for structural systems, concrete

assemblies, and many materials. More helpful would be an easy way to build up the concrete and

steel mixes, including recycled content, fuel inputs, and manufacturing method. The broad range

of wood options is not useful, as Arup rarely works in wood. Seismic and wind conditions are

also not included in the IE. TRACI midpoint indicator results are hard to interpret for designers,

and a major question is whether they are accurate for the project location. A final limitation is

how to compare results. The IE compares results to a universal baseline, which is typically

dissimilar in size, form, and function from the actual building. More useful would be a

comparison that is similar in terms of size, shape, and type to the building project under analysis.

Arup also believes that industry potential exists for a method that integrates LCA and

LCC feedback into early stage parametric design using techniques that generate many design

alternatives. The primary concern is validation of results and transparency of the degree of

accuracy of the results. High-performing results need to be clearly rationalized, including why

certain design variables were considered and others were not. Designers need to clearly

understand design tradeoffs, the biggest contributors to the building’s impacts, and those design

changes that will improve the design the greatest. The tool must also easily allow changes to

constraints. This is especially necessary for example if the building forms for high-performing

results are not constructible.

24

Atelier Ten

Founded in 1990 by a team of engineers, Atelier Ten is a London-based environmental

design firm with a focus on applying technological innovation to the design of sustainable built

environments. The firm has six offices in North America, Europe, and the Middle East, and

personnel represent a mixture of architects and engineers. Projects have included the tallest

LEED Gold certified commercial building in the United States, the first use of earth-duct

technology in the UK, and the use of layered facades, high performance ventilation, and a

“thermal labyrinth” for cooling of spaces in a large arts and media building in Melbourne,

Australia.

Emma Marchant in Atelier Ten’s San Francisco office was interviewed in May 2011 and

provided the following insights on their use of LCA in design. The firm performs environmental

design primarily after the early design stages. Typically the building form has been designed, and

Atelier Ten then analyzes climate data for the given building type. Their focus is applying design

strategies to make the operational phase more efficient. In the rare case when the firm is involved

in the early design stages, they take into account clients’ goals and constraints, climate data, and

code considerations before creating a carbon zero roadmap. Strategies for this roadmap include

daylighting, passive cooling, renewable energy sources, and water and waste reduction. Early

design decisions include cladding and other façade materials and floor and ceiling types. Most

other materials decisions are made late in the design process.

LCA is rarely used in Atelier Ten’s projects. Clients are not interested in whole building

LCAs because of perceptions over cost and efficiency: designers and building owners believe

LCA is synonymous with high cost and a protracted design process. Architects would like to be

25

as green as possible, but they would like complete information in a cheap, efficient, and easy to

understand manner.

Atelier Ten also questions the potential for a tool with form-generation capabilities. A

method of automating form changes is rarely used in practice, because designers like to maintain

control over the building form. Visualization of a building’s form evolution throughout the

design process is critical to architects. A challenge would be to design a form-generation tool

that appeals to designers in terms of form control, especially if an innovative form’s cost is

greatly different from a simple form. However, it would be useful for designers to understand the

environmental impact and cost tradeoffs of one building form over another.

The firm is also very interested in receiving environmental impact and cost performance

feedback for many design alternatives. Such feedback at the early design stages may outweigh

loss in control of the form’s generation. The firm would also like a clear understanding of the

pros and cons of these design alternatives in terms of environmental impact and cost. For

example, it would be useful to understand the tradeoffs of materials, such as whether to use a

heavyweight or lightweight material for the structural frame, cladding, rainscreen, and

foundation. As far as material choices, Atelier Ten would find it helpful to visualize the

embodied energy, cost, and availability of each material.

Atelier Ten also believes an early stage design LCA and LCC feedback tool should above

all be clear in its results. The architect should be able to easily interpret the results and trust that

the option or options presented are the best. Architects should be able to assign preference

weights to these issues, and results should then be ranked accordingly.

26

Kohn Pedersen Fox

Kohn Pedersen Fox Associates (KPF) is a large architecture firm founded in New York in

1976. Offices today include London, Shanghai, Seoul, Hong Kong, and Abu Dhabi. The firm’s

expansion in the 1980s to Europe, where LCA has historically had a stronger foothold than in the

United States, helped foster a sustainable design approach. KPF’s in-house environmental

specialists use tools such as Ecotect and Green Building Suite to consider operational energy

design strategies. Important projects include the World Bank Headquarters in Washington, D.C.,

redevelopment of the large-scale Canary Wharf in London, the Shanghai World Financial

Center, and several LEED gold and platinum certified projects in North America and China.

Brad Zuger in KPF’s New York office was interviewed in May 2011 to provide insights

on the firm’s use of LCA in design. KPF’s New York sustainability team considers both

operational and embodied energy considerations in the design process. Team members are

typically trained as architects rather than engineers, and specialized environmental analyses such

as LCA are out-sourced in the rare case when a client calls for them. Economic cost is just as

important, if not more important, a design consideration as environmental impact.

KPF believes an optimized form-finding LCA and LCC feedback tool has potential for

architects during the early design stages. Architects are always looking for new tools and are

willing to give up some of the creative process to generative design. However, architects should

be able to easily manipulate the results to generate new building forms. Architects would not

want to invest heavily in learning the tool and are cautious about the manual burden of sorting

through many generative solutions. The tool should also avoid narrow consideration of

objectives. Instead, the tool should be multi-disciplinary by showing tradeoffs between

environmental impacts and economic costs. These tradeoffs should relate not just to building

27

form but also material choices. An iterative manual approach between design and analysis is

much less desirable than an automated approach. In that sense, the tool’s ability to automatically

generate many options and present a few best examples – coupled with the analytical data –

could be of high interest to architects. Visualization of the form and aesthetics are important to

architects, but they are just as mindful of achieving their performance objectives. The analysis

should be rigorous, complete, and easily understandable in order for it to be useful.

Summary of Key Themes

The three AEC firms interviewed all performed design and environmental analysis to

varying degrees. This was due in large part to whether personnel included engineers and

architects (Arup, Atelier Ten) or architects alone (KPF). In the rare case when the firms

conducted LCA, it was almost always used in the late design stages when the building form was

established and firm design choices had been made. Such retrospective analyses were typically

used for marketing a project as green or sustainable, even though the LCA played no part in

guiding design choices. LCA feedback used specifically at the conceptual design stage could

therefore fill this gap by guiding design choices that help create buildings with lower life cycle

carbon footprint. The firms all agreed on several features of a LCA feedback tool. Data should be

rigorous and show design tradeoffs, especially the life cycle environmental impact and cost

implications of material choices and building orientation. Display of a design’s cost was

mentioned as a valuable feature. In order to overcome the perception that LCA is costly and

inefficient, the tool should be easy to learn and not require specialized knowledge. Results

should be easily understandable, presented quickly, and represent a manageable number of best

solutions. The utility of form-generation capabilities received mixed opinions. However, the

28

firms agreed that presenting a few snapshots of the best designs coupled with the analytical

results could be very useful, as long as the building form could be easily manipulated for those

best designs.

Reflection on Use of Life Cycle Assessment in Building

Design

The previous section of this chapter described the observed problem: life cycle

assessment lacks integration with conceptual building design. Instead, LCA is applied – if at all –

in the late stages of the design process, when the influence on a building’s performance is low.

The main reasons for this lack of integration are summarized here in order to motivate the

research questions presented in the next section.

The primary reason why LCA lacks integration with conceptual building design is that

LCA typically requires many detailed inputs. This relates to the top-ranked priority in a 2009

survey of designers and architects on the usability of building performance simulation tools and

their effectiveness in integrating with the building design process: analyses should be quick and

efficient, and assumptions and default values should be allowed in place of detailed inputs (Attia

et al. 2009). This sentiment was also shared in the survey given to design firms and as described

in the previous section, in which many respondents cited complexity of LCA as a barrier to

integrating LCA with their design processes (see Figure 4). Designers have formalized very little

information about a building project during the conceptual design stage, and therefore it is

difficult at this stage to apply LCA tools that require large amounts of precise information. More

useful would be a method that provides rapid LCA feedback based on a scaled-down number of

inputs.

29

A second problem with LCA feedback is that tools do not provide sensitivity analysis on

building design parameters in relation to design decisions. This was ranked the second-highest

priority in the survey (Attia et al. 2009) and could explain why firms completing the industry

survey cited “irrelevance of LCA to firm’s work” as a barrier to performing LCA (see Figure 4).

Users have no clear way of understanding which design parameters contribute significantly and

consistently to a project’s life cycle impact and which parameters are consistently less important.

A mechanism is needed that provides this sensitivity analysis feedback so that designers can

focus their efforts on those decisions that affect a building’s impacts. A related problem is that

designers do not have a clear way of understanding the impact tradeoffs of certain design

decisions. For example, a designer may wish to understand the embodied versus operational

impact tradeoffs of glazing materials in order to minimize a building’s life cycle impact. Without

such an understanding, a designer may choose a material with a low embodied impact but with a

high operational impact or vice versa. A method that shows such impact tradeoffs for a range of

design parameters would allow designers to make decisions yielding low life cycle impacts.

A third problem related to LCA and building design is that a tool does not exist that

integrates automated feedback with building design. This was the top priority cited by the survey

respondents when asked about information management of a building performance simulation

tool’s interface (Attia et al. 2009). Although this problem does not relate directly to any point

cited in the industry survey described in the previous section, use of automation can potentially

increase the relevance of LCA in firms’ design work, one of the top barriers preventing firms

from performing LCA as shown in Figure 4. As the survey by Attia et al. (2009) pointed out,

designers would like to compare their initial design’s performance with the performance of

multiple design alternatives. Such feedback would be especially useful if provided sequentially,

30

or immediately after each design decision is made, as opposed to after all decisions have been

made. Automated processes can be leveraged that provide designers with the ability to compare

the performance of many building design alternatives. Related to this problem is the ability for

designers to understand the full range of impacts possible across many design alternatives. This

would allow designers to easily compare the performance of a building design to the best and

worst designs possible as well as show designers the degree to which each design decision

improves, worsens, or does not affect a building design’s performance.

Research Questions

Development of an automated method for providing life cycle environmental impact and

life cycle cost feedback during the conceptual building design is motivated by the following five

research questions:

1. How many design inputs are required for a method that incorporates life

cycle assessment and life cycle cost feedback into sequential building design?

2. Which design decisions contribute most significantly to building embodied impacts?

3. How well does a design strategy minimizing only operational impact compare

with a strategy minimizing both operational and embodied impacts?

4. How well can a method that leverages automated feedback be used to support

sequential building design decision-making processes?

5. What is the range of control for a set of building design parameters in terms of

life cycle environmental impact and life cycle cost performance, and how can designers

make decisions within this range?

31

Organization of the Dissertation

The research is organized into five chapters, which build on each other in terms of

developing the method for providing conceptual design phase life cycle impact feedback.

Chapter 2 lays groundwork for the first research question by developing equations for an

important part of the feedback method. These heuristics calculate pre-operational embodied

impacts using a minimal number of inputs, in order that the method may be applied as early in

the design process as possible. Chapter 3 develops the methodology further by using a sampling

method to apply the equations to a case study. This chapter answers the second research question

by highlighting the degree to which design decisions contribute to embodied impacts, and

operational impacts are left out of the scope. Chapter 4 includes operational impacts within the

scope of the research, and the third research question is answered by applying an optimization

algorithm to the same case study. The chapter presents an application of the method, which

allows designers to understand the relative importance of embodied versus operational impacts.

Chapter 5 continues to build the method by using a sampling algorithm to integrate automated

feedback into sequential design. The chapter answers the fourth and fifth research questions by

applying probability mass functions to a range of sequential design strategies. Designers are

enabled to understand the range of control they have over a building project’s impacts for a given

problem formulation. Chapter 6 extends the integration of sequential decisions and LCA

feedback to include LCC feedback. The chapter introduces a new set of cost heuristics, which,

similar to the embodied impact heuristics introduced in Chapter 3, allow designers to receive

feedback on many designs given very few inputs. Probability mass functions are then applied as

in Chapter 5 to show designers the full range of possible costs and environmental impacts after

each design decision. This multi-objective feedback approach allows designers to evaluate the

32

life cycle environmental impact and life cycle cost trade-offs of sequential design decisions.

Based on this feedback, designers can understand how well each decision aligns with their

particular building performance strategy.

Chapters 3 through 6 are four journal papers, and each has been submitted to peer-

reviewed journal publications. Chapter 3 presents a method for calculating embodied impacts

and performing sensitivity analysis of building components and has been published in Building

and Environment (Basbagill et al. 2013). Chapter 4 presents a method for describing the

embodied versus operational impact tradeoffs of building component decisions and has been

submitted to Energy and Buildings. Chapter 5 presents a method for integrating automated life

cycle environmental impact feedback into sequential building design decisions and has been

submitted to The International Journal of Architectural Computing. Chapter 6 integrates life

cycle environmental impact and life cycle cost objectives into sequential building design

decisions and has been submitted to Automation in Construction. Each paper stands as an

independent publication, which means the papers’ background sections overlap somewhat in

content. The papers have been largely untouched in the dissertation, and references have been

aggregated into a list at the end of the dissertation.

Chapter 7 is the conclusion of the research and summarizes the primary contributions of

the research developed in Chapters 2 through 6. The chapter also summarizes answers to the

research questions, which are also developed in Chapters 2 through 6. Chapter 7 also includes

sections on challenges for researchers who may wish to extend the work presented here,

limitations of the method, and possible future research avenues. Finally, a set of appendices are

provided which include supplementary data tables.

33

Chapter 2: Scope, model development, and validation

This chapter describes the development of the model for integrating conceptual building

design with life cycle environmental impact and life cycle cost feedback. The model is intended

for application specifically during the early design stages, when the design problem is typically

not well defined, the number of design alternatives is large, and the potential to reduce

environmental impacts and cost is greatest. The approach for building the model leverages

automated processes. A computational feedback processor is integrated with building

information modeling (BIM), life cycle assessment, and life cycle cost software tools. The

processor performs sensitivity analysis on building design parameters by quickly evaluating the

impacts of thousands of building design alternatives. The feedback processor applies an

optimization algorithm to a given problem formulation, which yields a set of improved designs.

Alternatively, the processor can apply a sampling algorithm to the design space and build a

profile of the full range of impacts. Such feedback can be used to show designers which building

components are consistently large contributors to a building’s impacts, and which are less

important.

As part of the development of the software integration method, two sets of heuristics

were developed for calculating pre-operational impacts. These heuristics require few inputs and

allow for quick calculation of pre-operational 1) embodied impacts and 2) costs for a range of

building components. Use of the heuristics within the BIM-enabled LCA-LCC feedback method

allows designers to understand the relative importance of building components’ life cycle

embodied impacts and costs for thousands of building design alternatives. The sections in this

chapter outline the steps required to create the BIM-enabled LCA-LCC feedback method and

34

describe the development of the pre-operational embodied impact heuristics. Chapter 6 provides

detail on the development of the pre-operational cost heuristics.

Scope of the method

The goal of the methodology is to use an automated approach to provide LCA and LCC

feedback to designers for many building designs at the conceptual design stage. Central to

creating the method is deciding on the scope for building life cycle phases, environmental impact

indicators, and building components. Several phases and components are included in the method

in an effort to maintain as broad a scope as possible.

Building life cycle phases

Figure 5 schematically shows the complete physical life cycle of a typical building. The

shaded area shows those phases that are included in the method. Embodied impacts prior to the

operational phase include raw material acquisition and material production, and embodied

impacts during the operational phase include the maintenance, repair, and replacement (MRR) of

building components. Utility impacts during the operational phase include impacts due to

HVAC, lighting, plug loads, and water use. Demolition and on-site construction have been

excluded, since impacts associated with these phases have been shown to be difficult to calculate

(Schoch et al. 2011) and small when compared with other phases (Scheuer et al. 2003). The

scope of the building phases included in the method is identical to the scope of the building

phases included in the validation of the method. See the section “Validation of embodied impact

heuristics” for details on the validation.

35

Figure 5 – Building life cycle phases included in the BIM-enabled LCA-LCC feedback method.

Environmental impact indicators

Researchers have identified several impact categories that are useful in measuring the

environmental impact of buildings. These impact categories include global warming potential,

non-renewable energy consumption, human toxicity, acidification, and eutrophication, among

others (Jolliet et al. 2003). Although the importance of all of these categories in comprehensively

assessing environmental impact is recognized, the proposed method is demonstrated for only

global warming potential. The metric used for this purpose is carbon dioxide equivalents (CO2e)

using the relevant 100-year global warming potential (Wright et al. 2011), which measures the

total amount of greenhouse gas emissions of the building, considering all relevant sources. The

building owner or designer could add other impact categories to the analysis as required.

Building components, materials, and dimensions

The framework used to determine the scope of the building components and materials is

based on Uniformat 2010. This classification system is used in the AEC industry to classify

building components within building element categories (Construction Specifications Institute

2010). Elements within the system refer broadly to the parts of a building. Uniformat elements

36

within the project scope are: Substructure (A), Shell (B), Interiors (C), and Services (D). The

remaining elements (Equipment and Furnishings (E), Special Construction and Demolition (F),

and Sitework (G)) are not considered, since these decisions relate to interior aesthetics, require

specialized knowledge of site conditions, or otherwise involve decisions that would be

impractical to make by designers before the design development stage.

Table 1 outlines the assemblies and their sub-components for each of the four Uniformat

elements. Material choices for each component are determined using choices for building

component assemblies available in RSMeans (RSMeans 2007) and Athena EcoCalculator

(Athena 2011). These choices are not meant to be exhaustive but rather representative of

common materials for each component. Appendix 2 enumerates the material choices and their

properties for each building component. These properties include material densities and

embodied CO2e factors, or the amount of carbon dioxide equivalents associated with materials’

feedstock energy, energy required to process the materials into building components, and fuel

cycle energy for all pre-operational processes.

The building component classification framework includes thickness as a dimensioning

variable. Specifications from several construction material and equipment supplier sources are

used to determine thickness ranges. These sources are listed in the description for Table 1. The

smallest minimum value and largest maximum value are identified across all sources for each

sub-component and placed into the table. Thickness ranges are not articulated for every

component, namely those whose size determinations are difficult to reduce to one single

thickness parameter and/or best quantified by structural analysis methods applicable to later

design stages.

37

Table 1 – Building component classification framework. Sources used for thickness ranges (by

Uniformat element): A: (RSMeans 2007), (ACI 2004); B: (RSMeans 2007); C: (RSMeans 2007).

bThickness

Uniformat element Assembly aSub-components Number of

material choices Minimum (m) Maximum (m)

cA: Substructure piles piles, vapor barrier, caps, slab-on-

grade, grade beam, rebar, formwork

2, 2, 1, 1, 1, 1, 1 0.1 0.4

footings footings, vapor barrier, slab-on-grade,

grade beam, rebar, formwork

1, 2, 1, 1, 1, 1 0.1 0.4

mat foundation foundation, vapor barrier 1, 2 0.2 1.8

B: Shell columns and beams 10 n/a n/a

floor structure 12 n/a n/a

roof roof structure, membrane, insulation,

paint

10, 5, 1, 1 n/a n/a

stairs stairs, railings 3, 3 n/a n/a

cladding 7 0.02 0.08

exterior walls wall structure, insulation, membrane,

gypsum, paint

5, 1, 1, 1, 1 n/a n/a

glazing glass, polyvinyl butyral, frame,

hardware

1, 1, 5, 1 0.007 0.02

doors door, hardware 3, 1 n/a n/a

C: Interiors partitions partition structure, gypsum, paint 2, 1, 1 0.2 0.6


wall finishes covering, paint 2, 1 0.005 0.02

flooring surface, insulation 9, 13 0.1 0.2

ceiling plaster, gypsum, paint 1, 1, 1 0.006 0.02

dD: Services mechanical 17 sub-components e13 n/a n/a

electrical 16 sub-components 1 n/a n/a

plumbing 23 sub-components 1 n/a n/a

fire 4 sub-components 1 n/a n/a

conveying elevator 1 n/a n/a aTotal number of sub-components listed in the table equals 107. Of these, 102 are distinct sub-components. Five are double counted and occur in multiple components:

vapor barrier, slab-on-grade, grade beam, rebar, and formwork. This double counting occurs because the substructure consists either of piles (seven sub-components),

or footings (six sub-components, five of which are present in the piles sub-components), or mat foundation (two sub-components, one of which is present in piles and

footings).

b Thickness ranges correspond to bold sub-component and all material choices for that sub-component. For assemblies with multiple bold sub-components, ranges

represent combined thicknesses.

c Substructure consists of one of the three listed assemblies. Remaining three elements consist of all listed assemblies.

d Large numbers of services sub-components preclude enumeration.

e Duct insulation is a mechanical sub-component with 13 material choices. Remaining mechanical sub-components have one material choice.

38

Software Integration Method

Figure 6 is a schematic showing the software and data dependencies in the automated

conceptual building design and LCA and LCC feedback method. The method is called the

Architectural Design And Performance Tool, or ADAPT. The arrows in the figure represent the

data dependencies. A broad overview of the method is presented here, whereas further details on

the specific software used are presented in the method section of Chapters 3, 4, 5, and 6.

Figure 6 – Architectural Design And Performance Tool (ADAPT): software integration tool

providing life cycle embodied impact, operational impact, and cost feedback on conceptual

building designs.

The process of receiving automated impact feedback on building design alternatives

begins with a designer manually creating a building information model (BIM). The BIM inputs

consist of the building’s size, type, and location as well as ranges for each of the variables. Table

2 lists these inputs in terms of constraints, variables, and assumptions. The constraints are

39

necessary for determining the maintenance, repair, and replacement schedule. For example, a

building of one type and size located in a hot and dry climate may have very different MRR

impacts than a building of another type and size located in a cold and wet climate. Appendix 2

lists the materials considered for each of the building components. Table 1 lists the ranges of

dimensions considered for each building component. A broad overview of all variables

considered is presented here. The specific variables considered for each of the studies performed

in Chapters 3, 4, 5, and 6 depends on the particular objectives of the study. Details for each

specific problem configuration are provided in the chapters.

Table 2 – Required inputs, variables, and assumptions for BIM-enabled automated feedback

method for life cycle assessment and life cycle cost.

Required Inputs

Location

Building type

Gross floor area

Variables

Number of buildings

Number of floors

Shape parameters

Window-to-wall ratio

Orientation

Building component materials

Presence of shading devices

Substructure type

Building materials

Building component sizes

Assumptions

Footing depth = 2m

Pile depth = 15m

Bay spacing = 9m

Floor-to-floor height = 4m

Wall assembly R-value = 16.81 K·m2/W

Roof R-value = 21.84 K·m2/W

Service life = 30 years

40

Embodied impact calculation

Once the designer has specified inputs in the BIM, the automated design-feedback

process begins. Calculation of pre-operational carbon footprint is the first step in this process.

The use of embodied impact heuristics is an essential part of this calculation. These heuristics

answer the first research question and are a theoretical contribution of the work and can be

applied to many types of LCA and LCC building analyses. These formulas depend on the BIM

inputs outlined in Table 2, and Appendix 2 provides all the formulas. The building phases

included in the scope of these formulas are shown in Figure 5 and are identical to the scope of

the building phases included in the validation of these formulas in the next section. The impact

of each building component material can be calculated from these formulas. Inputs include

material choices and average thickness values from Table 1 as well as gross floor area and all

required inputs, variables, and assumptions outlined in Table 2. Pre-operational carbon footprint

is calculated by multiplying each quantity by the embodied CO2e factors in Appendix 2 and

summing the resulting impacts from the 102 sub-components.

These heuristics can be contrasted with embodied impact formulas used in Athena

EcoCalculator, which have been used by several recent studies to calculate embodied impacts of

building components (Wang et al. 2005b, Crawford et al. 2010, Xiong and Zhao 2011, Pidgeon

2012). Athena requires ten inputs, including building location, building type, and gross area for

the foundation wall, slab, supported floors and roof, intermediate floors, exterior cladding,

windows, partitions, and roofs. Building size and type are also required in ADAPT, whereas the

eight area inputs are reduced to a single input, the gross floor area of the entire building. This

reduction in inputs was accomplished by developing heuristics in consultation with senior

estimators at Beck Technology, an AEC firm. Beck aggregated data from bill of material

41

quantities on approximately one dozen of their building projects. The number of formulas totals

102 and equals the number of distinct building sub-components outlined in Table 1.

Calculation of pre-operational embodied impacts due to the service equipment (e.g.,

HVAC equipment, plumbing equipment) is an important sub-step of this process. These impacts

comprise 61 of the 102 building component impacts and are calculated by sizing each piece of

service equipment according to peak building load. An energy simulation program performs this

step as follows (eQUEST 2010). Inputs from Table 2 are automatically passed from the BIM to

the energy simulation program. Thermal zones are defined in the resulting energy simulation

model as well as standard assumptions regarding building occupancy and HVAC system controls

(ASHRAE 2009). The energy simulation software then calculates peak building load from these

inputs and assumptions. The result becomes the input to the 61 material quantity formulas for the

service equipment. Equipment supplier documentation is used to determine whether each piece

of service equipment typically increases in size as peak building load increases. Material

quantities for those pieces of equipment that typically increase in size are scaled linearly

according to peak building load. The resulting scaled and non-scaled material quantities are then

multiplied by the CO2e impact factors in Appendix 2 to determine the service equipment’s pre-

operational embodied impact.

The remaining 41 embodied impacts include building components in the substructure,

shell, and interiors. These components include all parts of the building envelope as well as the

structural systems. Calculation of the material quantities of these components is more

straightforward than the calculation of the service equipment material quantities, as no scaling

factor is applied according to peak building load. The formulas are given in Appendix 2 and

include values for the required inputs, variables, and assumptions in Table 2. The material

42

quantities are then multiplied by a CO2e impact factor in Appendix 2 to determine the embodied

impact.

Operational impact calculation

An energy simulation model is used to calculate the annual energy consumption of the

building in terms of electricity (kWh) and natural gas (kBtu). The energy simulation model is

created based on the geometry and building material information contained in the BIM. Thermal

zones as well as standard assumptions regarding building occupancy and HVAC system controls

are defined in the BIM (ASHRAE 2009).

A maintenance, repair and replacement (MRR) schedule is used to determine the impacts

associated with maintaining service equipment during the operational phase of the building.

Impacts associated with the production of materials in this schedule are grouped with the

building’s embodied impact. The schedule is determined by the gross floor area, building type,

location, and structural and mechanical details defined in the building information model, which

are entered into an online facility operations reference database (CostLab 2011).

Operational carbon footprint calculations have two components. The first depends on the

building’s electricity and natural gas consumption as calculated by the energy simulation model.

These quantities are multiplied by a unit impact to calculate carbon footprint. The second

component is associated with the maintenance, repair, and replacement of the service equipment.

The MRR schedule is determined by inputting all the constraints as well as the service life

assumption from Table 2 into an online facility operations reference database (CostLab 2011).

The program returns each component’s MRR dollar costs for every year of the building’s

operation. Equipment supplier documentation is then used to look up a typical material, material

43

quantity, and cost for each component. Material quantities are then calculated by combining the

MRR cost outputs from the operations database with the data from the supplier documentation.

These quantities are multiplied by a CO2e impact factor in a similar fashion to the pre-

operational impact calculation to determine the MRR operational carbon footprint. The life cycle

embodied carbon footprint is then calculated by summing the pre-operational and MRR

operational CO2e impacts. Life cycle environmental impact is equal to the sum of the embodied

and operational impacts.

Feedback processor

A genetic algorithm is used to automatically iterate the carbon footprint analyses

described above across a defined range of design variables. The algorithm may be used for such

objectives as minimization of embodied energy, minimization of operational energy, or

minimization of total energy. A sampling algorithm is used to understand the full range of total

impacts possible for a given set of design variables. The algorithm generates a probability mass

function, which is useful for showing how designs generated by the genetic algorithm compare

to the full range of total impacts possible for the design problem under consideration.

Software used

Eight software components are used to implement the proposed method illustrated in

Figure 6. DProfiler is used for building information modeling (DProfiler 2012). SimaPro and the

Athena EcoCalculator are used for environmental impact data and for calculating the building’s

carbon footprint (SimaPro 2010, Athena 2011). RSMeans is used to calculate building life cycle

44

cost (RSMeans 2007). The energy simulation software eQUEST is used to calculate operational

energy (eQUEST 2010), and CostLab is used to estimate the service schedules (CostLab 2011).

Excel is used to calculate the carbon footprint metrics based on the data provided by the previous

components (Excel 2007). The optimization and sampling processes are implemented using

ModelCenter (ModelCenter 2008), a program that allows users to bring commercial software

tools into a common environment using software “wrappers” to facilitate the application of

automated design problem exploration techniques. The genetic algorithm chosen is the Darwin

algorithm, and the sampling algorithm chosen is an orthogonal array for 90% of the designs and

a Latin hypercube for 10% of the designs.

Validation of Embodied Impact Heuristics

Eight buildings were analyzed to validate the embodied impact heuristics developed in

ADAPT. Data from these buildings were obtained from the Arup Project Embodied Carbon

Database (Arup 2013). Various sources conducted highly detailed life cycle assessments of

embodied impacts requiring considerable effort and information on each of these buildings, and

the scope of these LCAs was identical to the scope of the embodied impact heuristics developed

for ADAPT (Figure 5). The methods for performing these LCAs were independent of the

methods performed by ADAPT. Table 3 provides information on each of these buildings,

including the source that conducted the LCA, building type, building size, and the building’s

construction completion date. Each of the eight LCAs calculated a single value for life cycle

embodied impact in terms of kg CO2e/m2. This value is provided in Table 3 under “mean life

cycle embodied impact”.

45

Table 3 – Comparison of embodied impact values for eight building case studies: ADAPT versus

Arup (2013).

Method of Calculating

Embodied Impacts

Building Type Building

Size (m2)

Construction

Completion

Date

# of

Building

Designs

Analyzed

aMean Life

Cycle

Embodied

Impact

(kg CO2e/m2)

Standard

Deviation

(kg CO2e/m2)

ADAPT Office 1,358 1989

968 1,994 1,036 bSuzuki and Oka 1998 1 1,100 n/a

ADAPT Religious 2,500

Summer

2013

920 1,494 668

Arup 1 1,229 n/a

ADAPT Office 2,900 (completed)

738 1,763 812

Arup 1 1,059 n/a


922 1,363 714 bSuzuki and Oka 1998 1 790 n/a


952 944 239 bSuzuki and Oka 1998 1 780 n/a

ADAPT Civic 34,910 (completed)

980 906 229

Arup 1 818 n/a

ADAPT Train Station 47,250

Summer

2015

995 873 215

Arup 1 1,089 n/a

ADAPT Office, Retail,

and Restaurant 85,000 2014

986 853 207

Arup 1 776 n/a aScope of all LCAs is identical to ADAPT and illustrated in Figure 5.

bStudy was conducted independent of Arup and included in the Arup database.

The embodied impact values for the eight detailed LCAs were compared with

distributions of impact values generated using ADAPT. Eight models were created in ADAPT,

and the only input was the gross floor area for each of the eight case studies. Ranges were

entered for the variables listed in Table 2. The number of buildings was held constant for each

case study and equaled one, except for the 2,900-m2 office building which consisted of five

buildings. The range of values for the shape parameters and number of floors was adjusted based

on the gross floor area, with the larger buildings having slightly greater minimum and maximum

possible values than the smaller buildings. Table 3 shows the mean embodied impact and

standard deviation for each of the eight distributions. Operational impact and cost were not

calculated as part of the embodied impact validation method.

46

Figures 7 through 14 compare the embodied impacts generated using ADAPT with the

embodied impact value obtained from the Arup database for each of the eight case studies. The

results are presented from smallest to largest building. The results show that ADAPT captures

reasonably well embodied impact values of highly detailed LCAs for small, medium, and large

buildings. All eight distributions captured the detailed LCA value, and this value was within one

standard deviation of the mean for seven out of the eight distributions. The train station case

study was the exception, and this embodied impact value was within 1% of the upper value of

the distribution’s standard deviation. Mean values of the distributions for large buildings were

generally much closer to the detailed LCA values than small buildings. Embodied impacts of

small building sizes measured using ADAPT were consistently greater than embodied impacts of

detailed LCAs. The standard deviations of the distributions are also lower for larger building

sizes, suggesting the heuristics are able to predict impacts more precisely as building size

increases. These observations are consistent with the fact that the underlying embodied heuristics

in ADAPT were developed from data on large building projects. ADAPT is also better suited for

larger projects, since such projects have a larger number of variables and variable ranges and a

larger design space size than smaller projects. However, the results suggest that the heuristics

may be applied to both small and large building projects.

47


versus ADAPT for a 1,358-m2 building.



48





49





50





51

Chapter 3: Application of life cycle assessment to early

stage building design for reduced embodied

environmental impacts1

Abstract

Decisions made during a building’s early design stages critically determine its environmental

impact. However, designers are faced with many decisions during these stages and typically lack

intuition on which decisions are most significant to a building’s impact. As a result, designers

often defer decisions to later stages of the design process. Life cycle assessment (LCA) can be

used to enable better early stage decision-making by providing feedback on the environmental

impacts of building information modeling (BIM) design choices. This paper presents a method

for applying LCA to early stage decision-making in order to inform designers of the relative

environmental impact importance of building component material and dimensioning choices.

Sensitivity analysis is used to generalize the method across a range of building shapes and design

parameters. An impact allocation scheme is developed that shows the distribution of embodied

impacts among building elements, and an impact reduction scheme shows which material and

thickness decisions achieve the greatest embodied impact reductions. A multi-building

residential development is used as a case study for introducing the proposed method to industry

practice. Results show that the method can assist in the building design process by highlighting

those early stage decisions that frequently achieve the most significant reductions in embodied

carbon footprint.

52

1This paper was co-authored with postdoctoral fellow Forest Flager, Assistant Professor Michael Lepech, and

Professor Martin Fischer in the journal Building and Environment. The citation is as follows: Basbagill J, Flager F,

Lepech M, and Fischer, M. (2013). Application of life cycle assessment to early stage building design for reduced

embodied environmental impacts. Building and Environment 60: 81-92.

Keywords: Life cycle assessment; Sustainable design; Embodied environmental impact;

Sensitivity analysis

Introduction

Buildings consume significant amounts of energy and materials. They account for 41% of

the total energy consumption in the United States (Fumo et al. 2010) and 38% of the nation’s

greenhouse gas emissions (USDOE 2011c). Buildings’ embodied energy, which includes

feedstock and process energy for production of building materials as well as the total fuel cycle

energy for all processes required to construct a building, may be particularly significant (Fay et

al. 2000, Bribian et al. 2009). In cases where buildings have been designed for low- or net-zero

energy, embodied environmental impacts can approach the magnitude of impacts due to

operational energy use (Citherlet 2001, Thormark 2002, Winther and Hestnes 1999).

A significant portion of a building’s life cycle impacts are determined by decisions made

in the early design stages (Cofaigh et al. 1999, Ellis et al. 2008, Kotaji et al. 2003). Choosing

materials with low embodied impacts at this stage therefore has potential to significantly reduce

a building’s life cycle impact (Lawson et al. 1995). However, evaluation of the environmental

performance of these decisions and strategies for generating alternatives that improve upon the

performance of designs are typically not performed until the design development stage

(Schlueter and Thesseling 2009). Life cycle Assessment (LCA), when applied to buildings, is a

method for predicting how a facility will perform over its lifetime, which includes raw material

extraction, manufacturing, construction, operation, maintenance, repair, replacement, and

53

demolition (ECDGEI 2007). LCA considers environmental and social impacts and is often

coupled with life cycle cost assessment methods that consider economic impacts (Norris 2001).

Commonly applied environmental indicators include global warming potential, carcinogenicity,

and resource consumption.

Life cycle assessment is commonly used in such industries as automotive design,

equipment manufacturing, and consumer product design (Spitzley et al. 2005, DeKleine et al.

2011, Keoleian et al. 2004). Compared to products produced in these industries, buildings are

unique, their lifetime is decades long, they have multiple functions, and they are locally

assembled. Adoption of LCA methods to architecture, engineering and construction (AEC)

projects has been limited due to these features. In addition, LCA methods typically require

significant time and effort for implementation. The difficulties in applying LCA to the AEC

industry have been noted by others, including obtaining complete environmental impact data for

building components, tracking material flows, and clearly defining system boundaries (Yohanis

and Norton 2006, Gluch and Baumann 2004, Lee et al. 2009). In addition, building information

modeling (BIM), which is increasingly used by AEC designers to digitally represent a facility

during the early design stages, currently lacks interoperability with LCA software (Bribian et al.

2009). An additional challenge of performing LCA during the early stages of a building project

is the complexity and large number of decisions that a designer faces. For example, the building

design process requires material and dimensioning specifications for hundreds of components.

Yet the design process is highly fragmented, with professionals working in an uncoordinated

fashion on such solutions as safety, health, serviceability, and aesthetics. Applying LCA to early

building design is therefore not straightforward, and material and dimensioning specifications are

typically deferred to engineering and construction teams in the design development stage (Kienzl

54

et al. 2003). Postponing or making changes to such decisions during this stage has been shown to

lead to significant increases in building impact (Schlueter and Thesseling 2009).

In order for LCA to be an effective early stage decision-making tool for the AEC

industry, designers must therefore be better enabled to understand which material and

dimensioning decisions most significantly determine a building’s environmental impact and

which decisions are less important. This knowledge can be part of an integrated, BIM-enabled

environmental impact feedback process, with designers focusing on decisions with large impact

during the early design stages and deferring decisions with marginal impact to later design

stages. This paper introduces a framework for providing designers with intuition on how

buildings’ embodied impacts are distributed throughout building elements. The framework is

intended for application specifically during the early design stages, when the design problem is

typically not well defined, the number of design alternatives is large, and the potential to reduce

environmental impacts is greatest. The framework utilizes a computational method that

integrates BIM software with LCA and energy analysis software, in order to quickly evaluate the

embodied impacts of thousands of building designs. Sensitivity analysis is then performed on

these results in order for designers to understand which building components’ embodied impacts

consistently contribute the largest to a building’s environmental impact across the designs.

Material choice and component dimensions, in the case of this paper a surface component’s

thickness, are selected as the two bases for demonstrating how designers can reduce a building’s

environmental impact. The framework is applied to a case study to show how impacts are

distributed throughout a building in the early design stages as well as which building component

decisions are the most important in terms of environmental impact. The framework requires a

minimal number of inputs and accommodates a range of values for massing parameters and other

55

design inputs. The inherent flexibility of the method and minimal required inputs therefore make

it useful for the early design stages.

Literature Review

Researchers and practitioners have recognized the importance of early design stages

when reducing buildings’ life cycle environmental impact. Numerous researchers have shown

that the earlier decisions are made in the design process and the fewer the changes to these

decisions at later stages, the greater is the potential for reducing the building’s environmental

impact. For example, by selecting an environmentally preferred building shape and orientation

during the early design stages, Cofaigh et al. (1999) were able to reduce a baseline design’s

environmental impact by 40%. Massing changes made during late design stages were shown by

Ellis et al. (2008) to have considerable environmental and economic cost ramifications.

Providing designers with early stage environmental impact performance feedback was

demonstrated by Schlueter and Thesseling (2009) to have strong effects on design choices,

resulting in less energy intensive buildings and increasing awareness of ways to reduce energy

consumption.

Building on this early stage design work, others have integrated BIM software with LCA

methodology and optimization techniques in order to minimize buildings’ environmental impacts

during early stage design. Wang et al. (2005b) computationally integrated BIM, LCA, energy

analysis, and optimization software in order to evaluate the environmental impact consequences

of various early stage building design parameters. A multi-objective genetic algorithm was used

to identify Pareto optimal solutions for minimized cost and environmental impact performance,

resulting in a significant reduction in global warming potential. Similarly, Hauglustaine and Azar

56

(2001) developed a computational integration method for providing BIM energy performance

feedback prior to the sketch design phase. A limited number of design variables were included,

such as those relating to a building’s geometry and thermal performance. A genetic algorithm

was used to optimize cost and energy performance, and sensitivity analysis was performed to

show the relationship between performance characteristics and changes to the design variables.

Coley and Schukat (2002) integrated BIM and thermal analysis software in developing a method

for optimizing early-stage building designs for energy performance. The method gives designers

the flexibility to choose from a set of high-quality designs based on non-optimized criteria.

LCA has also been used to estimate impacts of buildings at the early design stages.

Pushkar et al. (2005) used LCA methodology to group design variables into four clusters then

show each variable’s environmental impact bounds for each phase in a building’s life cycle.

Common building material and dimensioning alternatives were considered. Sensitivity analysis

was conducted using different fuel sources and production methods, in order to show the range

of material quantity impacts for each life cycle stage. Bribian et al. (2011) also applied LCA to

early stage building design using common building component materials and sizes, in order to

provide material selection guidelines based on minimized embodied impacts.

A number of software tools have also been developed for using LCA to assess buildings’

environmental impact at the early design stages. For example, the Athena EcoCalculator

provides environmental impact estimates of buildings based on minimal inputs (Athena 2011).

However, these tools provide no sensitivity analysis showing how building components’

environmental impacts vary over a range of design alternatives. Lack of integration with BIM

tools also reduces their utility during the early design stages.

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Prior research has also neglected to conduct sensitivity analysis on the embodied impacts

of building component materials and dimensions for a range of design alternatives. An early

stage decision support method is lacking that shows the degree to which design choices achieve

embodied impact reductions for thousands of design variations. Massing parameters, such as

building shape and height, typically are not varied in environmental impact analyses of

buildings. Prior studies also do not include impacts due to mechanical, electrical, and plumbing

(MEP) or other service equipment or the maintenance, repair and replacement (MRR) of building

components. These limitations are addressed by the proposed method.

Methodology

Scope

The goal of the proposed methodology is to enable designers to understand the relative

environmental impact implications of building component decisions. The choices of building

component material and building component dimensions (e.g., thickness) have been shown to be

important in terms of contributing to a building’s life cycle environmental impact (Venkatarama

and Jagadish 2003, Gustavsson and Sathre 2006, Comakli and Yuksel 2004). In addition,

material and thickness choices extend to many building components, such as the foundation,

cladding, walls, floors, and duct insulation. Broadness of scope and degree of impact therefore

drive the selection of material and thickness as the two types of decisions used to determine

which building components contribute most significantly to a building’s embodied impact.

A second goal of the methodology is to create an automated or semi-automated process

that provides environmental impact feedback on many building designs. Central to the method is

the integration of BIM software with LCA, energy simulation, and sensitivity analysis software.

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Designers manually input a range of values into the BIM for a limited number of design

variables. The BIM also requires input values for a limited number of constraints. The method

then computationally iterates through all possible design variable values, thereby creating a large

set of building designs each with an environmental impact. Data is then aggregated and analyzed

in order to determine which building components consistently contribute the most to a building’s

embodied impact. The method is not a full integration, since LCA and energy analysis data are

manually extracted from various programs prior to the iterations. Further detail on the degree of

automation used in the process is presented later in the paper.

The Uniformat 2010 classification system is used in the AEC industry to classify building

components within building element categories (Construction Specifications Institute 2010).

These elements refer broadly to the parts of a building. Uniformat elements within the project

scope are: Substructure (A), Shell (B), Interiors (C), and Services (D). The remaining elements

(Equipment and Furnishings (E), Special Construction and Demolition (F), and Sitework (G)) are

not considered, since these decisions relate to interior aesthetics, require specialized knowledge

of site conditions, or otherwise involve decisions that would be impractical to make by designers

before the design development stage. A detailed description of this classification framework is

presented in the next section.

Figure 15 schematically shows the complete life cycle of a typical building. The shaded

area shows those phases that are included in this method. Operational phase impacts due to

HVAC, lighting, plug loads, and water use have been excluded in the scope. Rather, the research

focuses on embodied impacts due to building component material and dimension choices. The

operational phase is limited to embodied impacts due to maintenance, repair, and replacement

(MRR) of building components. As such, decisions determining a building’s impact due to

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operational energy use from utilities are assumed to be independent from decisions determining

embodied impacts. The intention of the study is to make as granular as possible those decisions

determining building embodied impacts. Including the effects of building materials and

thicknesses on operational energy would dilute this feedback. Therefore, operational energy use

beyond MRR is excluded from the study. Coupling of these inter-life cycle-phase design

decisions is a topic of future planned research. Demolition and on-site construction have also

been excluded, since impacts associated with these phases have been shown to be difficult to

calculate (Schoch et al. 2011) and small when compared with other phases (Scheuer et al. 2003).


environmental impact of buildings. These impact categories include global warming potential,

non-renewable energy consumption, human toxicity, acidification, and eutrophication, among

others (Jolliet et al. 2003). Although the authors recognize the importance of all of these

categories in comprehensively assessing environmental impact, this methodology considers only

global warming potential. The metric used for this purpose is carbon dioxide equivalents (CO2e)

using the relevant 100-year global warming potential, which measures the total amount of

greenhouse gas emissions of the building, considering all relevant sources (Wright et al. 2011).

The building owner or designer could add other impact categories to the analysis as required.

Figure 15 – Building life cycle phases included in scope.

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Building component classification framework

The framework used to structure the building component decision-making process is

based on Uniformat 2010. Table 4 outlines the assemblies and their sub-components for each of

the four Uniformat elements. Material choices for each component are determined using

RSMeans (RSMeans 2007) and Athena EcoCalculator (Athena 2011). These choices are not

meant to be exhaustive but rather representative of common materials for each component. The

appendices enumerate the material choices and their properties for each building component.

These properties include material densities and embodied CO2e factors, or the amount of carbon

dioxide equivalents associated with materials’ feedstock energy, energy required to process the

materials into building components, and fuel cycle energy for all pre-operational processes. The

“Software Integration” sub-section of the “Implementation” section describes the software from

which these factors are obtained and why these programs are chosen.

The building component classification framework includes thickness as a dimensioning

variable. Specifications from several construction material and equipment supplier sources are

used to determine thickness ranges. These sources are listed in the description for Table 4. The

smallest minimum value and largest maximum value are identified across all sources for each

sub-component then placed into the table. Thickness ranges are not articulated for every

component, namely those whose size determinations are difficult to reduce to one single

thickness parameter and/or best quantified by structural analysis methods applicable to later

design stages.

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Table 4 – Building component classification framework. Sources used for thickness ranges (by

Uniformat element): A: (RSMeans 2007), (ACI 2004); B: (RSMeans 2007); C: (RSMeans 2007).

bThickness

Uniformat element Assembly aSub-components Number of

material choices Minimum (m) Maximum (m)

cA: Substructure piles piles, vapor barrier, caps, slab-on-

grade, grade beam, rebar, formwork

2, 2, 1, 1, 1, 1, 1 0.1 0.4

footings footings, vapor barrier, slab-on-grade,

grade beam, rebar, formwork

1, 2, 1, 1, 1, 1 0.1 0.4

mat foundation foundation, vapor barrier 1, 2 0.2 1.8

B: Shell columns and beams 10 n/a n/a

floor structure 12 n/a n/a

roof roof structure, membrane, insulation,

paint

10, 5, 1, 1 n/a n/a

stairs stairs, railings 3, 3 n/a n/a

cladding 7 0.02 0.08

exterior walls wall structure, insulation, membrane,

gypsum, paint

5, 1, 1, 1, 1 n/a n/a

glazing glass, polyvinyl butyral, frame,

hardware

1, 1, 5, 1 0.007 0.02


C: Interiors partitions partition structure, gypsum, paint 2, 1, 1 0.2 0.6


wall finishes covering, paint 2, 1 0.005 0.02

flooring surface, insulation 9, 13 0.1 0.2

ceiling plaster, gypsum, paint 1, 1, 1 0.006 0.02

dD: Services mechanical 17 sub-components e13 n/a n/a

electrical 16 sub-components 1 n/a n/a

plumbing 23 sub-components 1 n/a n/a

fire 4 sub-components 1 n/a n/a

conveying elevator 1 n/a n/a aTotal number of sub-components listed in the table equals 107. Of these, 102 are distinct sub-components. Five are double counted and occur in multiple components:

vapor barrier, slab-on-grade, grade beam, rebar, and formwork. This double counting occurs because the substructure consists either of piles (seven sub-components),

or footings (six sub-components, five of which are present in the piles sub-components), or mat foundation (two sub-components, one of which is present in piles and

footings).

b Thickness ranges correspond to bold sub-component and all material choices for that sub-component. For assemblies with multiple bold sub-components, ranges

represent combined thicknesses.

c Substructure consists of one of the three listed assemblies. Remaining three elements consist of all listed assemblies.

d Large numbers of services sub-components preclude enumeration.

e Duct insulation is a mechanical sub-component with 13 material choices. Remaining mechanical sub-components have one material choice.

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Analysis process

The general steps of the proposed integrated, BIM-enabled embodied impact feedback

method are shown in Figure 16. The arrows in the figure represent data dependencies between

process steps.

Figure 16 – Software integration for embodied impact feedback method.

The process begins with a designer manually creating a building information model

(DProfiler 2012). Table 5 lists the BIM inputs in terms of constraints, variables, and

assumptions. The constraints are necessary for determining the maintenance, repair, and

replacement (MRR) schedule. For example, a building of one type and size located in a hot and

dry climate may have very different MRR impacts than a building of another type and size

located in a cold and wet climate. Minimum and maximum values are required for the variable

inputs, and no material or size specifications are required. Assumptions are automatically

programmed into the BIM but may be modified by the designer.

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Table 5 – Required inputs, variables, and assumptions for building information modeling-

enabled embodied impact feedback method.

Required Inputs

Location

Building type

Gross floor area

Variables

Number of buildings

Number of floors

Length and width parameters determining

building footprint

Window-to-wall ratio (WWR)

Assumptions

Footing depth

Bay spacing

Floor-to-floor height

Service life

Once the BIM is created, the automated design-feedback process begins. Calculation of

pre-operational carbon footprint is the first step in this process. The use of material quantity

formulas is an essential part of this calculation. These formulas depend on the BIM inputs

outlined in Table 5, and many were developed in consultation with senior estimators at Beck

Technology, an AEC firm. Beck aggregated data from bill of material quantities on

approximately one dozen of their building projects. The number of formulas totals 102 and

equals the number of distinct building sub-components outlined in Table 4. Appendix 2 provides

all formulas for each of the four building elements. The formulas are used to calculate the

minimum and maximum possible quantities for each building component material. Inputs are

material choice, minimum thickness, and maximum thickness as given in Table 4 as well as

gross floor area and all variables and assumptions outlined in Table 5. Pre-operational carbon

footprint is calculated by multiplying each quantity by the embodied CO2e factors in the

Appendix 2 and summing the resulting impacts from the 102 sub-components.

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Calculation of pre-operational embodied impacts due to the service equipment is an

important sub-step of this process. These impacts comprise 61 of the 102 building component

impacts and are calculated by sizing each piece of service equipment according to peak building

load. An energy simulation program performs this step as follows (eQUEST 2010). Inputs from

Table 5 are automatically passed from the BIM to the energy simulation program. Thermal zones

are defined in the resulting energy simulation model as well as standard assumptions regarding

building occupancy and HVAC system controls (ASHRAE 2009). The program then calculates

peak building load from these inputs and assumptions, and the result is an input to the 61

material quantity formulas. Equipment supplier documentation is used to determine whether each

piece of service equipment typically increases in size as peak building load increases. Material

quantities for those pieces of equipment that typically increase in size are scaled linearly

according to peak building load. The resulting scaled and non-scaled material quantities are then

multiplied by the CO2e impact factors in Appendix 2 to determine the service equipment’s pre-

operational embodied impact.

A maintenance, repair and replacement schedule is used to determine the operational

phase impacts associated with the building components. The MRR schedule is determined by

manually entering all the constraints as well as the service life assumption from Table 5 into an

online facility operations reference database (CostLab 2011). The program returns each

component’s MRR dollar costs for every year of the building’s operation. Equipment supplier

documentation is then used to look up a typical material, material quantity, and cost for each

component. Material quantities are then calculated by combining the MRR cost outputs from the

operations database with the data from the supplier documentation. Material quantities are then

scaled according to peak building load as described in the previous paragraph. These quantities

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are multiplied by a CO2e impact factor in a similar fashion to the pre-operational impact

calculation to determine the minimum and maximum MRR operational carbon footprint. The life

cycle embodied carbon footprint is then calculated by summing the pre-operational and MRR

operational CO2e impacts.

Sensitivity analysis software is then used to search the design space for the minimum and

maximum possible embodied impacts due to each building component by varying the input

variables from Table 5 using the pre-defined ranges (ModelCenter 2008). The entire space is

searched and the number of designs generated equals the product of the number of choices for

each variable in Table 5. By generating a large number of designs, the method therefore shows

how each building component’s embodied impact varies across a wide range of input parameters.

The results present designers with an impact allocation scheme, which shows the

minimum and maximum embodied impacts possible for each of the building components across

all designs considered. The maximum possible embodied impact for a given design is first

determined by selecting the material and thickness with the largest impact. A building

component’s minimum impact is then determined by selecting the material and thickness with

the smallest impact for that component. The material and size with the largest impact are chosen

for all other components. Maximum impacts are determined in a similar fashion. Minimum and

maximum impacts are expressed as a percentage of a given design’s maximum possible

embodied impact.

Designers are also presented with an impact reduction scheme, which shows the degree to

which each building component achieves reductions in embodied impact due to changes in both

material and thickness. The maximum embodied impact reduction due to a change in material is

calculated by subtracting the smallest possible impact from the largest possible impact. The

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maximum embodied impact reduction due to a change in thickness is calculated in a similar

fashion. The reductions are expressed as a percentage of the entire building’s maximum possible

embodied impact for a given design.

The material and thickness impact reductions for each building component are calculated

for all designs. The lowest and highest of these maximum values represent the range of

maximum impact reductions possible across all designs for each building component. Each

design’s building component impact reductions are then summed, and the designs with the

minimum and maximum sums represent the lowest and highest maximum impact reductions for

the whole building. Histograms showing the distribution of maximum impact reductions for the

building are then generated. One histogram is created for material decisions. Another histogram

is created for thickness decisions. Distributions for building component material and thickness

impact reductions are overlayed showing the degree to which each decision reduces a building’s

mean embodied impact. These distributions are generated in order of those that reduce the

histogram’s mean embodied impact the most to those that reduce the mean the least.

Implementation

Problem formulation

A mid-rise multi-building residential development is used as a case study to demonstrate

the utility of the proposed method to industry practice. The development plan was provided by

Beck Technology as a retrospective case study. At the time of publication, the complex was in

the early design stage. The case study thus provided an opportunity to show which decisions

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could reduce the embodied environmental impact of the development the most in terms of

building component material and thickness changes.

The problem formulation is described in Table 6 in terms of required inputs, variables,

and assumptions. Values for the required inputs and assumptions are given as well as choices for

the variable inputs. The total number of possible designs is 5,832.

The development has a total floor area of 50,000 m2. The variable “Number of

buildings” refers to the choices for the number of individual buildings in the housing

development. For a given design, each building is identical in terms of values selected for the

variables as well as the material and thickness selected for each building component. Six shape

parameters determine the form of the building. Peak building load is used to size the service

equipment and determine the MRR impacts for every possible combination of number of floors

and number of buildings, using the process described in the previous section. Embodied carbon

footprint ranges and reductions due to building component material and thickness changes are

calculated in terms of CO2e as described in the previous section. Internal loads and the weekly

operating schedule are determined from the 2009 ASHRAE Fundamentals (ASHRAE 2009).

Since orientation is constrained in this study to a single value, orientation is not included as a

variable in the problem formulation and therefore does not influence changes in embodied

impact.

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Table 6 – Problem formulation showing required inputs, variable values, and assumptions.

Required Inputs

Location = confidential

Building type = residential mid-rise

Gross floor area = 50,000 m2

Variables

Number of buildings = 3, 4

Number of floors = 5, 6, 7, 8

Shape parameters:

a = 0m, 15m, 30m

b = 10m, 20m, 30m

c = 0m, 15m, 30m

d = 10m, 30m, 50m

e = 0m, 15m, 30m (a)

f

Window-to-wall ratio = 0.15, 0.325, 0.50

Assumptions

Footing depth = 2m

Bay spacing = 9m

Floor to floor height = 3.6m

Service life = 30 years (a)

Shape parameter “f” is dependent on the other five

shape parameters. Minimum possible value for “f” is 15,

and maximum possible value is 30.

Software integration

The various software components used to implement the BIM-enabled embodied impact

feedback method are shown in Figure 16. Descriptions of the software, pros, cons, and

alternatives are discussed in the following section.

DProfiler is used as the selected BIM software (DProfiler 2012). The program is a

conceptual level building modeler and is useful for early design stages. The program provides

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detailed material quantity and energy analysis feedback given minimal building design inputs.

Heuristics programmed into the software compute building component material quantities. The

program outputs a detailed BIM with fewer inputs than is typically required by alternative BIM

programs such as Revit (Revit 2013). Another advantage of DProfiler is that the BIM created by

the program is automatically exported to eQUEST, an energy simulation program (eQUEST

2010). This export occurs entirely within DProfiler without any user interface with eQUEST.

Linkage of the BIM and energy model configuration is therefore useful for the method developed

in this paper since users receive energy analysis feedback on BIM inputs without having to

separately create an energy simulation model.

A number of limitations exist with the computational architecture shown in Figure 16.

DProfiler has limitations in terms of the range of geometric forms that it can create. DProfiler’s

building wizard tool is used to implement the method in Figure 16, and BIM geometries are

limited to fairly simple orthogonal building shapes such as the H-shaped figure shown in Table

6. Complex or freeform architectural shapes are currently not accommodated by the software.

Limitations of eQUEST include long run times, meaning it may take several days to execute the

method shown in Figure 16 for thousands of runs. The program also does not model natural

ventilation, operable windows, or thermal comfort. However, the proposed method includes only

MRR operational impacts and so is not affected by these limitations.

SimaPro is the LCA software used to obtain many of the CO2e impacts outlined in

Appendix 2 (SimaPro 2010). As described in the analysis process section, these factors are

critical for converting building component material quantities into embodied impacts. SimaPro is

chosen because the software contains impact factors for many different building materials, and

these impacts correspond to the life cycle phases scoped in the method and outlined in Figure 15.

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SimaPro is provided the quantity of building component material as an input and returns kg

CO2e per kg of material. A limitation of the program is that the software does not

computationally integrate into the method proposed in Figure 16. Instead, the CO2e impact

factors must be manually extracted and placed into Excel.

Athena EcoCalculator is also used to obtain some of the CO2e impact factors in Appendix

2 (Athena 2011). The program is helpful for estimating impacts of structural assemblies, such as

columns and beams, interior walls, or the roof structure. The tool scales impacts by building

gross floor area rather than a building component thickness parameter. This scaling method is

especially useful during the early stages of building design when very limited information is

known about a building such as structural requirements. The tool integrates well with the

proposed method, since gross floor area is a required input in Table 5. As with SimaPro,

however, the program does not computationally integrate into the proposed method and CO2e

impact factors are manually extracted and placed into Excel.

CostLab is the online facility operations reference database used to calculate MRR

impacts (CostLab 2011). The software creates an MRR schedule comprised of a detailed list of

building components within its database. LCA tools do not provide such detailed MRR

information. CostLab works well with the proposed method since the program takes as inputs the

constraints outlined in Table 5. Since the program is not an LCA tool, impacts are outputted in

terms of dollar costs instead of CO2e for every component for every year of a building’s

operation. Therefore, the program is cumbersome to work with since dollar costs must be

converted to embodied impacts as described in the analysis process section. In addition, since the

program does not computationally integrate into the proposed method, BIM inputs are manually

entered and cost outputs are manually extracted and placed into Excel.

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Excel is used to perform simple mathematical operations on the material quantities that

determine the embodied carbon footprint of each building design (Excel 2007). Data is manually

extracted from the various software sources as described above and Excel formulas are used to

perform the operations for each iteration.

ModelCenter is the sensitivity analysis software used to integrate DProfiler and Excel

into a common environment (ModelCenter 2008). The program’s ScriptWrapper utility enables

users to integrate or “wrap” different software components using a scripting language. In this

work, DProfiler is wrapped with ModelCenter to allow the programs to communicate with each

other in terms of inputs and outputs. Phoenix Integration, Inc. has wrapped Excel with

ModelCenter, thereby allowing DProfiler and Excel to communicate with each other. The

program’s “Design Explorer” tool allows sensitivity analysis to be performed on a large design

space by computationally iterating through all possible designs. Ranges for design variables are

articulated either as a continuous range or as a discrete set. Table 6 lists the discrete set of

variable choices used for the case study. The program iterated through all 5,832 designs in order

to determine the minimum and maximum embodied impacts for each building component.

Choosing every single integer value for each variable in Table 6 between the minimum and

maximum given values would require significant memory and several weeks of computer

processing time. Due to these constraints, only the lower bound, upper bound, and mid-point

value of each variable range were chosen as shown in the table. Limiting the selection of

parameter values in this way does not affect results, since sensitivity analysis is concerned with

determining only minimum and maximum possible embodied impacts.

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Results and Discussion

The proposed method was applied to the case study project to determine in which

building components embodied impacts are concentrated as well as which design decisions

achieve the greatest reductions in embodied impact.

Table 7 presents the impact allocation and impact reduction schemes described in the

analysis process section. Tables 8 and 9 regenerate the impact reductions after each material and

thickness decision, respectively, are made. The results show how the range of embodied impacts

is steadily reduced as decisions are made in order from those achieving the greatest embodied

impact reductions to those achieving the least reductions. The results are not meant to suggest

that making decisions in a certain sequence – from those achieving the greatest impact reduction

to those achieving the least – can help designers arrive at a best or improved design in terms of

lowered embodied impact. Rather, the results are meant to help designers visualize the potential

reductions for each building component so that they can understand which decisions consistently

contribute to a building’s embodied impact then focus on making choices for those decisions that

matter the most.

The impact allocation scheme shows that the range of impacts is very large for building

components in all four of the elements. The total embodied impact can potentially be

concentrated in any of these elements, as long as materials and thicknesses with minimum

embodied impact are chosen for components in each of the other elements. Each element may

contribute over 50% of the development’s embodied impact, depending on the design under

consideration.

In terms of the impact reduction scheme shown in Table 7, both material and thickness

changes can potentially lower embodied impacts by large amounts for many building

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components in the substructure (Uniformat Element A), the shell (Element B), and the interiors

(Element C). Fourteen of the 21 components’ maximum impacts are greater than 10% of the total

embodied impact, and six are greater than 40%. The largest impact changes are seen in cladding

material (reduced from 35% to approximately 7% of the maximum possible total embodied

impact), cladding thickness, piles material, glazing material, and flooring material. Designers

should be aware of the material and thickness choices for these building components during the

early design stages. In contrast, changes to materials and thicknesses are not important for all of

the services components within Uniformat Element D, suggesting designers need not focus on

these decisions during the early design stages.

Tables 8 and 9 regenerate the impact reductions after each material and thickness

decision, respectively, is made. The results show how the range of embodied impacts is steadily

reduced as decisions are made in order from those achieving the greatest embodied impact

reductions to those achieving the least reductions. The tables confirm the results from Table 7.

Tables 8 and 9 show that large reductions can be achieved for both material and thickness

decisions for building components in Uniformat Elements A, B, and C. In terms of material

choice, the greatest reductions are achieved for cladding, substructure, partitions, and flooring

surface. In terms of thickness choice, the greatest reductions are achieved for cladding, flooring

surface, ceiling, and wall finishes.

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Table 7 – Impact allocation scheme and impact reduction scheme.

Impact Allocation Scheme Impact Reduction Scheme

(as % of total embodied impact)

Material change

(as % of max embodied impact)

Thickness change

(as % of max embodied impact)

Uniformat

element

Assembly Minimum

impact

Maximum

impact

Min impact

reduction

Max impact

reduction

Min impact

reduction

Max impact

reduction

Whole building 62.95 74.94 19.98 37.33

A: Substructure 0.21 55.07 7.77 19.65 0.33 2.03

piles 1.35 51.51 7.77 19.65 0.33 0.63

footings 11.13 55.07 n/a n/a 0.33 0.63

mat foundation 0.21 10.68 n/a n/a 0.85 2.03

B: Shell 2.22 78.97 21.17 49.64 6.03 27.27

columns and

beams 0.27 19.18 2.59 4.50 n/a n/a

floor 0.29 25.77 3.74 7.31 n/a n/a

roof 0.02 2.78 0.36 0.47 n/a n/a

stairs 0.00 2.13 0.25 0.50 n/a n/a

cladding 0.01 68.47 6.78 35.08 5.10 26.39

exterior walls 0.24 30.38 0.97 5.15 n/a n/a

glazing 0.35 55.61 0.60 7.52 0.22 3.17

doors 0.00 0.13 0.01 0.03 n/a n/a

C: Interiors 5.42 70.27 14.98 26.23 8.61 13.82

partitions 0.92 39.40 6.81 12.20 n/a n/a

doors 0.00 0.44 0.05 0.09 n/a n/a

wall finishes 0.89 23.40 1.35 1.81 2.02 2.5

flooring 0.18 44.69 6.77 12.13 3.76 6.73

ceiling 1.95 30.78 n/a n/a 2.69 4.81

D: Services 8.06 70.27 1.09 1.94 0.00 0.78

mechanical 4.12 42.62 1.09 1.94 0.00 0.57

electrical 2.96 24.10 n/a n/a 0.00 0.2

plumbing 0.84 6.72 n/a n/a 0.00 0.01

fire 0.03 0.21 n/a n/a n/a n/a

conveying 0.05 0.38 n/a n/a n/a n/a

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Table 8 – Ranking scheme for material decisions achieving embodied impact reductions.

Material

decision number Assembly (specific component)

(a) Minimum

impact reduction

(a) Maximimum

impact reduction

1 Cladding 38.86 59.70

2 Substructure (vapor barrier, piles) 29.71 44.23

3 Partitions 22.89 32.03

4 Flooring surface 16.11 21.75

5 Floor structural assembly 10.76 17.28

6 Column and beam

structural assembly

6.50 14.52

7 Window assembly 5.39 8.37

8 Wall assembly 3.14 4.78

9 Wall finishes 1.41 2.97

10 Mechanical system

(duct insulation)

0.71 1.03

11 Roof assembly 0.32 1.00

12 Stairs 0.07 0.12

13 Interior doors 0.01 0.03

14 Exterior doors 0 0 (a) Material impact reduction ranges reflect the reduction in embodied impact possible after each decision has been made.

Therefore, no impact reduction is possible once the final decision has been made.

Table 9 – Ranking scheme for thickness decisions achieving embodied impact reductions.

Thickness

decision number Assembly

(a)Minimum

impact reduction

(a)Maximum

impact reduction

1 Cladding 10.67 16.67

2 Flooring surface 6.69 10.09

3 Ceiling 3.82 6.98

4 Wall finishes 1.56 4.69

5 Substructure 0.24 3.66

6 Window assembly 0.00 0.78

7 Mechanical system 0.00 0.21

8 Electrical system 0.00 0.01

9 Plumbing system 0 0 (a)

Thickness impact reduction ranges reflect the reduction in embodied impact possible after each decision has been made.

Therefore, no impact reduction is possible once the final decision has been made.

Figures 17 and 18 are histograms illustrating the distributions of embodied impact

reductions across all 5,832 designs before any decisions have been made, after the first decision

achieving the greatest reduction has been made, and after the second decision achieving the next

greatest reduction has been made for material and thickness choices, respectively. The “Analysis

process” section described how these histograms are generated. The impact ranges for each

decision correspond to the minimum and maximum impact reduction values in Tables 8 and 9.

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Impact Reduction Due to Material Decisions

Impact reduction (as % of max embodied impact)

Num

ber

of

desi

gns

No material decisions made

Decision achieving greatest reduction: Cladding

Decision achieving second greatest reduction: Substructure

The rightmost distributions in Figures 17 and 18 show the potential for reducing a building’s

maximum total embodied impact before any material or thickness decisions have been made. For

Figure 17, anywhere from a 63% to 75% reduction in the building’s maximum total embodied

impact is possible, depending on the particular set of design parameters selected from Table 6.

Once the cladding material decision has been made, the impact due to cladding is substracted

from the total impact, and between 39% to 60% of the remaining embodied impact can be

reduced. This process continues in a similar fashion for the remaining 13 material decisions until

no impact reduction is possible once the final decision has been made. Results for the thickness

decisions are presented in a similar fashion, with anywhere from a 20% to 37% reduction in the

building’s total embodied impact possible depending on the design configuration. The

histograms allow designers to visually determine during the early design stages which building

components are most important in terms of achieving embodied impact reductions through

material and thickness choices.

\

Figure 17 – Embodied impact reduction due to material decisions.

77

Impact Reduction Due to Thickness Decisions

Impact reduction (as % of max embodied impact)

Num

ber

of

desi

gns

No thickness decisions made

Decision achieving greatest reduction: Cladding

Decision achieving second greatest reduction: Flooring Surface

Figure 18 – Embodied impact reduction due to thickness decisions.

Tables 7-9 and Figures 17 and 18 together show that significant embodied impact

reductions can be achieved in the substructure, shell, and interiors of the case study building. The

largest reductions for material changes are cladding, substructure, and partitions, and the least

important material decisions are related to the doors, stairs, and service equipment. The largest

reductions for thickness dimension changes are cladding, flooring surface, and the ceiling, and

the smallest reductions are for the window assembly and service equipment. The impact

reduction schemes for material and thickness dimensions taken together suggest that significant

reductions in the building development’s embodied impact cannot be achieved by making

decisions for the wall finishes or service equipment.

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The distributions show the relative importance of making a decision for one building

component over another. Designers are provided with intuition on which decisions frequently

achieve significant embodied impact reductions. By postponing material and thickness decisions

achieving smaller reductions to the design development stage, designers would avoid expending

effort on inconsequential decisions during the critical early design stages. The method ultimately

allows designers to focus their efforts during the early design stages on those decisions that are

most likely to decrease a building’s life cyle embodied environmental impact.

Conclusions

A BIM-enabled decision support method is proposed that helps designers predict which

decisions most critically determine a building’s embodied impact. The automated method

integrates BIM, LCA, energy simulation, MRR scheduling, and sensitivity analysis software.

The framework is well suited for the early design stages as very few inputs are required, and the

method can quickly iterate across many building designs thereby presenting a number of design

alternatives.

A case study analysis is presented in order to show how designers can understand which

building component decisions consistently contribute the largest to a building’s embodied

impact. Results are presented in the form of an impact allocation scheme, an impact reduction

scheme, and histograms showing the distributions of embodied impacts for many designs. The

results rank building components from those achieving the greatest to least embodied impact

reductions. A building’s embodied impact can potentially be concentrated in the substructure,

shell, or interiors. Embodied impacts due to service equipment are small, whereas cladding

material and thickness choices are consistently the most significant, regardless of building design

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configuration. A designer should focus during the early design stages on these decisions that

achieve a large embodied impact reduction and defer less important decisions to the design

development stage.

The scope of this method is limited to building components for which dimensional

thickness ranges can be predicted at the early design stages. Future work will consider additional

sizing parameters besides thickness in order that structural components and service equipment

may be included in the sizing decisions. Operational impacts do not include impacts from HVAC

or lighting equipment, plug loads, or water use. Future research will consider the effects that

orientation and thermal properties of the building envelope have on operational energy use in

order to develop a more comprehensive understanding of the relationship between early stage

design decisions and life cycle environmental impacts. Finally, the validation of the method is

currently limited to a single case study involving a particular building type, size, location, and

geometry. Additional case study applications will be required to comment more generally on the

performance and robustness of the proposed decision support method.

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Chapter 4: Evaluating embodied versus operational

environmental impact trade-offs of conceptual

building designs1

Abstract

The conceptual building design phase involves decisions that have significant life cycle

environmental impact implications. Decisions made during this phase may be costly to change

during later design stages. By understanding how design alternatives compare in terms of carbon

footprint, designers can make choices resulting in buildings with lower impacts. An important

part of this process is understanding the embodied versus operational impact trade-offs of design

decisions. Improving the operational performance of a building may negatively impact its

embodied performance. Similarly, decisions weighted towards embodied energy reduction may

greatly increase operational energy. This paper presents an automated method that employs a

multi-objective genetic algorithm to analyze the trade-offs between embodied and operational

impacts for a range of building design alternatives. A residential case study is used to evaluate

trade-offs for several decisions, including window-to-wall ratio, shading devices, and glazing

thickness. Solutions yielding significant reductions in the building’s carbon footprint are

identified. The method also determines the degree to which strategies weighted towards

minimizing embodied or operational impacts yield lower total impact. Designers are informed of

the relative importance of embodied versus operational impacts for several decisions and enabled

to make decisions leading to less energy intensive and more sustainable buildings.

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1This paper was co-authored with Assistant Professor Michael Lepech and submitted to Energy and Buildings in

October 2013.

Keywords: Conceptual building design, embodied energy, operational energy, environmental

impact, optimization

Introduction

The building industry is a primary consumer of energy and natural resources, and

building energy consumption has reached between 20% and 40% of total energy use among all

industry sectors in developed countries (Perez-Lombard et al. 2008). The problem extends to

developing countries in such regions as Asia and the Middle East, where energy consumption

has risen dramatically in recent decades due in large part to the increase in building construction

(Aboulnaga 2006). High rise buildings built in such urban centers as Shanghai and Doha have

contributed to developing countries’ carbon emissions per capita rising to twice those of western

developed countries in the past two decades (Kazim 2007).

Buildings’ life cycle environmental impacts are determined by embodied and

operational energy use (Ding 2004, Crowther 1999). Embodied energy is energy sequestered in

building materials during production processes, on-site construction, maintenance, repair,

replacement, and end-of-life scenarios. Operational energy is energy expended during the life of

the building, and this includes energy for heating and cooling, lighting, water use, and operation

of appliances (Dixit 2010).

Operational energy typically dominates the energy profile of buildings, contributing

80% or more of the life cycle energy consumption (RAIA 2004). Research has primarily focused

on ways to reduce operational energy by improving the thermal efficiency of the building

envelope (Der-Petrossian 2000, Scheuer and Keolian 2002). One problem with this approach is

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that building designs with highly insulated wall and window assemblies improve the operational

energy efficiency of buildings at the expense of using materials with high embodied energy

and/or increasing building material quantities (Huberman and Pearlmutter 2008). Such energy

efficient strategies increase the energy requirements of the material production phase, and these

may outweigh the operational energy savings and ultimately increase the life cycle

environmental impact. The role of embodied energy and its relationship to operational energy

becomes increasingly more important, especially since embodied energy has been found to

contribute up to 60% of a building’s life cycle energy use (Langston and Ding 2001, Thormark

2002, Yohanis and Norton 2002). This is also apparent as strategies for reducing operational

energy become more prominent in building design (RAIA 2004, Cole and Kernan 1996, Mumma

1995).

Building designers often make several decisions exhibiting embodied versus operational

energy trade-offs. The decisions typically involve components in the building envelope, since

components here have been found to account for nearly 30% of buildings’ embodied energy

(Atkinson et al. 1996, Yohanis and Norton 2002). Here, embodied and operational energy often

have an inverse relationship with each other. For example, increasing glazing thickness will

improve a window’s thermal resistance, thereby decreasing the amount of energy needed to cool

a building. This increased thickness comes at the expense of reducing the glazing’s thermal

transmissivity, which increases the amount of energy needed to heat the building. In addition, the

increased thickness increases the building’s embodied energy, since a greater amount of glazing

material is produced. Similarly, energy efficient strategies may reduce a building’s window-to-

wall ratio (WWR) may improve a façade’s thermal performance during the operation of the

building. However, the energy consumed during the material production phase may increase if a

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building’s cladding material has a greater embodied energy per kilogram than its glazing

material. The net embodied energy will be greater than for the higher WWR. If this increase in

embodied energy is greater than the embodied energy savings, then the net effect of reducing the

WWR is to increase the life cycle environmental impact of the building. Given Earth’s surface

temperature has increased by between 0.3 and 0.6°C over the last 100 years (Houghton et al.

1995), such trade-offs may become increasingly important in regions with extremely hot climate

conditions. For example, cities on the Arabian Peninsula are likely to face summer mean

temperatures often exceeding 40° C (Jentsch et al. 2010). In regions with such harsh climatic

conditions, the cooling needs of poorly designed buildings may be considerable (Al-Homoud

2005). Highlighting the energy trade-offs associated with building envelope decisions can avert

this problem by allowing designers to make design decisions optimized for building energy

reductions, thereby leading to lower carbon emissions.

Designers also need a systematic method of exploring the large number of design

possibilities associated with these energy trade-offs. Design teams typically explore building

design alternatives using an iterative, trial and error process that is performed manually.

Exploring the design space in this way can be difficult, given the large number of alternatives

and the complicated interactions between many design variables. Designers also often reason

backwards using a deductive approach to make large problems more manageable (Ahmed et al.

2003). This approach can leave large areas of the design space unexplored. Automated

procedures to optimize aspects of building design can reduce the effort needed to systematically

search the complete design space for high-performing design solutions (Flager et al. 2012).

This paper introduces an optimization method that provides designers with information

on the embodied versus operational energy trade-offs of building design decisions for a range of

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design alternatives. The method calculates impacts for several conceptual design decisions,

including shape, massing, site orientation, and building materials. The method also generates

many design alternatives, and probabilistic analysis of these alternatives determines whether

minimizing only for operational energy and/or embodied energy can serve as a proxy for

minimizing for both embodied and operational energy. In this way the design problem may be

simplified, and designers can focus effort on decisions that minimize only operational or

embodied energy.

Related Studies

A number of studies have looked at methods for evaluating the environmental impact

implications of building design decisions. Radford and Gero’s (1980) research introduced the

idea of trade-off diagrams using a Pareto optimality approach. By moving beyond trial and error

methods and quantitatively relating the performance of design variables to each other, the

research offered a prescriptive method for improving building performance. This approach was

used to show trade-offs between day-lighting and peak summer temperature as well as associated

values for a set of design variables, including glass type, window size, and sun shade projection

(Gero et al. 1983).

Recent studies have applied this method when looking specifically at the embodied

versus operational energy trade-offs of building design decisions (Hacker et al. 2008, Thormark

2006, Rai et al. 2011, Ardente et al. 2008). Huberman and Pearlmutter (2008) looked at

embodied and operational energy consumption of a building in a desert region in Israel when

optimizing building materials according to minimum life cycle energy requirements. Tuhus-

Dubrow and Krarti (2010) included building shape variation in using a genetic algorithm to

85

optimize a building envelope for minimized energy use. Pierquet et al. (1998) looked at wall

system trade-offs for buildings in cold climates in the United States and found that systems made

from non-renewable materials often performed more poorly than systems made with natural

materials. Radhi (2010) also looked at wall systems and incorporated sensitivity analysis to show

the economic, operational, and embodied energy performance trade-offs. The research concluded

that trade-offs are not straightforward and that a careful approach should be taken when choosing

building materials to reduce carbon emissions.

Wang et al. (2005a, 2005b) also evaluated building design alternatives in terms of both

embodied and operational energy consumption. The variables in the study included building

orientation, aspect ratio, window-to-wall ratio and wall construction type. A multi-objective

genetic algorithm was applied to identify Pareto optimal solutions considering both energy

consumption and life cycle cost objectives. The energy simulation performed did not consider

natural lighting or define multiple thermal zones within the building. Maintenance, repair and

replacement impacts were also not included in the analysis.

Although many of the studies used a prescriptive approach to evaluate building design

decisions, none explicitly optimized embodied versus operational energy impacts for a broad set

of design variables. The research here fills this gap by using an automated Pareto optimization

approach to consider a comprehensive set of variables exhibiting this trade-off. The method

evaluates a range of building shapes and massing alternatives, in order to increase the utility of

the method specifically during the conceptual design phase as well as to generalize the findings

across a range of building prototypes. The research relies on probability distributions in

conveying the results, in order to show the likelihood of a given design choice associated with a

trade-off variable being found among designs optimized for environmental impacts. Such a

86

method allows designers to quickly determine how strongly a design choice yielding either an

embodied or operational impact reduction correlates with a reduction in the building’s overall

impact. The research intends to help designers understand the complicated energy trade-offs of

building design choices and lend careful calibration to conceptual design decision-making in a

way that will help minimize buildings’ life cycle environmental impacts.

Methodology

The goal of the proposed methodology is to enable designers to accurately assess

trade-offs between embodied and operational impacts for a predefined range of building design

alternatives. To illustrate the potential to optimize for environmental performance across a large

number of building systems, the building’s substructure, façade, interior, and service equipment

are included in the analysis.

The shaded area in Figure 19 shows the phases of the building life cycle that are

considered in the research. Evidence from previous research suggests the included phases,

namely raw material acquisition, building material production, maintenance, repair, and

replacement, and operation account for over 95% of a building’s life cycle environmental impact

(Cole and Kernan 1996). Demolition has not been included since impacts associated with this

phase have been shown to be difficult to calculate (Pushkar et al. 2005, Schoch et al. 2011) and

small when compared with other phases (Scheuer et al. 2003).


environmental impact of buildings, including global warming potential, human toxicity, and

acidification, among others (Jolliet et al. 2003). Although the authors recognize the importance

of all of these categories in assessing the life cycle environmental impact of buildings, the

87

proposed method considers only global warming potential. The metric used for this indicator is

carbon dioxide equivalents (CO2e), which measures the total amount of greenhouse gas

emissions of the building.

Figure 19 – Building life cycle phases included in scope.

The general steps involved in the proposed method for evaluating the embodied versus

operational environmental impact trade-offs of conceptual building design decisions are shown

in Figure 20. The arrows in the figure represent data dependencies between process steps. The

analysis process begins with a building information model representing a given design

configuration. This model describes the building’s geometry, materials and components as well

as the project’s geographic position and orientation. The embodied carbon footprint is calculated

based on the building material and component quantities extracted from the model. Additional

details on how the embodied carbon footprint is calculated can be found at Basbagill et al.

(2013).



created based on the geometry and building material information contained in the building

88

information model. Thermal zones are also defined in the model as well as standard assumptions

regarding building occupancy and HVAC system controls (ASHRAE 2009).

A maintenance, repair and replacement schedule is used to determine the impacts

associated with service equipment during the operational phase of the building. Impacts

associated with the production of materials in this schedule are grouped with the building’s

embodied impact. The schedule is determined by the gross floor area, building type, location,

and structural and mechanical details defined in the building information model, which are

entered into an online facility operations reference database (CostLab 2011).

Operational carbon footprint calculations have two components. The first depends on the

building’s electricity and natural gas consumption as calculated by the energy simulation model.

These quantities are multiplied by a unit impact to calculate carbon footprint. The second


The carbon impact of the mechanical, electrical, and plumbing equipment is determined by

looking up a typical material, material quantity, and cost for each component using equipment

supplier documentation. Each quantity is then multiplied by a unit impact in a similar fashion to

the pre-operational impact calculations. Total environmental impact is calculated by summing

embodied and operational CO2e totals.

89

Figure 20 – Software integration for optimized life cycle environmental impact feedback.

A genetic algorithm is applied to automatically iterate the carbon footprint analyses

described above across a defined range of design variables. The algorithm is used for three

separate objectives: minimization of embodied energy, minimization of operational energy, and

minimization of total energy, which is the sum of embodied and operational energies. The goals

of the optimization process are twofold: first, to determine whether a strategy that focuses on

minimizing only embodied energy and/or a strategy that focuses on minimizing only operational

energy can consistently yield designs with low total carbon footprint. This involves determining

whether either strategy yields designs with total carbon footprint close to the carbon footprint of

designs minimized for total carbon footprint. In this way, it is determined whether minimizing

embodied and/or operational impact can act as a proxy for minimizing total impact. The second

goal is to highlight the embodied versus operational impact tradeoffs for six variables: WWR,

glazing thickness, presence of fins, presence of overhangs, fin depth, and overhang depth.

Probability mass functions are constructed in order to characterize the impacts associated with

each variable value then determine which values are consistently found in high-performing

90

designs. Inspection of these functions determines whether minimizing embodied or operational

energy alone may reduce a building’s life cycle environmental impact to a similar degree as a

strategy that minimizes embodied and operational energy together.

A sampling algorithm is used to understand the full range of total impacts possible for a

given set of design variables. The algorithm generates a probability mass function, which is

useful for showing how designs generated by the genetic algorithm compare to the full range of

total impacts possible for the problem definition under consideration.

Seven software components are used to implement the proposed method illustrated in

Figure 20. DProfiler is used for building information modeling (DProfiler 2012). SimaPro and

the Athena EcoCalculator are used for life cycle environmental impact data and for calculating

the building’s carbon footprint (SimaPro 2010, Athena 2011). The energy simulation software

eQUEST is used to calculate operational energy (eQUEST 2010), and CostLab is used to

estimate the service schedules (CostLab 2011). Excel is used to calculate the carbon footprint

metrics based on the data provided by the previous components (Excel 2007). The optimization

and sampling processes are implemented using ModelCenter, a program that allows users to

bring commercial software tools into a common environment using software “wrappers” to

facilitate the application of automated design space exploration techniques (ModelCenter 2008).

The genetic algorithm chosen is the Darwin algorithm, and the sampling algorithm chosen is an

orthogonal array for 90% of the designs and a Latin hypercube for 10% of the designs.

91

Case Study

A residential complex of four eight-story buildings located in the Middle East is used as a

case study to evaluate the embodied versus operational impact trade-offs of several building

design decisions. The proposed design has buildings of identical size, shape, orientation, and

building materials. At the time this paper was submitted, the initial design scheme for the

buildings had been determined. The case study thus provided an opportunity to show how

retrospective changes in design could reduce the environmental impact of the building complex.

The proposed design has a total floor area of 50,468 m2, and the service life of the

building was assumed to be 30 years. The floor-to-floor height is 4.0 m. The building envelope

consists of a uniform cladding pattern consisting of steel and a translucent glazing material. The

mechanical system is a variable air volume forced air system with direct-expansion coils for

cooling and a central furnace for heating. Internal loads and the weekly operating schedule were

determined for a residential building using the 2009 ASHRAE Fundamentals (ASHRAE 2009).

Problem formulation

Table 10 summarizes the objectives and variables used in the optimization study. Carbon

footprint was calculated in terms of CO2e as described in the Methodology section. The

following energy conversions were used to perform the analysis: electricity impact: 0.664 kg

CO2e/kWh, natural gas: 0.251 kg CO2e/kBtu (SimaPro 2010).

Fourteen design variables were manipulated to minimize the environmental impact of the

building complex: (1) amount of glazing on the facade as a percentage of total façade area, (2)

glazing thickness, (3) presence or absence of fins and overhangs, (4) fin and overhang depth, (5)

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building “H” shape determined by six shape parameters, (6) number of buildings, and (7) number

of floors. Total square footage remained constant. Table 11 lists the materials and impact factors

for each of the variables having an embodied versus operational energy impact trade-off.

Table 10 – Optimization problem formulation describing objectives and variable values.

Objectives

Minimize embodied impact (kg CO2e/m2)

Minimize operational impact (kg CO2e/m2)

Minimize total impact (kg CO2e/m2)

Variables Possible values

Window-to-wall ratio 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%

a,b

Glazing thickness (m) 0.0032, 0.0064, 0.0095, 0.0127, 0.0159, 0.0191

Has fins? true, false

Has overhangs? true, false

Fin depth (m) 0.3048, 0.9144, 1.524, 2.134

Overhang depth (m) 0.3048, 0.9144, 1.524, 2.134

Building shape a: 10, 15, 20, 25, 30

b: 10, 15, 20, 25, 30

c: 0, 5, 10, 15, 20, 25, 30

d: 10, 15, 20, 25, 30, 35, 40, 45, 50

e: 5, 10, 15, 20, 25

cf

Number of buildings 3, 4

Number of floors 5, 6, 7, 8

Orientation 0, 5, 10, …, 345, 350, 355

aU-factor (W/m

2*K) associated with each glazing thickness: 0.46, 0.23, 0.16, 0.12, 0.092, 0.077.

bSolar heat gain coefficient is 0.32 and visible transmittance is 0.62 for each glazing thickness.

cShape parameter “f” is dependent on the values for a, b, c, d, and e and ranges from 0m to 30m.

93

Table 11 – Material, dimensional, and impact assumptions for building components with

embodied versus operational impact trade-offs.

Building Material Thickness (m) Area (m2) Embodied Impact

Component (kg CO2e/kg material)

Glazing aVNE 1-63

bvaries

cvaries 1.06

Cladding steel 0.0677 cvaries 1.89

Fins concrete b,varies 1% of glazing area 0.121

Overhangs concrete bvaries 3% of glazing area 0.121

aGlazing supplied by Viracon (2013).

bValues are given in Table 10.

cDepth values are proportional to WWR, the values of which are given in Table 10.

Results

Comparison of optimization objectives

The distribution of design configurations generated by the proposed optimization process

in the performance space is shown in Figure 21. The graph represents a total of 10,297 designs

generated over 94 hours. The final design proposed by the design team for the building complex

is shown as well as the best design for each of the three objectives. A Pareto front shows the

relationship of each of these designs to the set of non-dominated best designs, and designers can

quantitatively see the tradeoffs between embodied and operational impacts. Operational energy

dominates the life cycle energy consumption and ranges from 76% to 99% of the total impact.

Eighty-seven percent of the design configurations generated by the proposed optimization

process exhibit improved performance with respect to total environmental impact compared to

the design chosen by the design team.

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Figure 21 – Distribution of optimized design configurations.

The distribution of total impacts generated by the sampling algorithm is shown in Figure

22. The results consist of 5,522 designs and have a mean total impact of 19,170 kg CO2e per m2.

The most frequent range occurs between 16,000 to 17,000 kg CO2e per m2, the minimum impact

is 4,589 kg CO2e per m2, and the maximum impact is 50,888 kg CO2e per m

2. In comparing

Figures 21 and 22, 91% of the designs generated by the optimization algorithm have total

impacts less than 90% of the total impacts for the designs generated by the sampling algorithm.

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Figure 22 – Distribution of life cycle environmental impacts.

Table 12 compares the best designs for the three objectives with the proposed design

configuration. The mean total impact for the top-performing designs for the strategy that

minimized operational impact was only 0.51% greater than the strategy that minimized both

operational and embodied impacts. This suggests that a strategy for minimizing carbon impact

that takes into account only operational impact may yield just as favorable results as when both

embodied and operational impacts are considered. This may be useful for a design team which

lacks the knowledge of material choices for building components and would like to focus on

operational impact. The strategy minimizing for operational impact also discovered the design

with the lowest total impact, which was lower than the proposed design’s total impact by 62%.

This value was within 1% of the best design found by the algorithm minimizing for total impact,

further suggesting minimizing for operational impact may act as a proxy for minimizing total

impact. The mean total impact for the top-performing designs minimized for embodied impact

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was 52% greater than the strategy that minimized both operational and embodied impacts,

suggesting minimizing for embodied impact is not a good proxy for minimizing total impact.

The best designs for all three optimization objectives were better than the best design

discovered by the sampling algorithm in Figure 22, showing the utility of the genetic algorithm

in discovering high-performing designs regardless of the objective. This suggests a strategy

utilizing optimization algorithms are more effective at discovering high-performing designs than

sampling algorithms. Designs with low embodied impact often had a high operational impact,

further suggesting that minimizing for embodied impact may not approximate well high-

performing designs minimized for total impact. For example, the lowest embodied impact for the

minimized embodied impact objective was 539 kg CO2e/m2, or 38% lower than the embodied

impact of the design with the lowest total impact for this objective. Yet the operational impact

corresponding to this embodied impact was 8,015 kg CO2e/m2, or 119% higher than the

operational impact for the best design for this objective.

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Table 12 – Comparison of trade-off variable values and carbon impacts.

Selected design configurations

●

Baseline Minimized

total impact

Minimized

embodied impact

Minimized

operational impact

Mean total impact

(kg CO2e/m2)

a

-- 4,285 6,520 4,307

Variables

Glazing thickness (m) 0.013 0.0032 0.0032 0.0032

Window-to-wall ratio 30% 15% 15% 15%

Has fins? true true false true

Has overhangs? true false false false

Fin depth (m) 0.30 0.30 -- 0.30

Overhang depth (m) 0.30 -- -- --

Best design (kg CO2e/m2)

Total 8,783 3,424 4,473 3,348

Relative difference -˗ -61% -49% -62%

Embodied impact 741 893 814 814

Operational impact 8,042 2,531 3,659 2,534

Lowest impacts (kg CO2e/m2)

Embodied n/a 558 540 558

Operational n/a 2,531 3,659 2,534 a

Calculated among top 10% of designs with lowest total impact.

Analysis of trade-off variables

Figures 23 through 26 represent probability mass functions for the six variables

associated with embodied versus operational impact trade-offs. The figures show the likelihood

of each variable value appearing in the top 10% of designs minimized for either embodied

impact, operational impact, or total impact. The graphs make clear which variable values are

likely to be found in high-performing designs and which are less likely. They also determine

whether a strategy weighted towards minimizing embodied and/or operational impacts correlates

with a strategy minimizing total impact, is likely to achieve high-performing designs, and can

therefore serve as a proxy for a strategy that minimizes both embodied and operational impacts.

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Figure 23 shows the distributions for WWR. The minimized operational impact objective

correlates strongly with the minimized total impact objective, as all top designs for both

optimization strategies converge to the minimum value of 15%. The minimized embodied impact

showed a strong preference for the maximum value of 50%, which is expected given the lower

impact factor for glazing in Table 2 compared with the steel cladding impact factor. The results

suggest that for WWR a strategy minimizing only operational impact is a good approximation of

total impact and likely to achieve high-performing designs, whereas a strategy that minimizes

only embodied impact may not serve as a good proxy and may not yield high-performing

designs. Future work will consider alternate cladding materials to determine the extent to which

these results are specific to steel cladding.

Figure 23 – Distribution of window-to-wall ratio values.

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Figure 24 shows the distributions for glazing thickness. The minimized embodied impact

objective strongly favored a low value, which is to be expected since glazing thickness is

proportional to embodied impact. The minimized operational objective also favored the low

value but to a lesser degree. The distribution for the minimized operational objective correlated

fairly well with the total impact distribution and better than did the embodied impact distribution,

suggesting that for glazing thickness a strategy minimizing only operational impact is also a

fairly good approximation of total impact.

Figure 24 – Distribution of glazing thickness values.

Figures 25 and 26 are the probability distributions for the presence of fins and overhangs

and show that each objective strongly preferred no fins or overhangs. The results for minimized

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embodied impact are predictable, since embodied impact increases when shading devices are

present. As for total impact, the presence of the devices may have blocked a significant amount

of solar energy during the cooler portions of the day, thus requiring an amount of heating energy

that was greater than the difference between the cooling energy saved and the embodied energy

of the shading devices. A strategy that minimizes either embodied or operational energy is

therefore likely to achieve high-performing designs for these variables. A moderate percentage

of high-performing designs also had fins and overhangs for all three objectives. Careful

consideration must therefore be given to the placement of these components, since they have

noticeable effects on buildings’ embodied and operational impacts. Future work will consider

alternate climates, shading materials, and building sizes and will optimize the placement of

shading devices by building face in order to determine under which conditions the presence of

fins and overhangs will minimize a building’s life cycle environmental impact.

Figure 25 – Distribution of presence of fins values.

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Figure 26 – Distribution of presence of overhangs values.

Figures 27 and 28 are the distributions for fin and overhang depth. Few trends emerged,

although the minimized total impact objective favored low overhang depth values. Given that

fewer than 35% of the top designs favored shading devices across all three objectives, it is not

surprising that preferences did not emerge. This is likely because changes to fin and overhang

depth were found to minimally affect embodied and operational impacts when no other variables

changed. As with the presence of fins and overhangs, future work will consider other climates,

building sizes, and shading materials in order to determine under what conditions changes to

shading depth may significantly alter a building’s embodied and operational impacts.

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Figure 27 – Distribution of fin depth values.

Figure 28 – Distribution of overhang depth values.

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Sensitivity Analysis2

Additional analyses were carried out in order to understand the degree to which the

results may be generalized across a broad set of variables. Previous research performed by

Basbagill et al. (2013) demonstrated that changes to cladding material can significantly affect a

building’s embodied impact. Changes to geographic location (Pulselli et al. 2009, Christenson et

al. 2006, Krarti et al. 2005) and gross floor area (Chung et al. 2006) have also been shown to

significantly affect a building’s energy performance. Therefore sensitivity analysis was

conducted on these three variables in order to comment on the degree to which the results may

be generalized to variables potentially significantly influencing the results. One alternative was

analyzed for each variable. Concrete was chosen to replace steel as the cladding material, since

concrete’s unit impact of 0.121 kg CO2e/kg material is significantly lower than steel’s value of

1.89 kg CO2e/kg material. In addition, concrete’s unit impact is significantly lower than the

glazing unit impact of 1.06 kg CO2e/kg material given in Table 11, whereas steel’s unit impact is

nearly twice that of the glazing material. Therefore it was believed that for concrete cladding the

minimized embodied impact objective would likely favor a low WWR value that better aligns

with the minimized total impact and minimized operational impact objectives than the high value

favored in Figure 23 by steel. Chicago was chosen as the alternate location, since Chicago is

located in climate zone 5 (cool, humid). The weather here is significantly different than the

weather for climate zone 2a (hot, humid) used in the results. A building gross floor area of

30,000 m2 was chosen as the alternate building size.

2This section provides additional analysis which was not included in the submission to Energy and Buildings.

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Cladding material

Concrete was chosen as the alternate cladding material. Results are presented in terms of

the Pareto plot (Figure 29) and corresponding table of metrics (Table 13), distribution of impacts

(Figure 30), and favored values for the six tradeoff variables for the three optimization strategies

(Figures 31-36).

The Pareto plot and Table 13 show that substituting concrete for steel cladding

consistently achieves significant reductions in embodied impact. The average embodied impact

across all three optimization strategies for concrete cladding is 500 kg CO2e/m2, whereas the

average impact for steel cladding is 710 kg, a reduction of 30%. This reduction falls to 24%

when the probability mass functions (Figures 22 and 30) are compared for the two materials,

which is still a significant reduction. Comparison of the probability mass functions also shows

that substituting concrete for steel cladding filters out the worst-performing designs. These

results are expected, given concrete’s significantly lower carbon impact factor.

When operational impact is included in the analysis, Table 13 shows that the mean

impact for concrete cladding for the top 10% of high-performing designs is lower than the mean

impact for steel cladding for all three optimization strategies. The best designs are also achieved

with steel cladding. Since a constant R-value is assumed for both steel and concrete cladding, no

embodied versus operational impact tradeoff exists for cladding material. Therefore, these results

are likely due to the randomness of the genetic algorithm in discovering better designs with

lower operational energy for concrete cladding.

The results can also generalize the answer to the research question asking whether an

optimization strategy that minimizes only operational impact can act as a proxy for total impact.

As with steel cladding, the mean impact for the top 10% of high-performing designs for the

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minimized operational impact objective is very close to the mean impact for the top-performing

designs for the minimized total impact objective. This alignment of values for a cladding

material with a high impact factor (steel) and a lower impact factor (concrete) for these two

optimization strategies suggests that operational impact may act as a good proxy for total impact

when implementing environmental impact optimization strategies regardless of cladding

material.

Figure 29 – Distribution of optimized design configurations for alternate cladding material.

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Figure 30 – Distribution of life cycle environmental impacts for alternate cladding material.

Table 13 – Comparison of trade-off variable values and carbon impacts for alternate cladding

material.


●

Baseline Minimized

total impact

Minimized

embodied impact

Minimized

operational impact

Mean total impact

(kg CO2e/m2)

a

-- 3,960 5,346 3,959

Variables



Has fins? true false false false


Fin depth (m) 0.30 -- -- --



Total 8,783 3,934 4,107 3,934

Relative difference -˗ -55% -53% -55%





Operational n/a 3,437 3,615 3,437 a


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Analysis of the tradeoff variables shows that WWR (Figure 31) converges to the

minimum value for all three optimization strategies. This is expected, given that concrete’s

carbon factor is lower than the glazing material. Distribution of values for glazing thickness

(Figure 32) and presence of fins (Figure 33) or overhangs (Figure 34) remain the same for both

concrete and steel cladding, and this is expected given that that no coupled effects between

embodied and operational impact were set up in the problem formulation between cladding

material and these variables. The shape of the distributions for fin depth (Figure 35) and

overhang depth (Figure 36) are different between the two cladding types. Given shading depth

was found to minimally influence total impact as discussed in the results, it is likely that the

shading depth distributions are due to randomness in the genetic algorithm’s choices for shading

depth.

Figure 31 – Distribution of window-to-wall ratio values for alternate cladding material.

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Figure 32 – Distribution of glazing thickness values for alternate cladding material.

Figure 33 – Distribution of presence of fins values for alternate cladding material.

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Figure 34 – Distribution of presence of overhangs values for alternate cladding material.

Figure 35 – Distribution of fin depth values for alternate cladding material.

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Figure 36 – Distribution of overhang depth values for alternate cladding material.

Climate

Chicago is located in climate zone 2a (cool, humid) and is chosen as an alternate climate

zone to compare with Houston in climate zone 5 (hot, humid). Results present the Pareto plot

(Figure 37) and corresponding table of metrics (Table 14), distribution of impacts (Figure 38),

and favored values for the six tradeoff variables (Figures 39-44).

The Pareto plot, Table 14, and the distribution of impacts show that operational impacts

are significantly greater in Chicago than in Houston. The average operational impact in Chicago

is 23,335 kg CO2e/m2, or 26% greater than Houston’s average operational impact of 18,451 kg

CO2e/m2. Minimizing only for embodied impact is a clear poor strategy yielding top designs

with average impacts 74% higher than designs minimized either for operational impact only or

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total impact. The distributions of impacts also have higher values and are much more spread out

with lower probabilities in general than the Houston impact values. The average total impact

values in Chicago and Houston are 24,053 kg CO2e/m2 and 19,170 kg CO2e/m

2, respectively, and

the standard deviation values in Chicago and Houston are 19,793 kg CO2e/m2

and 7,684 kg

CO2e/m2, respectively.

The research question asks whether considering only operational impact in optimization

strategies can serve as a proxy for optimizing the total impact. Results show that both Chicago

and Houston climates yield mean impacts for the top 10% of high-performing designs for the

minimized operational impact objective very close (less than 1%) to the mean impact for the top-

performing designs for the minimized total impact objective. Therefore, the answer can be

generalized and operational impact may act as a good proxy for total impact when implementing

environmental impact optimization strategies in humid climates with extreme hot and/or cold

weather.

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Figure 37 – Distribution of optimized design configurations for alternate climate.

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Figure 38 – Distribution of life cycle environmental impacts for alternate climate.

Table 14 – Comparison of trade-off variable values and carbon impacts for alternate climate.


●

Baseline Minimized

total impact

Minimized

embodied impact

Minimized

operational impact

Mean total impact

(kg CO2e/m2)

a

-- 11,404 19,843 11,419

Variables



Has fins? true false true false


Fin depth (m) 0.30 -- 2.1 --



Total 8,783 11,367 12,165 11,367

Relative difference -˗ +29% +39% +29%





Operational n/a 10,646 11,563 10,646 a


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Analysis of the tradeoff variables for the alternate climate shows that the distributions for

WWR, glazing thickness, and presence of shading devices are very similar to Houston. Therefore

minimizing only for operational impact is likely a good strategy for achieving high-performing

designs when determining WWR or presence of shading devices. For glazing thickness, such a

strategy aligns fairly well with minimizing total impact. For shading depth, no clear trends

emerge due likely to the relative unimportance of these variables on total impact.

Figure 39 – Distribution of window-to-wall ratio values for alternate climate.

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Figure 40 – Distribution of glazing thickness values for alternate climate.

Figure 41 – Distribution of presence of fins values for alternate climates.

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Figure 42 – Distribution of presence of overhangs values for alternate climate.

Figure 43 – Distribution of fin depth values for alternate climate.

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Figure 44 – Distribution of overhang depth values for alternate climate.

Building size

An alternate building size of 30,000 m2 is chosen to compare with the proposed design of

50,468 m2. Results present the Pareto plot (Figure 45) and corresponding table of metrics (Table

15), distribution of impacts (Figure 46), and favored values for the six tradeoff variables (Figures

47-52).

The Pareto plot, Table 15, and the distribution of impacts show that operational impacts

are significantly greater for the smaller building, although not to the same degree as the Chicago

analysis. The average operational impact for the smaller building is 21,010 kg CO2e/m2, an

increase of 14% over the larger building. As with cladding material and climate, minimizing only

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for embodied impact is a poor strategy yielding top designs with average impacts 48% higher

than designs minimized either for operational impact only or total impact. The average total

impact values for the smaller and larger buildings are 21,817 kg CO2e/m2

and 19,170 kg

CO2e/m2, respectively, and the standard deviation values are 7,924 kg CO2e/m

2 and 7,684 kg

CO2e/m2.

The research question asks whether considering only operational impact in optimization

strategies can serve as a proxy for optimizing the total impact. Both building sizes yield mean

impacts for the top 10% of high-performing designs for the minimized operational impact

objective very close to the mean impact for the top-performing designs for the minimized total

impact objective. Therefore, the answer can be generalized and operational impact may act as a

good proxy for total impact when implementing environmental impact optimization strategies for

building sizes between 30,000 m2 and 50,468 m

2.

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Figure 45 – Distribution of optimized design configurations for alternate building size.

Figure 46 – Distribution of life cycle environmental impacts for alternate building size.

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Table 15 – Comparison of trade-off variable values and carbon impacts for alternate building

size.


●

Baseline Minimized

total impact

Minimized

embodied impact

Minimized

operational impact

Mean total impact

(kg CO2e/m2)

a

-- 9,079 13,430 9,092

Variables



Has fins? true False false false

Has overhangs? true False true false

Fin depth (m) 0.30 -- -- --

Overhang depth (m) 0.30 -- 1.5 --


Total 8,783 9,026 9,387 9,026

Relative difference -˗ +2.8% +6.9% +2.8%





Operational n/a 8,253 8,414 8,253 aCalculated among top 10% of designs with lowest total impact.

Analysis of the tradeoff variables shows similar patterns for all six variables for the two

building sizes. Minimization of only operational impact is a good strategy when selecting WWR

values, since this strategy aligns well with top-performing designs for the minimization of total

impact objective. This strategy also yields strong alignment in glazing thickness and shading

presence values in top-performing designs for these variables for the minimization of operational

impact and the minimization of total impact objectives. No trends can be drawn from shading

depth, again likely due to the relative unimportance of these variables in contributing to total

environmental impact.

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Figure 47 – Distribution of window-to-wall ratio values for alternate building size.

Figure 48 – Distribution of glazing thickness values for alternate building size.

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Figure 49 – Distribution of presence of fins values for alternate building size.

Figure 50 – Distribution of presence of overhangs for alternate building size.

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Figure 51 – Distribution of fin depth values for alternate building size.

Figure 52 – Distribution of overhang depth values for alternate building size.

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Conclusions

Evaluating the embodied versus operational impact trade-offs of building design

decisions can play an important part in creating a sustainable built environment. The proposed

optimization method allows designers to evaluate these trade-offs over a range of design

alternatives for a specified set of design variables. The method shows whether a design strategy

that minimizes only embodied or operational impact can serve as a proxy for minimizing total

impact. Design efforts can then focus on minimizing only this impact. Variation in building

shape, orientation, and massing extends the method’s scope to broad building typologies.

Results of the proposed method applied to the case study show that a design strategy

minimizing only operational impact consistently yields high-performing designs. Such a strategy

resulted in a mean total impact for the top-performing designs that was only 0.51% less than the

mean total impact for designs minimized for both operational and embodied impacts. In

particular, this strategy yielded high-performing design values for WWR, glazing thickness, and

presence of shading devices. A strategy minimizing only embodied impacts did not consistently

yield high-performing designs and resulted in a mean impact that was 52% greater than the mean

impact for designs minimized for total impact. The method also has the ability to improve upon

designs, as 87% of the designs had total impacts lower than the actual design proposed by the

design team.

The method allows designers to understand whether minimizing embodied and/or

operational impacts can guide their decision making for variable choices and yield reasonable

approximations of the minimized total impact, or whether minimization of both impacts together

is a better strategy. The scope of this method is limited to six trade-off variables for one building

size and type in a hot and humid climate with only one material considered for each variable.

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Future research will evaluate case studies in additional climates with alternative building sizes

and materials. Additional trade-off variables will also be incorporated, such as R-values of wall

assemblies and roof slabs and cladding material thicknesses. Several of the variables can also be

optimized by façade to increase the utility of the method.

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Chapter 5: A methodology for providing

environmental impact feedback on sequential

conceptual building design decisions1

Abstract

Conceptual design decisions play a critical role in determining the life cycle environmental

impact performance of buildings. Stakeholders often make these decisions without a quantitative

understanding of how a particular decision will impact future choices or the ultimate

performance of a project. A sequential decision support methodology is developed to provide

stakeholders with precise information on the relative influence that design decisions have on a

project’s environmental impact performance. Sensitivity analysis is performed on the impacts

associated with thousands of building design alternatives. A case study is presented showing how

the proposed methodology may be used by designers with various design strategies in mind.

Results are presented in the form of probabilistic distributions, which show the degree to which

each decision helps achieve a given strategy’s objective. The method provides environmental

impact feedback throughout sequential decision-making processes, thereby aiding designers in

achieving various building performance objectives during the conceptual design phase.

1This paper was co-authored with Assistant Professor Michael Lepech and submitted to The International

Journal of Architectural Computing in October 2013.

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Introduction

Multidisciplinary design optimization (MDO) methods exist that allow designers to

explore very large design spaces, quickly evaluate many design alternatives, and find optimal or

near optimal solutions for various performance criteria. The benefits of MDO methods are well

documented in such industries as aerospace, automotive, and electronics. Within the architecture,

engineering, and construction industry, application of MDO methods has been shown to yield

significant reductions in building life cycle cost and environmental impacts compared to

conventional methods (Flager et al. 2012, Wang et al. 2005b).

Although MDO has potential to improve design process efficiency and the quality of the

resulting product, MDO methods are not widely used within the building design industry,

particularly during conceptual design. The conceptual design stage has been recognized as a

critical determinant of project cost and environmental impact (Ellis et al. 2008, Schlueter and

Thesseling 2009). At the conceptual design stage, many choices exist for building decisions,

such as building shape, massing, and dimensioning and materials for each building component.

These decisions are typically made by architects in sequential fashion, such that for example

once the orientation of the building is known, the placement of shading devices can be

determined for each façade in order to minimize cooling loads. Designers may also wish to

understand the life cycle cost and environmental impacts associated with the wall assembly

system before deciding upon the cladding system. Such a multi-objective sequential feedback

approach is typical in the architecture, engineering, and construction industry in that project

stakeholders often need to evaluate design decision trade-offs for competing objectives. For

example, a designer wishing to minimize both environmental impact and cost may find a certain

window-to-wall ratio lowers carbon footprint at the expense of greatly increased life cycle cost.

128





feedback on any single design choice. MDO methods do not integrate well with the architecture,

engineering, and construction industry, which relies on flexible and often-changing decision-

making processes, especially at the early stages.

A new method is proposed that integrates MDO methods with conceptual building design

in a way that provides quantitative feedback for a range of design strategies reliant on sequential

decision-making processes. Building information modeling software is integrated with life cycle

assessment and energy simulation software, allowing a sampling algorithm to generate thousands

of building design alternatives across the design space and compute life cycle environmental

impact feedback. Probability mass functions are then used to characterize the environmental

impacts of decisions as they are made in sequential fashion, thereby providing designers with

visual quantitative feedback on each of many alternatives.

Figure 53 shows how the method can be applied to three different sequential decision-

making strategies often used by designers. In each scenario, probabilistic distributions show the

range of life cycle environmental impacts possible for all design alternatives before any

decisions have been made. Once a decision is made, a new probabilistic distribution is generated

showing the range of impacts possible for the remaining design decisions. Designers are able to

understand the full range of control of environmental impact performance as well as the relative

influence of design decisions throughout the sequential decision-making process.

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In Figure 53(a), a designer would like to minimize a building design’s life cycle

environmental impact. This strategy relies on single-objective optimization, which several

studies have shown can be an effective strategy for helping designers minimize the

environmental impact of buildings (Coley and Schukat 2002, Al-Homoud 1997, Wetter 2001).

As each sequential decision is made, the designer understands whether a decision improves upon

the previous decision in terms of either reducing or increasing the building’s life cycle

environmental impact. The designer also understands with each new decision whether chances

improve, worsen, or have been eliminated of achieving the design with the lowest possible

carbon footprint. Throughout the process the designer knows the full range of control for each

design decision as well as how each decision relates to the initial range of environmental impacts

before any decisions were made.

A second sequential decision-making approach employed by designers is designing for

environmental performance values (Figure 53(b)). Andreu and Oreszczyn (2004) discuss how

this strategy can be effective in creating designs with low life cycle environmental impact. Such

a strategy caters to designers interested in building rating systems and assessment tools, such as

the Green Building Challenge and the United States Green Building Council’s Leadership in

Energy and Environmental Design program. Dozens of building performance tools exist, which

are designed to provide indicators on the environmental performance of design alternatives or

rate the environmental performance level of a building (Ding 2008). In Figure 53(b), a designer

has an environmental impact performance target in mind. As in Figure 53(a), probabilistic

distributions are generated with each new design decision, and designers can understand whether

each decision helps or hurts in achieving the specified target value.

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A third sequential decision-making approach employed by designers is the maintenance

of flexibility and adaptability (ALwaer and Clements-Croome 2010). This objective is

particularly relevant for designers with competing objectives in mind, such as optimizing designs

for both economic as well as environmental sustainability. In Figure 53(c), a designer wishes to

preserve freedom and flexibility throughout the design process by maximizing the number of

remaining designs as decisions are made. The designer does not want to be confined to a narrow

subset of designs, regardless of whether they have low, medium, or high environmental impacts.

Such a strategy maximizes the tradeoff options to be considered among the competing

objectives.

(a) (b) (c)

Figure 53 – Three sequential decision-making approaches to which the environmental impact

feedback method may apply: (a) minimization of carbon footprint, (b) achievement of a carbon

target value, and (c) maintenance of freedom and flexibility.

The decision support methodology is a quantitative approach that supplies stakeholders

with precise information about the relative influence that each design decision has on

environmental impacts. Three sequential decision-making strategies – minimizing environmental

impact, achieving performance values, and maximizing conceptual design freedom – are

described to which the method applies, although the method can easily be adapted to other

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approaches. Designers quickly assess which design variables are significant contributors to a

building’s carbon footprint and which are less important. The use of probability mass functions

allows designers to predict with each successive decision the probability of achieving a given

impact value, and decisions may easily be adjusted in order to increase or decrease this

probability. The method provides visual understanding of the range of control of the entire

design space’s environmental performance, and by accommodating various sequential design-

making approaches, the method enhances its utility as a conceptual design stage decision-making

tool.

Related Studies

Research in multidisciplinary design optimization (MDO) is used as a point of

departure in order to present the proposed research methodology. MDO involves the

formalization of design coordination and iteration for groups working on complex engineering

systems such as buildings and civil infrastructure. Computational optimization techniques are

applied to systemically search through a range of design options defined by the design team to

find solutions that best meet the objectives and constraints of project stakeholders. MDO

methods were first developed in the aerospace industry in the 1970’s and are now successfully

used in a number of fields including automotive, naval architecture, and electronics design

(AIAA 1991).

A number of studies have used MDO as a method for providing environmental impact

feedback on conceptual building designs (Hauglustaine and Azar 2001, Caldas and Norford

2002, Wright et al. 2002, Geyer 2009). Wang et al. (2005b) integrated building information

modeling, life cycle assessment, energy analysis, and MDO software in order to evaluate the

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environmental impact consequences of various conceptual building design parameters, such as

shape, orientation, and building materials. A multi-objective genetic algorithm was used to

identify Pareto optimal solutions for minimized cost and environmental impact performance,

resulting in reductions in cost and global warming potential. Al-Homoud (1997) applied a direct

search optimization technique in order to minimize the annual energy consumption of an office

building for different climates. The method provided optimized thermal performance feedback

on several hundred design alternatives, and variables included building orientation and thermal

properties of glazing materials. Coley and Schukat (2002) integrated building information

modeling and thermal analysis in developing a method for optimizing the energy performance of

a community hall. The method applied a genetic algorithm during the conceptual design phase,

and the results displayed a range of architecturally distinct designs minimized for operational

energy.

Prior MDO research has several limitations as far as its ability to integrate with AEC

industry design practices. MDO has limited ability to provide useful feedback on design

decisions. MDO feedback typically relies on Pareto fronts for evaluating the tradeoffs between

objectives. Such diagrams provide feedback only when designs are fully articulated. They offer

no performance evaluation on partially defined designs and fail to show the sensitivities of

performance criteria to changes in design variables. The feedback does not align well with a

design process in which decisions are made in sequential fashion and environmental impacts are

in flux with each successive design change. MDO feedback has also often been limited to the

operational phase of a building rather than its entire life cycle, and design variables are usually

concentrated in the building envelope. Embodied impacts of building components are not always

133

included, and variables relating to building systems’ materials and dimensions, particularly

structural assemblies, walls, floors, and finishes are often excluded.

The proposed method fills these gaps by providing environmental impact feedback on a

comprehensive set of design variables throughout the envelope, substructure, interiors, and

structural components. A design space consisting of a very large number of design alternatives is

evaluated by the method. A sampling algorithm generates probability mass functions, which

dynamically show designers how the range of control over environmental impacts changes with

each sequential decision. The distributions also show the sensitivity of environmental impacts to

changes to these variables. Embodied and operational impacts are included in the scope, as well

as a range of shape, massing, building material, and dimensioning parameters. The method is

well suited to a range of decision-making strategies, since in all cases designers understand the

likelihood of achieving a certain carbon footprint after each new design decision.

Methodology

The goal of the proposed methodology is to provide MDO feedback to designers at the

conceptual design stage in such a way that designers can understand the environmental impact

implications of design alternatives for each sequential decision. To illustrate the potential to

provide environmental performance feedback across a large number of building systems, the

building’s substructure, façade, interior, and service equipment are included in the analysis.





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proposed method considers only global warming potential. The metric used for this indicator is

carbon dioxide equivalents (CO2e), which measures the total amount of greenhouse gas

emissions of the building.

Figure 54 – Building life cycle phases included in proposed method for providing environmental

impact feedback on sequential design decisions.

The proposed methodology integrates several building design and energy analysis

software packages. Figure 55 presents images from the building information modeling software

DProfiler (DProfiler 2012). Each image represents a unique design configuration for a set of

input parameters including percentage of glazing, building orientation, building shape, number of

floors, and presence of fins or overhangs.

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showing variations in several input parameters.

The general steps involved in the proposed method for providing environmental impact

feedback on sequential design decisions are shown in Figure 56. The arrows in the figure

represent data dependencies between process steps. The analysis process begins with an initial

seed design manually inputted into the building information model. This may represent a design

team’s proposed solution to which they would like to see how alternatives compare, or the initial

design may be a random configuration. The building information model describes the building’s

geometry, materials, and components as well as the project’s geographic position and

orientation. The embodied carbon footprint is calculated based on the building material and

component quantities extracted from the building information model. Each quantity is multiplied

by a unit impact (kg CO2e) to determine the carbon footprint.






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associated with the production of materials in this schedule contribute to the building’s embodied

impact. The maintenance, repair, and replacement schedule is determined by the gross floor area,

building type, location, and structural and mechanical details defined in the building information

model, which are entered into an online facility operations reference database (CostLab 2011).

Operational carbon footprint calculations have two components. The first depends on

the building’s electricity and natural gas consumption as calculated by the energy simulation

model. These quantities are multiplied by a unit impact to calculate carbon footprint. The second


The carbon impact of the mechanical, electrical, and plumbing equipment is determined by

looking up a typical material, material quantity, and cost for each service component using

equipment supplier documentation. Each quantity is then multiplied by a unit impact in a similar

fashion to the pre-operational impact calculations. Total environmental impact is calculated by

summing embodied and operational CO2e totals. The sampling algorithm is then used to generate

thousands of design alternatives, and feedback on these alternatives is inspected as described in

the following section.

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Figure 56 – Method for providing probabilistic environmental impact feedback on sequential

building designs.

The goal of the proposed building information model-environmental impact feedback

integration method is to allow designers continuous probabilistic visualization of the

environmental impact performance of their design choices for a range of sequential decision-

making strategies. A sampling algorithm is used to characterize the life cycle environmental

impacts of sequential design decisions across a broad range of design variables. Probability mass

functions are constructed from the environmental impact data received from each of the

thousands of designs sampled. Inspection of these distributions allows designers to achieve their

particular objective, whether to minimize carbon footprint, achieve a carbon performance value,

or preserve design freedom. These strategies are not mutually exclusive, as a designer interested

in minimizing carbon footprint and meeting a performance value can test out different options

for a single design variable and see how the resulting distribution relates to the full range of

environmental impacts. The designer can then choose the value that preserves the most number

of desirable designs or, if no value yields a preferred distribution, the designer can backtrack,

modify a prior decision, and consider the new options.

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Figure 56. DProfiler is used for the building information modeling software (DProfiler 2012).

SimaPro and the Athena EcoCalculator are used for environmental impact data and for

calculating the building’s carbon footprint (SimaPro 2010, Athena 2011). The energy simulation

software eQUEST is used to calculate operational energy (eQUEST 2010), and CostLab is used

to estimate the service schedules (CostLab 2011). Excel is used to calculate the carbon footprint

metrics based on the data provided by the previous components (Excel 2007). The sampling

distributions are generated using the software ModelCenter, an MDO program that allows users

to bring commercial software tools into a common environment using software “wrappers” to

facilitate the application of automated design space exploration techniques (ModelCenter 2008).

The sampling algorithm chosen is an orthogonal array for 90% of the designs and a Latin

hypercube for 10% of the designs.

Case Study

A residential complex of four eight-story buildings located in a hot and humid climate

is used as a case study to illustrate the proposed environmental impact feedback method. The

buildings are of identical size, shape, orientation, and building materials. At the time this paper

was submitted, the initial design scheme for the buildings had been determined. In addition to the

proposed objective mentioned in the previous section, the case study thus provided an

opportunity to show how retrospective changes in design could reduce the environmental impact

of the buildings.

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The case study building has a total floor area of 50,468 m2, and the service life of the

building was assumed to be 30 years. The floor-to-floor height is 4.0 m. The building envelope

consists of a uniform cladding pattern and a translucent glazing material. The mechanical system

is a variable air volume forced air system with direct-expansion coils for cooling and a central

furnace for heating. Internal loads and the weekly operating schedule were determined for a

residential building using the 2009 ASHRAE Fundamentals (ASHRAE 2009).

Thirty-three design variables could be manipulated in the design problem, and the

scope of these variables included the building’s substructure, envelope, and interiors. Table 16

categorizes the variables into three groups: materials, thicknesses, and design variables, which

represent variables not related to the previous two categories. Appendix 3 lists all possible input

values for each variable. Carbon footprint was calculated in terms of CO2e as described in the

Methodology section. The following energy conversions were used to perform the analysis:

electricity impact = 0.664 kg CO2e/kWh and natural gas impact = 0.251 kg CO2e/kBtu (SimaPro

2010). Total square footage remained constant.

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Table 16 – Case study variables used to characterize building life cycle environmental impacts.

Variable Type

Design Material Thickness

Window-to-wall ratio Cladding Ceiling

Has fins? Roof Cladding

Has overhangs? Wall Floor finishes

Fin depth Partitions Floor insulation

Overhang depth Columns and beams Mat foundation

aBuilding shape Floor finishes Wall finishes

Number of buildings Floor insulation Glazing

Number of floors Floor structure

Orientation Piles

Substructure system Shading device

Wall finishes

Window frame aShape parameters defined as follows. Note “f” is dependent on “a” through “e”:

Results

Results are presented for each of the three sequential decision-making approaches

described in Figure 53. For each approach, a probability mass function is generated after each of

four building design decisions. A range of decisions is evaluated to illustrate the breadth of

design choices the method can accommodate, and many others can be substituted in their place.

The process of generating a new probability mass function is repeated after each new design

decision, although length constraints preclude showing distributions here for all 33 decisions.

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Figure 57 shows the probability mass function for the entire design space before any

decisions have been made. The size of the design space is equal to the product of the number of

design choices for each design decision, or 3.69x1023

. From this design space, a sampling

algorithm selected 8,689 designs over 118 hours and computed the environmental impacts

according to the process shown in Figure 56. The mean total impact of the selected designs was

18,237 kg CO2e/m2, and the standard deviation was 6,907 kg CO2e/m

2. The global minimum was

3,826 kg CO2e/m2, and the global maximum was 47,207 kg CO2e/m

2. The following three

sections present the results for each of the three decision-making approaches after the first four

decisions have been made.

Figure 57 – Probability mass function characterizing a design space size of 3.69x1023

, showing

total environmental impacts for 8,689 selected designs prior to any design decisions.

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Sequential decision-making approach I: minimization of total

environmental impact

Figure 58 presents the probability mass functions for the first four decisions for the

objective minimizing total environmental impact, and Table 17 presents relevant metrics for each

decision. The percentage of remaining designs is reduced considerably after the first decision,

since the designer has selected only four values for the orientation out of 72 possible values. This

decision has also reduced the mean considerably, showing that orthogonal orientations

significantly lower the total impact. The standard deviation is also much lower, as the

distribution shows that the remaining designs are clustered at the low end of the distribution.

Decisions two through four also result in distributions clustered near the low end. The global

minimum of 3,826 kg CO2e/m2 is maintained throughout each decision, and the global maximum

is steadily reduced to a value after the fourth decision that is 73% lower than the original global

maximum. The designer has achieved their objective by successfully choosing a set of decisions

that eliminates high impact designs and leads to a relatively high probability that the final design

will be in close proximity to the global minimum.

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Figure 58 – Probability impact distributions for four sequential decisions for the objective

minimizing total environmental impact.

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Table 17 – Metrics characterizing the design space for a design strategy minimizing

environmental impacts.

Objective: minimize total environmental impact

No Decisions Decision 1 Decision 2 Decision 3 Decision 4

Decision Orientation = 90°, 180°,

270°, or 360°

Cladding material

= concrete or

limestone

Wall material =

steel stud 16” o.c.

or steel stud 24”

o.c.

Number of

buildings = 3

Mean

(kg CO2e/m2)

18,237 7,663 7,669 7,857 7,797

Standard

deviation

(kg CO2e/m2)

6,907 2,015 1,991 1,964 2,292

Remaining

designs

(% of total)

100 6.2 1.8 0.86 0.45

Global

minimum

(kg CO2e/m2)

3,826 3,826 3,826 3,826 3,826

Global

maximum

(kg CO2e/m2)

47,207 14,848 12,906 12,748 12,748

Sequential decision-making approach II: achievement of carbon

performance value


objective that aims to achieve a carbon performance value, and Table 18 presents the relevant

metrics after each decision has been made. An arbitrary performance value of 15,000 kg

CO2e/m2 has been marked on each distribution with a dashed line. The designer following this

approach may have a competing objective and so should maintain as high a number of designs as

possible within as close proximity to the performance value as possible. The distributions and

values in Table 18 show that the mean stays within 38% of the performance value throughout the

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decision-making process. The percent of designs meeting the performance value and staying

within one standard deviation of this value is 32% or less after all the decisions. The fourth

decision results in the highest mean and standard deviation and lowest percent within one

standard deviation for the four decisions, and this may motivate a designer to backtrack and

revise the decision in order to reduce the mean and standard deviation, thereby increasing the

number of designs close to the performance value. Such a tactic highlights the ability of the

feedback method to accommodate decisions that are in flux throughout the conceptual design

stage.

Figure 59 – Impact distributions for first four decisions for the objective achieving a carbon

performance value.

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Table 18 – Metrics characterizing the design space for a design strategy achieving a carbon

performance value.

Objective: achieve environmental impact performance value


Decision Number of

floors = 7

Orientation =

130-210°

Glazing

thickness ≤

0.0064m

Shape

parameters

“C”, “E” ≤ 15m

Mean

(kg CO2e/m2)

18,237 18,445 18,682 18,358 20,590

Standard

deviation

(kg CO2e/m2)

6,907 6,901 6,685 6,854 8,021

Remaining

designs

(% of total)

100 24 5.9 2.1 0.73

% of designs

within 1 σ of

performance

value

30 30 30 32 27

Global

minimum

(kg CO2e/m2)

3,826 3,935 5,348 5,348 6,711

Global

maximum

(kg CO2e/m2)

47,207 47,114 44,254 44,254 44,254

Sequential decision-making approach III: maximization of design

freedom


objective that aims to maximize freedom and flexibility and preserve a large number of design

options. A designer in this case would like to maintain a high standard deviation and percentage

of remaining designs, in order to avail the most number of options in the design space after each

decision. As with the strategy that aims to achieve a performance value, this strategy may also

have a competing objective, such as preserving as many aesthetic differences in the building

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shape or façade materials. Table 19 presents the relevant metrics after each decision has been

made and shows that nearly ten times as many designs are still available to the designer after the

fourth decision than in either of the previous two decision-making strategies. Most importantly,

the standard deviation remains high throughout all the decisions. The mean, standard deviation,

and global minimum and maximum all stay within 3% of the mean, standard deviation, and

global values for the distribution generated before any decisions have been made. A designer

would have many options at this point from which to compare environmental impacts with other

objectives.

Figure 60 – Impact distributions for first four decisions for the objective maximizing design

freedom.

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Table 19 – Metrics characterizing the design space for a design strategy maximizing design

freedom.

Objective: maximize freedom, flexibility, and design options


Decision Has overhangs = true

Overhang depth

≤ 0.9144m

Wall finish =

vinyl

Number of

buildings = 3

Mean

(kg CO2e/m2)

18,237 18,253 18,100 18,145 17,801

Standard

deviation

(kg CO2e/m2)

6,907 7,000 7,009 7,111 7,037

Remaining

designs

(% of total)

100 50 24 12 6.8

Global

minimum

(kg CO2e/m2)

3,826 3,935 3,935 3,935 3,935

Global

maximum

(kg CO2e/m2)

47,207 47,132 47,093 46,894 46,894

Validation2

Validation of the method was conducted by determining whether enough data points had

been generated by the sampling algorithm. The simulation was run again with an identical

problem formulation but for a different sampling algorithm. The resulting distribution was then

combined with the original distribution to yield an aggregate distribution. Comparison of the

mean and standard deviation among the three distributions shows that enough data points had

been generated.

Figure 61 is the impact distribution generated using the same problem formulation as

2This section provides additional analysis not included in the submission to The International Journal of

Architectural Computing.

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described in the methodology. The only difference is that instead of using an orthogonal array

algorithm for 90% of the points and a Latin hypercube algorithm for 10% of the points, all points

were generated using a Latin hypercube sampling algorithm. Figure 62 is the aggregate impact

distribution when Figures 57 and 61 are combined. Table 20 shows that the mean of the Latin

hypercube distribution is within 2% of the mean of the distribution from the results section, and

the mean of the aggregate distribution is within 1%. The standard deviations are also within

10%. These metrics suggest that the impact distribution generated in the results section is

reasonably stable and valid.

Figure 61 – Distribution of life cycle environmental impacts for alternate Latin hypercube

sampling algorithm.

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Figure 62 – Combined distribution of life cycle environmental impacts for original and alternate

sampling algorithms.

Table 20 – Validation of impact distributions using alternate sampling method.

Sampling

method

Mean

(kg CO2e/m2)

Relative

Difference

Standard

deviation

# of designs Minimum impact

(kg CO2e/m2)

Maximum

impact

(kg CO2e/m2)

90% orthogonal

array+10% Latin

hypercube

18,237 -- 6,907 8,689 3,826 47,207

Latin hypercube 17,874 -1.99% 6,240 7,693 4,665 49,602

Combined 18,066 -0.94% 6,604 16,382 3,826 49,602

Conclusions

The application of MDO to conceptual building design is currently not well suited to

conventional architectural, engineering, and construction design practices. A method is needed

that accommodates the flexible, often-changing nature of sequential decision-making processes.

An environmental impact feedback methodology is proposed that relies on probabilistic

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distributions to describe the full range of control of environmental impacts for sequential

building design decisions during the conceptual design stage. The method allows designers to

easily make changes to a set of decisions after viewing the probabilistic impacts associated with

each decision. The inclusion of a range of variables, including shape, massing, materials, and

dimensions, increases the method’s flexibility and applicability to a broad range of building

designs.

Results of the case study show how the method can successfully accommodate several

sequential decision-making strategies, including the minimization of environmental impact,

achievement of a performance value, and maximization of design freedom. Four sample

decisions are provided for each design strategy, and inspection of the mean, standard deviation,

number of remaining designs, and other metrics after each decision shows the likelihood of each

strategy meeting its objective. In several examples, decisions were made that increased the

likelihood of meeting the objective. The feedback method also shows when a decision does not

help in achieving an objective, such as the case study’s restriction of the shape to a square layout

instead of an “H” pattern, which in turn yielded a distribution that increased the mean and

standard deviation and reduced the likelihood of meeting the performance value.

The scope of the method is limited to 33 design variables and a design space size of

3.69x1023

that includes the substructure, façade, and interiors, and each variable has a limited

number of options. The case study validates the method for one building size and type in one

climate. Future research will consider alternative building sizes and types as well as alternate

climates. Designers must rely on intuition and use trial and error in determining which design

choices improve or worsen the chances of meeting an objective. Future research will employ

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probability mass functions to show which variable values consistently aid in the achievement of

a particular objective and which values consistently lessen the chances of achieving an objective.

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Chapter 6: A multi-objective feedback approach to

evaluating sequential building design decisions1

Abstract

Conceptual design decision-making plays a critical role in determining life cycle environmental

impact and cost performance of buildings. Stakeholders often make these decisions without a

quantitative understanding of how a particular decision will impact future choices or a project’s

ultimate performance. A sequential decision support methodology is developed to provide

stakeholders with quantitative information on the relative influence design decisions have on a

project’s life cycle environmental impact and life cycle cost. A case study is presented showing how

the proposed methodology may be used by designers considering these performance criteria.

Sensitivity analysis is performed on thousands of computationally generated building alternatives.

Results are presented in the form of probabilistic distributions showing the degree to which each

decision helps in achieving a given performance criterion. The method provides environmental

impact and cost feedback throughout the sequential building design process, thereby guiding

designers in creating low-carbon, low-cost buildings at the conceptual design phase.

1This paper was co-authored with postdoctoral fellow Forest Flager and Assistant Professor Michael Lepech and

submitted to Automation in Construction in October 2013. This is the capstone chapter that builds on the

contributions developed in Chapters 2-5.

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Introduction

Multidisciplinary design optimization (MDO) methods exist that allow designers to

explore very large design spaces, quickly evaluate many design alternatives, and find optimal or

near optimal solutions for various performance criteria. The benefits of MDO methods are well

documented in such industries as aerospace, automotive, and electronics. Within the architecture,

engineering, and construction industry, application of MDO methods has been shown to yield

significant reductions in building life cycle environmental impact and cost compared to

conventional design methods (Flager et al. 2012, Wang et al. 2005b).

Although MDO has potential to improve design process efficiency and the quality of the

resulting product, MDO methods are not widely used within the building design industry,

particularly during conceptual design. The conceptual design stage has been recognized as a

critical determinant of project environmental impact and cost (Ellis et al. 2008, Schlueter and

Thesseling 2009). At the conceptual design stage, many choices exist for building decisions,

such as shape, orientation, massing, and materials for each building component. These decisions

are typically made by architects in sequential fashion, such that for example once the orientation

of a building is known, the placement of shading devices can be determined for each façade in

order to minimize cooling loads and life cycle costs. Designers may also wish to understand a

project’s environmental impact and cost once the wall assembly system has been chosen but

before deciding upon the cladding system. Such a multi-objective sequential feedback approach

is typical in the architecture, engineering, and construction industry in that project stakeholders

often need to evaluate design decision trade-offs for competing objectives. For example, a

designer wishing to minimize both environmental impact and cost may find a certain window-to-

wall ratio lowers carbon footprint at the expense of greatly increased life cycle cost.

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feedback on any single design choice. MDO methods do not integrate well with the architecture,

engineering, and construction industry, which relies on flexible and often-changing decision-

making processes, especially at the early stages.

A new method is proposed that integrates MDO methods with conceptual building design

in a way that provides life cycle environmental impact and life cycle cost feedback for sequential

decision-making processes. Building information modeling software is integrated with life cycle

assessment and energy simulation software, a sampling algorithm generates thousands of

building design alternatives across the design space, and life cycle environmental impact and

cost feedback is computed for each alternative. Probability mass functions are then used to

characterize the environmental impact and cost of decisions as they are made in sequential

fashion. Designers are provided with visual quantitative feedback on many alternatives and can

determine the degree to which each decision helps or hurts in achieving each of their objectives.

Figure 63 illustrates how the method can be applied to three different sequential decision-

making strategies often used by designers. Environmental impact is displayed here as the

feedback type, although distributions can be simultaneously provided for cost feedback as well.

In each scenario, probabilistic distributions show the range of impacts possible for all design

alternatives before any decisions have been made. Once a decision is made, new probabilistic

distributions are generated showing the range of impacts possible for the remaining design

decisions. Designers are able to understand the full range of control of life cycle environmental

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impact and cost performance as well as the relative influence of design decisions on both of these

objectives throughout the sequential decision-making process.

(a) (b) (c)

Figure 63 – Three sequential decision-making design strategies to which designers might

apply the multi-objective feedback method: (a) minimization of carbon footprint,

(b) achievement of an environmental impact performance target, and (c) maintenance of

design freedom.

In Figure 63(a), a designer would like to minimize a building design’s life cycle

environmental impact. This strategy relies on single-objective optimization, which studies have

shown can be an effective strategy for helping designers minimize the environmental impact of

buildings (Coley and Schukat 2002, Al-Homoud 1997, Wetter 2001). As each sequential

decision is made, the designer understands whether a decision improves upon the previous

decision in terms of either reducing or increasing the building’s remaining life cycle

environmental impacts. The designer also understands with each new decision whether chances

improve, worsen, or have been eliminated of achieving the design with the lowest possible

carbon footprint. Throughout the process the designer knows the full range of control for each

design decision as well as how each decision relates to the initial range of building impacts

before any decisions were made.

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A second sequential decision-making approach employed by designers is designing for a

specific environmental impact performance target. Andreu and Oreszczyn (2004) discuss how

this strategy can be effective in creating designs with low life cycle environmental impact. Such

a strategy caters to designers interested in building rating systems and assessment tools, such as

the Green Building Challenge and the United States Green Building Council’s Leadership in

Energy and Environmental Design program. Designers employing this strategy may have a

secondary objective which they would like to optimize. In Figure 63(b), a designer has an

environmental impact performance target in mind and may secondarily like to minimize life

cycle cost. As in the first scenario, probability mass functions are generated with each new

design decision for both environmental impact and cost. Designers can simultaneously visualize

the range of control for each objective as well as evaluate the degree to which each decision

helps in achieving both objectives.

A third sequential decision-making approach employed by designers is the maintenance

of flexibility and adaptability throughout the design process (ALwaer and Clements-Croome

2010). As with the previous strategy, this approach is particularly relevant for designers with

competing objectives in mind, such as optimizing designs for both environmental impact and

cost. In Figure 63(c), a designer wishes to preserve freedom and flexibility throughout the design

process by maximizing the number of remaining designs as each decision is made. The designer

has a cost objective in mind that competes with environmental impact and does not want to be

confined to a narrow subset of designs, regardless of whether they have low, medium, or high

environmental impacts. Such a strategy maximizes the trade-off options to be considered

between the competing objectives.

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The proposed decision support methodology is a quantitative approach that supplies

stakeholders with information about the relative influence that each design decision has on both

life cycle environmental impact and life cycle cost. The method is applied to a multi-objective

sequential decision-making strategy – achieving an environmental impact performance target and

minimizing cost – although the method can be adapted to other strategies. Designers quickly

assess which design variables are significant contributors to a building’s carbon footprint and

cost and which are less important. The use of probability mass functions allows designers to

predict with each successive decision the likelihood of achieving a given impact value, and

decisions may easily be adjusted in order to increase or decrease this likelihood. The method

provides visual understanding of the range of control of the entire design space’s environmental

impact and cost performance, and by accommodating various sequential design strategies, the

method enhances its utility as a conceptual design stage decision-making tool.

Related Studies

Research in Multidisciplinary Design Optimization (MDO) is used as a point of departure

in order to present the proposed research methodology. MDO involves the formalization of

design coordination and iteration for groups working on complex engineering systems such as

buildings and civil infrastructure. Computational optimization techniques are applied to

systemically search through a range of design options defined by the design team to find

solutions that best meet the objectives and constraints of project stakeholders. MDO methods

were first developed in the aerospace industry in the 1970’s and are now successfully used in a

number of fields including automotive, naval architecture and electronics design (AIAA 1991).

A number of studies have used MDO as a method for analyzing the trade-offs between

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life cycle environmental impact and cost feedback on conceptual building design alternatives

(Hauglustaine and Azar 2001, Caldas and Norford 2002, Wright et al. 2002, Geyer 2009). Wang

et al. (2005b) integrated building information modeling, life cycle assessment, energy analysis,

and MDO software in order to minimize two objective functions: life cycle environmental

impact and life cycle cost. Several building design parameters were included in the analysis,

including shape, orientation, and building materials. A multi-objective genetic algorithm was

used to identify Pareto optimal solutions, or those solutions not dominated by any others in the

decision variable space for both life cycle carbon footprint and cost. Al-Homoud (1997) applied

a direct search optimization technique in order to minimize the annual energy consumption of an

office building for different climates. The method provided optimized thermal performance

feedback on several hundred design alternatives, and variables included building orientation and

thermal properties of glazing materials. Coley and Schukat (2002) integrated building

information modeling and thermal analysis in developing a method for optimizing the energy

performance of a community hall. The method applied a genetic algorithm during the conceptual

design phase, and the results displayed a range of architecturally distinct designs minimized for

operational energy.

Prior MDO research has several limitations as far as its ability to integrate with

architecture, engineering, and construction industry design practices. MDO has limited ability to

provide useful feedback on sequential design decisions. MDO feedback typically relies on Pareto

fronts for evaluating the tradeoffs between objectives. Such diagrams provide feedback only

when designs are fully articulated. They offer no performance evaluation on partially defined

designs and fail to show the sensitivities of performance criteria to changes in design variables.

The feedback does not align well with a design process in which decisions are made in sequential

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fashion and life cycle environmental impacts and costs are in flux with each successive design

change. MDO feedback has also often been limited to the operational phase of a building rather

than its entire life cycle, and design variables are usually concentrated in the building envelope.

Embodied impacts of building components are not always included, and variables relating to

building systems’ materials and dimensions, particularly structural assemblies, walls, floors, and

finishes are often excluded.

The proposed method fills these gaps by providing life cycle environmental impact and

cost feedback on a comprehensive set of design variables throughout the envelope, substructure,

interiors, and structural components. A design space consisting of a very large number of design

alternatives is evaluated by the method. A sampling algorithm generates probability mass

functions, which dynamically show designers how the range of control over environmental

impacts and costs changes with each sequential decision. The distributions also show the

sensitivity of impacts to changes to these variables. Embodied and operational impacts are

included in the scope, as well as a range of shape, massing, building material, and dimensioning

parameters. The method accommodates a range of decision-making strategies, since in all cases

designers understand the likelihood of achieving a certain carbon or cost footprint after each new

design decision.

Methodology

Scope

The goal of the proposed methodology is to provide multi-objective feedback to

designers at the conceptual design stage in such a way that designers can understand the life

161

cycle environmental impact and cost implications of design alternatives for each sequential

decision. To illustrate the potential to provide this feedback across a large number of building

systems, the building’s substructure, façade, interior, and service equipment are included in the

analysis.

The Uniformat 2010 classification system is used in the AEC industry to classify building

components within building element categories (Construction Specifications Institute 2010).

Uniformat elements within the project scope are: Substructure (A), Shell (B), Interiors (C), and

Services (D). The remaining elements (Equipment and Furnishings (E), Special Construction and

Demolition (F), and Sitework (G)) are not considered, since these decisions relate to interior

aesthetics, require specialized knowledge of site conditions, or otherwise involve decisions that

would be impractical to make by designers before the design development stage. Further detail

on the classification framework is found at Basbagill et al. (2013). Appendix 4 enumerates the

material choices and their properties considered for each building component. These properties

include material densities and embodied CO2e factors, or the amount of carbon dioxide

equivalents associated with materials’ feedstock energy, energy required to process the materials

into building components, and fuel cycle energy for all pre-operational processes.








162

Figure 64 – Building life cycle phases included in proposed method for providing multi-

objective feedback on sequential design decisions.





proposed method is demonstrated for global warming potential. The metric used for this

indicator is carbon dioxide equivalents (CO2e), which measures the total amount of greenhouse

gas emissions of the building.

Analysis process

The general steps involved in the proposed method for providing multi-objective

feedback on sequential design decisions are shown in Figure 65. The proposed methodology

integrates several building design, energy analysis, life cycle assessment, and life cycle cost

software packages. The arrows in the figure represent data dependencies between process steps.

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Figure 65 – Automated method for providing life cycle environmental impact and life cycle cost

feedback on sequential building design decisions.

The analysis process begins with an initial seed design manually inputted into the

building information model (DProfiler 2012). This may represent a design team’s proposed

solution to which they would like to see how alternatives compare, or the initial design may be a

random configuration. The building information model describes the building’s geometry,

materials, and components as well as the project’s geographic position and orientation. The

constraints are necessary for determining the operational environmental impacts and costs.

Assumptions are automatically programmed into the model but may be modified by the designer.

Figure 66 presents images from the building information modeling software. Each image

represents a unique design configuration for a set of input parameters including percentage of

glazing, building orientation, building shape, number of floors, and presence of fins or

overhangs.

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showing variations in several input parameters.

The embodied carbon footprint is calculated based on building component material

quantities extracted from the building information model. Each quantity is multiplied by a unit

impact (kg CO2e) to determine the carbon footprint. Appendix 4 provides sample formulas for

material quantities associated with each of the four building elements. Further details on how

these material quantities were derived can be found at Basbagill et al. (2013).






Pre-operational costs are calculated using formulas derived from RSMeans (RSMeans

2007). Many of these costs are based on the building’s gross floor area. Pre-operational costs not

dependent solely on gross floor area are calculated from variables defined by designers. Default

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values are provided for those variable values not defined by designers. Sample cost formulas for

the four building elements considered in the scope of the research are given in Appendix 4.



associated with the production of materials in this schedule contribute to the building’s embodied

impact. The maintenance, repair, and replacement schedule is determined by the gross floor area,

building type, location, and structural and mechanical details defined in the building information

model, which are entered into an online facility operations reference database (CostLab 2011).

Operational carbon footprint and cost calculations have two components. The first

depends on the building’s electricity and natural gas consumption as calculated by the energy

simulation model. These quantities are multiplied by a unit impact and a unit cost to calculate

carbon footprint and cost, respectively. The second component is associated with the

maintenance, repair, and replacement of the service equipment. The cost data is obtained directly

from the maintenance, repair, and replacement schedule. The carbon impact of the mechanical,

electrical, and plumbing equipment is determined by looking up a typical material, material

quantity, and cost for each service component using equipment supplier documentation. Each

quantity is then multiplied by a unit impact in a similar fashion to the pre-operational impact

calculations.

Life cycle environmental impact and cost are then calculated by summing the pre-

operational and operational CO2e and cost totals. The life cycle cost is calculated as a net present

value assuming a specified discount rate and escalation rate for electricity and natural gas prices.

The pre-operational costs are incurred at year zero and the operational costs are accounted for on

an annual basis over the service life of the facility.

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A sampling algorithm is then applied to automatically iterate the life cycle carbon and

cost analyses described above across a defined range of design variables. The method generates

thousands of design alternatives, and feedback on these alternatives is inspected as described in

the next section.

Inspection of results

The goal of the proposed method for providing life cycle environmental impact and cost

feedback is to allow designers continuous visualization of the environmental impact and cost

performance of their design choices for sequential decision-making strategies. A sampling

algorithm is used to characterize the life cycle impacts of sequential design decisions across a

broad range of design variables. Two sets of probability mass functions – one set for life cycle

environmental impact and one set for life cycle cost – are constructed from each of the thousands

of designs analyzed. Inspection of these distributions aids designers in achieving their particular

objective, whether to minimize both carbon footprint and cost, attain a performance target, or

maximize design freedom. The method accommodates multiple objectives, as a designer

interested in meeting an environmental impact performance target and minimizing cost can test

out different options for a single design variable and see how the resulting distributions improve

upon the full range of carbon and cost impacts. The designer can then choose the value that

preserves the most number of desirable designs for both objectives or, if no value yields a

preferred distribution, the designer can backtrack, modify a prior decision, and consider the new

options.

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Software implementation


Figure 65. DProfiler is used for the building information modeling software (DProfiler 2012).

SimaPro and the Athena EcoCalculator are used for environmental impact data and for

calculating the building’s carbon footprint (SimaPro 2010, Athena 2011). RSMeans is used to

calculate building life cycle cost (RSMeans 2007). The energy simulation software eQUEST is

used to calculate operational energy (eQUEST 2010), and CostLab is used to estimate the service

schedules (CostLab 2011). Excel is used to calculate the carbon footprint metrics based on the

data provided by the previous components (Excel 2007). The sampling distributions are

generated using the software ModelCenter, an MDO program that allows users to bring

commercial software tools into a common environment using software “wrappers” to facilitate

the application of automated design space exploration techniques (ModelCenter 2008). The

sampling algorithm chosen is an orthogonal array for 90% of the designs and a Latin hypercube

for 10% of the designs.

Case Study

A residential complex of four eight-story buildings located in a hot and humid climate is

used as a case study to illustrate the proposed method. The buildings are of identical size, shape,

orientation, and building materials. Table 21 lists the building information model inputs in terms

of required inputs and variables, and Table 22 lists the assumptions. Variables not defined by the

designer are selected from the values given in Appendix 4. The case study building has a total

floor area of 50,468 m2.

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Table 21 – Required inputs and variables for automated life cycle environmental impact

and life cycle cost feedback method. Required Inputs

Location

Building type

Gross floor area

Variables

Design Material Thickness

Number of buildings Cladding Ceiling

Number of floors Roof Cladding aBuilding shape Partitions

Window-to-wall ratio Columns and Beams

Has fins?

Has overhangs?

Floor finishes

Floor insulation

Fin depth Floor structure

Overhang depth Piles

Orientation Shading device

Substructure system Wall finishes

Glazing material aShape parameters defined as follows, with “f” dependent on “a” through “e”:

Table 22 – Assumptions for automated life cycle environmental impact and life cycle cost

feedback method.

Assumptions Value

Footing depth (m) 2.0

Bay spacing (m) 9.0

Floor-to-floor height (m) 4.0

Service life (years) 30

Discount rate

Electricity

5%

Cost (USD/kWh) a0.20

Impact (kg CO2e/kWh) b0.66

Escalation rate 3.0%

Natural gas

Cost (USD/kBtu) a0.03

Impact (kg CO2e/kWh) b0.25

Escalation rate 3.0% aRSMeans (2007)

bSimaPro (2010)

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Twenty-seven design variables are manipulated in the design problem, and the scope of

the variables includes the building’s substructure, envelope, and interiors. Table 21 categorizes

the variables into three groups: materials, thicknesses, and design variables, which represent

variables not related to the previous two categories. The building envelope consists of a uniform

cladding pattern and a translucent glazing material. The mechanical system is a variable air

volume forced air system with direct-expansion coils for cooling and a central furnace for

heating. Internal loads and the weekly operating schedule are determined for a residential

building using the 2009 ASHRAE Fundamentals (ASHRAE 2009). Cost is calculated in terms of

US dollars, and carbon footprint is calculated in terms of CO2e as described in the “Analysis

process” section of the Methodology.

Results

Results are presented in the form of probability mass functions for a designer interested in

multiple objectives: minimizing cost while also attaining an environmental impact performance

target. These two objectives correspond to the schematics shown in Figures 63(a) and 63(b),

respectively, and the probability mass functions for both objectives are presented together in the

same figure. Such side-by-side presentation of the results allows designers to easily compare the

two objectives, determine whether undesired trade-offs exist for any design decisions, then either

make a new decision or revise a previous decision. For example, a decision may have the

undesired effect of lowering a building’s mean environmental impact at the expense of

increasing the mean cost. A designer can easily visualize this effect by comparing the two

objectives’ probability mass functions when placed next to each other as opposed to in separate

figures. Designers view these sets of distributions as a sequence: one set before making the

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decision and the next set after the decision has been made. In this way, a designer receives

sequential feedback one design decision at a time for multiple objectives and can visually

determine whether each decision helps attain one, both, or neither of their objectives.

Probability mass functions are generated here for both objectives after each of four

sample building design decisions. A range of decisions is evaluated to illustrate the breadth of

design choices the method can accommodate, and many others can be substituted in their place.

In addition, one of these decisions is revised to show the ability of the method to improve upon

decisions that do not help attain both objectives. The process of generating new probability mass

functions is repeated after each design decision; length constraints preclude showing

distributions here for all 27 sequential decisions.

Figure 67 shows the probability mass functions for the entire design space for

environmental impact and cost before any decisions have been made. The size of the design

space is equal to the product of the number of choices for each variable, or 6.07x1016

. From this

design space, the sampling algorithm selected 7,623 designs and computed the environmental

impacts and costs over 118 hours according to the automated process shown in Figure 65. The

mean life cycle environmental impact of the selected designs is 20,981 kg CO2e per m2, and the

mean life cycle cost is $1,481 per m2. An environmental impact performance target of 13,000 kg

CO2e/m2 is marked with a dashed line. This value is arbitrarily chosen to represent a value that a

designer wishes to attain, perhaps as prescribed by a green building rating system or a design

firm’s sustainability benchmarks. A designer with the two stated objectives in mind –

minimization of cost and attainment of an environmental impact target – would try to achieve as

low a mean cost as possible while simultaneously maintaining as many designs as possible close

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to the target value of 13,000 kg CO2e/m2. Additional relevant metrics for the two distributions

are presented in the first column of Table 23.

Figure 67 – Distribution of building life cycle environmental impacts and life cycle costs for a

design space size of 6.07x1016

.

Figures 68-72 show the resulting probability mass distributions after each of the four

decisions have been made, and Table 3 presents relevant metrics for each of the distributions.

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The first decision sets the number of buildings equal to 3, and Figure 68 shows the

resulting probability mass distributions. Figure 68 and the metrics for this decision in Table 23

show that this decision filters out low-performing designs for the environmental impact objective

and high-performing designs for the cost objective. The percent of designs whose environmental

impact is within one standard deviation of the performance target increases slightly by 1%, and

the mean cost impact increases by 3%. The decision therefore shows a trade-off between

lowering carbon impact at an increased cost. A designer may accept the result if they favor the

environmental impact objective more than the cost objective, or if not a designer may revise the

decision.

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Figure 68 – Distribution of life cycle environmental impacts and life cycle costs after decision 1:

number of buildings equals 3.

Assuming the first decision remains unchanged, the second hypothetical decision is low-e

glazing. Figure 69 shows the probability mass functions for life cycle environmental impact and

cost, and Table 23 shows relevant metrics after this decision. The decision has little effect from

the previous decision, as the means for both objectives decrease by less than 1%, the percent of

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designs within one standard deviation from the performance target remains unchanged, and the

extreme values remain for both distributions. Still, the decision promotes both objectives, and a

designer would likely retain the decision after receiving this feedback.


low-e glazing.

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Figure 70 shows the environmental impact and cost distributions and Table 23 shows the

relevant metrics after the third decision, window-to-wall ratio equal to 50. The decision filters

out high-performing designs and increases the mean environmental impact of remaining designs

by 30% and cost by 10%. The percent of designs falling within one standard deviation of the

environmental impact target also decreases from 27% to 4.2%. The decision clearly does not

help a designer achieve either of the two objectives, and the decision would likely be revised.

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window-to-wall ratio equals 50.

Figure 71 and Table 23 show the effect of modifying the third decision’s window-to-wall

ratio value from 50 to 15. The decision is highly favorable to both objectives, as the mean

environmental impact is reduced by 43% from the second decision, and the mean cost is reduced

by 15%. The worst-performing designs for both objectives have carbon impacts and costs that

are 56% and 32% lower, respectively, than the worst-performing designs after the second

decision. The method can easily accommodate changes like this that better satisfy multiple

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objectives, and a designer would likely retain this decision over the previous window-to-wall

ratio value.

Figure 71 – Distribution of life cycle environmental impacts and life cycle costs after decision 3

(revised): window-to-wall ratio equals 15.

The final decision limits the orientation to values between 0° and 180° (inclusive), as

shown in Figure 72 and Table 23. This decision shows the ability of the method to accommodate

multiple discrete choices for a single decision, thereby expanding its utility to designers who

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may be able to filter out some values but not all. The decision promotes both objectives, as the

mean environmental impact is reduced by 4% and the percent of designs within one standard

deviation of the environmental impact performance target remains at 32%. The mean cost is

reduced slightly by 0.4%, and a designer with the stated objectives in mind would likely accept

the results and continue on to the next decision. The process would continue for the remaining 23

decisions until a final design is reached.

At this point in the design process the results can also be compared with the full range of

impacts possible prior to any design decisions. The mean life cycle environmental impact has

decreased by 46%, the percent of designs within one standard deviation of the environmental

impact performance target has increased by 6%, and the mean life cycle cost has decreased by

12%.

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orientation from 0° to 180°.

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Table 23: Metrics characterizing the design space for two design strategies: (a) achieving a life

cycle environmental impact performance value and (b) minimizing life cycle cost.

Objectives: (a) achieve life cycle environmental impact performance value

(b) minimize life cycle cost

No Decisions Decision 1 Decision 2 Decision 3 Decision 3 (revised) Decision 4

Decision # buildings = 3 Glazing = low-e WWR = 50 WWR = 15 Orientation = 0° to

180°

Objective (a) (b) (a) (b) (a) (b) (a) (b) (a) (b) (a) (b)

aMean 20,981 1,481 20,958 1,530 20,896 1,528 27,124 1,689 11,842 1,302 11,426 1,297

aStandard

deviation

7,517 180 7,558 195 7,592 196 7,570 191 3,208 92 3,112 83

Remaining

designs

(% of total)

n/a n/a 50 50 25 25 4.9 4.9 1.5 1.5 0.85 0.85

b% of

designs

within 1 σ of

target

26 n/a 27 n/a 27 n/a 4.2 n/a 32 n/a 32 n/a

aGlobal

minimum

5,042 1,027 5,178 1,084 5,258 1,084 7,467 1,134 5,258 1,084 5,258 1,084

aGlobal

maximum

50,692 2,328 50,351 2,328 50,351 2,328 50,351 2,328 22,100 1,579 21,127 1,505

aUnits for (a) are kg CO2e/m

2, and units for (b) are USD/m

2.

bThis metric is relevant only to the strategy achieving an environmental impact performance target and so is not

calculated for the strategy minimizing life cycle cost.

Conclusions

The application of multi-disciplinary design optimization to conceptual building design is

currently not well suited to conventional architectural, engineering, and construction design

practices. A method is needed that accommodates the flexible, often-changing nature of

sequential decision-making processes. A multi-objective feedback methodology is proposed that

relies on probabilistic distributions to describe the full range of control of impacts for sequential

building design decisions during the conceptual design stage. The method allows designers to

easily make changes to a set of decisions after viewing the probabilistic impacts associated with

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each decision. The inclusion of a range of variables, including shape, massing, materials, and

dimensions, broadens the method’s applicability to a variety of building typologies.

Results of the case study show how the automated sequential feedback method can

successfully accommodate multiple objectives, in this case minimization of life cycle cost and

achievement of a life cycle environmental impact performance target. Four sample decisions are

provided, and visual inspection of the resulting probability mass functions shows the degree to

which each decision helps promote the objective. The results show that high-performing designs

are retained for many of the scenarios. When the number of buildings is set to three, a trade-off

arises between the two objectives, as the mean environmental impact decreases but the mean cost

increases. This trade-off may be due for example to a material’s higher unit cost and lower unit

embodied environmental impact than another material; in this way, the method can help

illuminate complex carbon versus cost trade-offs. For decisions involving such a trade-off, a

designer would weight their objectives, determine whether the net result is beneficial, then either

maintain or revise the decision. The case study results also show that high-performing designs

for both objectives are filtered out when the window-to-wall ratio equals 50, and a designer

would likely revise the decision. On the other hand, a window-to-wall ratio value of 15 greatly

improves both objectives. In this way, the method is flexible enough to easily accommodate

changes to design decisions that may better promote a designer’s objectives.

The scope of the method is limited to 27 design variables in the substructure, façade,

and interiors, and each variable has a limited number of options. The case study demonstrates the

method for one building size and type in one climate. Future research will consider alternative

building sizes and types as well as alternate climates. Designers must rely on intuition and use

trial and error in determining which design choices improve or worsen the chances of meeting

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each objective. Future research will develop a method that more systematically evaluates the

trade-offs between the objectives, so that designers can make choices that yield results optimal to

both objectives. Further development of an integrated approach to evaluating multiple objectives

in this way during the conceptual design process can help designers analyze very large design

spaces with less effort leading to the creation of low-carbon, low-cost buildings.

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Chapter 7: Conclusions

This chapter summarizes the conclusions from chapters 3 through 6 as well as the

primary contributions to theory. Answers to the research questions presented in Chapter 1 are

summarized. The chapter ends with a discussion on the challenges and recommendations related

to the research as well as limitations and possible avenues for future work.

Summary of Conclusions from Chapters 3 through 6

Chapter 3 presented an automated method of integrating life cycle assessment with

conceptual building design in a way that reduces embodied impacts. The method integrates BIM,

LCA, energy simulation, and MRR scheduling software with a feedback processor. Very few

inputs are required to generate many design alternatives. Sensitivity analysis was performed

using a case study and showed which building components consistently contribute to a building’s

embodied impact. Embodied impacts can potentially be large in the substructure, shell, or

interiors but not the service equipment. Cladding material and cladding thickness are consistently

large contributors to embodied impact. Therefore, a designer should focus on these decisions that

can potentially achieve a large embodied impact reduction and defer less significant decisions to

later design stages.

Chapter 4 builds on the work presented in Chapter 3 by including operational impacts in

the scope of the analysis. The method evaluates embodied versus operational environmental

impact trade-offs of conceptual building design decisions. Designers can evaluate these trade-

offs over a range of design alternatives for a specified set of design variables. The method also

shows whether a design strategy that minimizes only embodied or operational impact can serve

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as a proxy for minimizing total impact. Design efforts can then focus on minimizing only this

impact. Results of the method applied to a case study show that a design strategy minimizing

only operational impact consistently yields high-performing designs. This strategy yields high-

performing design values for window-to-wall ratio, glazing thickness, and presence of shading

devices. A strategy minimizing only embodied impacts does not consistently yield high-

performing designs. The method therefore allows designers to understand whether minimizing

embodied and/or operational impacts can guide their decision making for variable choices and

yield reasonable approximations of the minimized total impact.

Chapter 5 presented a method for providing life cycle environmental impact feedback on

sequential building design decisions. The method accommodates the flexible, often-changing

nature of sequential decision-making processes in the AEC industry. The method relies on

probability mass distributions to describe the full range of control of environmental impacts for

sequential building designs during the conceptual design stage. Designers can easily make

changes to a set of decisions after viewing the probabilistic impacts associated with each

decision. Results of a case study show how the method can accommodate a range of sequential

decision-making strategies, including minimizing environmental impact, achieving a

performance target value, and maximizing design freedom. Four sample decisions were made,

and the results show the degree to which each decision helps or does not help achieve a given

objective. In this way, designers can understand how likely a given decision will help them

successfully execute a sequential decision-making strategy.

Chapter 6 extends the work presented in Chapter 5 by including other objectives in the

analysis besides life cycle environmental impact. The method presented provides multi-objective

feedback when evaluating sequential building design decisions. Probability mass distributions

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are used again to provide probabilistic feedback on multiple objectives for a range of sequential

decision-making design strategies. Changes to decisions can easily be made after designers view

these sets of distributions. A case study applied the method to two strategies, minimization of life

cycle cost and achievement of a life cycle environmental impact performance target value,

although the method is flexible enough to accommodate additional strategies. Results of the case

study show the degree to which four sample decisions help achieve both strategies. Trade-offs

are highlighted showing when a decision helps achieve one strategy but not the other strategy.

Results also show when a decision filters out high-performing designs for both strategies. In this

way, the method gives designers the flexibility and ease to make changes to their design choices

in a way that better promotes both objectives.

Contributions

A methodology is presented that integrates life cycle assessment and conceptual building

design. The methodology provides automated environmental impact and cost feedback for many

design alternatives by integrating building information modeling, life cycle assessment, life cycle

cost, and energy simulation software with a feedback processor. Multi-disciplinary design

optimization (MDO) is a primary point of departure for the research, and part of the

methodology integrates automated feedback with sequential design decision-making processes.

The method allows designers to understand the full range of control they have in the detailed

decisions ahead over life cycle environmental impacts and life cycle costs for a given set of

inputs. Designers also understand which design decisions are significant contributors to these

impacts and which are less important. Various algorithms are used to iterate over the

computational method and generate the design alternatives, and these include a genetic algorithm

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and a sampling algorithm. The method requires as few inputs as possible, which makes the

method appropriate for use during the critical early stages of the design process.

Three theoretical contributions are developed in the research, and each is discussed in the

following sections.

Embodied impact heuristics

A set of heuristics is developed that is used to calculate the pre-operational embodied

impacts of a full range of building components given as few inputs as possible. The required

inputs are gross floor area, building location, and building type. The heuristics also include

several assumptions and variables, the ranges of which may or may not be known by a designer.

The heuristics are integrated within the computational methodology so that embodied impacts

can be quickly calculated for many building designs. Optimization algorithms can then be

applied to minimize impacts or costs, and sampling algorithms can be applied to allow designers

to understand the full range of impacts.

The heuristics depart from existing methods for calculating embodied impacts in that

only three inputs are required. Athena EcoCalculator has been used by other studies to calculate

embodied impacts of building components within a computational framework, and Athena

requires ten inputs. Therefore, the heuristics require less information than Athena, and they may

therefore be used even earlier in the conceptual design stage. This allows designers to receive

environmental impact feedback at a more critical point in the design process in terms of ability to

determine life cycle impact. Designers can therefore be more informed of their decisions earlier,

and the hope is that this will lead to a final design with lower life cycle environmental impacts

than had the method not been used.

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Chapter 2 introduces the embodied impact heuristics, and Chapter 3 provides detail on

their development and gives some examples. Chapter 3 then integrates the heuristics within the

computational methodology for providing environmental impact feedback. The heuristics are

applied to show which building components are consistently significant contributors to a

building’s embodied impact and which are less important. Chapter 4 applies the heuristics in a

way that shows the embodied versus operational impact tradeoffs of various design decisions.

Designers can understand the relative importance of these decisions’ embodied and operational

impacts on a building’s total impact. Designers also understand whether an optimization strategy

that focuses on minimizing only embodied impact and/or a strategy that minimizes only

operational impact can approximate a strategy that minimizes embodied and operational impacts

together. In this way, designers can understand which decisions to focus on during the

conceptual design phase and which information can be deferred to later design stages. Chapter 5

integrates the heuristics within a sequential decision-making process typically used by designers.

Contributions of this integration process are described in the next two sections.

Chapter 6 uses the embodied impact heuristics as a basis for coming up with a new set of

heuristics for calculating the pre-operational costs of a comprehensive set of building

components. As with the embodied impact heuristics, only three inputs are required, and the

heuristics are integrated within the computational feedback framework. When applied to

sequential decision-making processes, the heuristics inform designers of the degree to which

each design decision achieves cost-effective buildings. The environmental impact and cost

feedback can be used in parallel to achieve high-performing designs for multiple objectives.

The heuristics are validated in Chapter 2. Life cycle assessment data were obtained on

eight buildings of varying sizes in the Arup Project Embodied Carbon Database (Arup 2013).

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These eight LCAs are highly detailed and were performed after most of the design details of each

building had been determined. The LCAs therefore required considerable effort and detailed

information. ADAPT also performed LCAs on the same eight buildings but required only one

input, building size, as well as ranges for each variable to calculate life cycle embodied impact.

The embodied impact values for the eight detailed LCAs were then compared with distributions

of impact values generated using ADAPT. The impacts for the two methods are consistently

close – within one standard deviation in seven cases, and less than 1% away from one standard

deviation in the eighth case – with values for large buildings generally much closer to the

detailed LCA values. ADAPT therefore captures reasonably well embodied impact values of

highly detailed LCAs for small, medium, and large buildings. These observations suggest that

the embodied impact heuristics are validated for a range of building sizes, at least to the level of

precision that can be expected in conceptual design.

In summary, Chapters 2 and 3 develop the embodied impact heuristics, Chapter 2

presents validation of the heuristics, and Chapters 3 through 6 show how the heuristics can be

used for a number of useful applications relating to the creation of low-carbon, low-cost

buildings.

Integration of automated feedback and sequential decisions

A second contribution to theory is a new method for integrating automated feedback and

sequential design decisions. One of the shortcomings of multi-disciplinary design optimization is

that parameters and decisions must be declared before any feedback is received. Chapter 5

describes a reconfiguration of the design process such that automated feedback is provided

sequentially after every single design decision. This feedback can come in the form of life cycle

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environmental impact (Chapter 5), life cycle cost (Chapter 6), or other building performance

criteria. The use of probability mass functions allows designers to visually see the probabilistic

effects of each design decision relative to the full range of impacts possible before any decisions

have been made. In this way, designers can easily tailor their decisions to their precise design

strategy, be this minimization of a performance criterion, achievement of a performance target,

or maximization of design freedom. Designers can understand whether each decision they make

helps or hurts in achieving the goal of their strategy, and designers can easily backtrack and

modify design decisions so that they are more likely to achieve their objective.

Chapter 5 validates this method for integrating automated feedback with sequential

design decisions by examining a single building case study. The validation approach was to

determine whether enough data points had been generated by the sampling algorithm. The

method was then applied to an identical problem formulation but using a different sampling

algorithm. The resulting distributions – one set of distributions for the first sampling algorithm

and another set of distributions for the second sampling algorithm – were then combined to yield

an aggregate distribution. Comparison of the mean and standard deviation of the two

distributions in isolation as well as the aggregate distribution showed that the three means were

all within 2% and the standard deviations were within 10%. Thus the distributions were

reasonably stable. This comparison suggests that enough data points have been generated and

that the method for integrating LCA feedback with sequential designs is validated.

The method departs from existing methods for integrating MDO with design in that

MDO is not currently configured to accommodate sequential design processes. The contribution

is a novel way of making sequential design decisions based on automated feedback for a range of

building performance objectives.

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Range of control of building performance alternatives

A third contribution is the ability for designers to visualize the full range of control of

values for a given building performance objective. The contribution is the plot showing the range

of performance values possible versus the likelihood of achieving a particular performance value

for a given problem formulation. Each performance value is plotted on the x-axis, and the

probability of achieving each value is plotted on the y-axis. Possible performance objectives

include life cycle environmental impact, life cycle cost, and schedule performance, although the

method may accommodate other building performance objectives. The contribution builds off of

the previous two contributions, in that the embodied impact heuristics are required to calculate

the embodied impacts of building components. Automated feedback presented in sequential

fashion then allows designers to understand how each decision relates to the full range of control

of all possible building performance alternatives. Only three inputs are required to generate the

full range of control of values, making the contribution highly applicable during the conceptual

design stage. By knowing very limited information, a designer can understand the minimum and

maximum possible impacts, the mean and standard deviation, whether a given design decision is

favorable to achieving a designer’s objective, and how the decision compares to the full scope of

impact values before any decisions have been made. Chapter 5 presents the automated feedback

in the form of life cycle environmental impact, and chapter 6 presents multi-objective feedback:

life cycle environmental impact and life cycle cost.

The method departs from existing methods for understanding the full range of control of

values for a given building performance objective, in that the method is applicable specifically at

the conceptual design stage and requires only three inputs. No other methods are known that

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provide feedback on the full range of control of life cycle environmental impact and life cycle

cost feedback values for so few inputs.

Answers to Research Questions

Five research questions were proposed in Chapter 1, and a summary of the answers

follows in this section.

The first question asked how many design inputs are required for a method that integrates

MDO into sequential building design? Chapters 2 and 3 discussed development of several

embodied impact heuristics and demonstrated how the number of required inputs can be reduced

to three. These heuristics can then be used in a number of applications that provide building

performance feedback on design decisions during the conceptual design phase. One of these

applications is a method that integrates automated feedback into sequential building design

processes.

The second research question asked which design decisions contribute most significantly

to embodied impacts of buildings? Chapter 3 applied the embodied impact heuristics to a given

problem formulation in order to answer the questions. A ranked set of material and dimensioning

decisions was generated for a range of building components showing which decisions can achieve

significant embodied impact reductions and which are less important.

The third research question asked how a design strategy minimizing only operational

impact compares with a strategy minimizing both operational and embodied impacts? Chapter 4

looked at various optimization strategies in order to understand the relative importance of

embodied and operational impacts for a range of decisions exhibiting a tradeoff between

embodied and operational impacts. In nearly all cases, it was found that a design strategy that

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minimizes only operational impact compares favorably with and can approximate well a design

strategy that minimizes embodied and operational impacts together. Sensitivity analysis was also

carried out on an alternate cladding material, location, and building size, and results in these

scenarios were consistent with the original analyses.

The fourth research question asked how well can a method that leverages automated

feedback be used to support sequential building design decision-making processes? Chapter 5

presented a method for integrating automated feedback into sequential design decision-making

processes. The feedback is provided after every decision for a range of design strategies,

including minimization of a building performance objective, achievement of a performance value,

and maximization of design freedom. A sample set of decisions was made for each strategy

showing how the method can be successfully used to provide feedback on each decision in such a

way that a designer understands how much each decision helps in achieving their design

objective.

The fifth research question asked about the range of control for a set of building design

parameters in terms of life cycle environmental impact and life cycle cost performance, and about

how designers make decisions within this range. Chapter 5 generated impact distributions for a

given problem formulation showing the full range of control of life cycle environmental impact

performance values for a very large design space. Chapter 6 generated impact distributions for

both life cycle environmental impact and life cycle cost. Designers are able to see the probability

of achieving any one particular value along these distributions. When integrated with sequential

design decision-making processes, this range of control serves as a point of reference allowing

designers to understand how each decision they make compares with the full range of possible

impact values for each objective before any decisions have been made.

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Challenges and Recommendations

This section presents general challenges and recommendations which may be useful for

researchers who wish to extend the work presented here. The first set of challenges relates to use

of the software. New software versions may be incompatible with software that was compatible

with older versions. It is suggested that new versions be tested on a secondary machine to ensure

compatibility before installing on a primary machine. It is also important to be aware that

software licenses may have an expiration date. A suggestion is to avoid running models in close

proximity to these dates. Another point is that integrating multiple software platforms inevitably

means working with software developers at different firms, and the developers may not be

familiar with all software platforms. A suggestion is to plan group meetings with all developers

to ensure everyone understands the functionality and limitations of each software platform. A

final suggestion relating to the software is to clarify the functionality of “wrapper” inputs and

outputs with software developers. This will help understand precisely how building information

modeling inputs affect energy analysis outputs.

A second set of challenges relates to data sources. A suggestion is to determine early on

in the project the sources from which data will be obtained. This allows understanding of the

scope and limitations of the project. Another suggestion is to determine early on the feasibility of

integrating the data into a computational framework. If the data are embedded in software, a

suggestion is to discuss with software developers early on the feasibility of integrating the

software into the framework using automated integration techniques. If automated integration is

infeasible, researchers should determine early on whether manually extracting the data will

accomplish the project objectives.

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Recommendations are also provided relating to the project scope and organization. It is

recommended that an initial research scope be set early on and modified accordingly throughout

the project. The scope should be narrow at first and widened as progress is made. In terms of file

organization, a suggestion is to avoid saving files in multiple locations. When using a remote

computer, a recommendation is to create a system at the beginning of the project that establishes

which files will be saved on the remote computer and which files will be saved on the primary

computer. It is recommended that an organized file management system be created at the

beginning of a project. A naming convention system should be maintained for different versions

of files, and all versions of files should be saved in the same folder.

A final set of recommendations involves the use of human resources. It is recommended

that key contacts be established early on at architecture, building information modeling, energy

analysis consulting, and software firms. The relationships can be strengthened by maintaining

contact often, informing partners of problems and project successes, and collaborating on

publications. It is also suggested that researchers offer to help contacts with their own research

and share relevant resources including publications and other reference material.

Limitations and Future Work

The method presented for integrating life cycle assessment and conceptual building

designs has a number of limitations, each of which offers avenues for possible future research.

The first avenue for future work relates to expanding the scope of the project. Additional

software can be computationally integrated into the framework developed in the research.

Additional objectives beyond life cycle environmental impact and cost could then also be

considered in the analysis. Additional work may look at climate zones beyond the two analyzed

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in the research (climate zones 3 and 5). The only building type for which the method has been

tested is a residential mid-rise building, and additional work can determine the degree to which

the method can accommodate commercial and industrial building types. The geometry is also

restricted to orthogonal H-shapes, and the work could be extended to include more generalized

forms including curved shapes. The case studies only considered building heights from one to

eight stories and WWR values from 15 to 50%. Additional case studies could be analyzed to

comment more generally on the range of possible values for input parameters such as these.

Variation in R-value for different façade, roof, and floor materials is a possible extension of the

work to show additional embodied versus operational impact tradeoffs. Additional materials and

sizes can be included as well for each of the building components beyond those listed in the

Appendices. Another possible extension is optimizing variables by façade, in order to account

for variation in heat gain along each face of a building. Additional functionality can be built in to

the method to accommodate partial design decisions, such as when a designer may know a

façade R-value is not greater than a certain value but is uncertain about the lower bound.

A second avenue for future work involves building on the method for integrating

automated feedback with sequential building design decisions. The method uses probability mass

functions as the primary feedback mechanism to aid designers in understanding the

environmental impact implications of each design decision. These functions are provided only

after a decision has been made. Additional probability mass functions can be generated and

shown to designers prior to each decision. This pre-processing step would generate all possible

probability mass functions for every single variable for every single possible ordering of design

decisions. Considering the very large design space and large number of permutations such a

strategy would typically involve, an a priori tool would require considerable computing power in

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order to accurately characterize the distributions for each variable value across all possible

design decision sequences. One way to generate such functions would be brute-force

computation, although further research may illuminate more efficient ways to provide such

information and insight. Designers could then refer to these functions prior to making a decision,

visually determine which variable values best align with the achievement of their performance

objective, select a value, then receive feedback as described in Chapters 5 and 6 to see the

outcome of this decision. In this way, designers would have two separate tools for guiding their

decisions, one useful for determining the precise variable value to choose, and the other for

determining how well the outcome of the decision aligns with their performance objective.

Development of the method so far allows for backtracking and easy changes to design decisions.

However by including this additional tool, the process for selecting variable values is less reliant

on trial and error. Use of this additional tool should reduce the number of iterations and amount

of time needed to arrive at a desired design configuration.

Integration of such avenues for future work with the method’s current functionality can

yield a powerful tool for the sustainable design community. Designers, architects, contractors,

developers, engineers, and building owners can quickly receive life cycle environmental impact

and cost feedback on a range of building design alternatives. The method empowers stakeholders

with quantitative feedback on building designs, and integrating such information specifically

during the conceptual design phase should contribute to less fragmented interactions and more

streamlined workflows in the AEC industry. The method requires only a few inputs, which may

consist of nothing more than a project’s gross floor area. Alternatively, the method can

accommodate highly detailed building information models with unconventional geometries.

Thus, the method integrates with however much information is known and can adapt as new

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information is learned throughout the design process. The method’s ability to easily provide life

cycle assessment feedback based on very little information should also hopefully change

prevailing industry attitudes that life cycle assessment is too complex or not important enough to

perform on building designs. Sensitivity analysis is a powerful mechanism built into the method

that provides information on a full range of design alternatives, and such feedback enables

designers to forecast which design decisions affect cost and carbon footprint the most.

Stakeholders are therefore empowered to focus only on those decisions that have the greatest

bearing on their objectives of interest, and this can save the AEC industry significant time and

money as well as ultimately contribute towards the creation of a more sustainable built

environment.

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Appendices

Appendix 1 – Supporting data for chapter 1

Survey questions provided to design firms to gauge trends in use of

LCA and LCC feedback in design

(1) For new building projects, which feedback strategies does your firm use to reduce the

building’s environmental impact, and how often do you use these strategies? Choose

from the following strategies: LCA, strategies to reduce operational energy, and

strategies to reduced embodied energy.

(2) Which LCA software programs do you use?

(3) When in the design process (early stages, design development, late stages) does your firm

make massing and material decisions for each building component? Consider the

following components: foundation, structural elements, cladding, insulation, roofing,

partitions, finishes, and HVAC and mechanical systems.

(4) What factors prevent your firm from conducting LCA on projects?

(5) How helpful would an early stage design-LCA feedback tool be for your firm?

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Appendix 2 – Supporting data for chapters 2 and 3

Material alternatives considered in quantifying embodied impacts of

building components

Building component Material alternatives

Piles Steel pipe, precast concrete

Vapor barrier Polyethylene plastic sheeting, polypropylene cloth

Columns and beams Concrete column/concrete beam, concrete column/glulam beam, concrete column/LVL beam,

concrete column/WF beam, HSS column/glulam beam, HSS column/LVL beam, HSS column/WF

beam, WF column/glulam beam, WF column/LVL beam, WF column/WF beam

Floor structure Glulam/plywood decking, precast hollowcore/concrete topping, wood I-joist/plywood decking,

open-web steel joist/concrete topping, open-web steel joist/plywood decking, precast double-

T/concrete topping, steel joist/concrete topping, steel joist/plywood decking, suspended concrete

slab/concrete topping, wood joist/plywood decking, wood chord and steel web truss/plywood

decking, wood truss/plywood decking

Roof Precast hollow-core concrete, precast concrete double-T, suspended concrete slab, open-web steel

joist w/steel decking, open-web steel joist w/wood decking, glulam joist with plank decking, wood

I-joist w/WSP decking, solid wood joist w/WSP decking, wood chord/steel web truss with WSP

decking, wood truss (flat) with WSP decking

Roof membrane EPDM, PVC, modified bitumen, 4-ply built-up roofing system, steel roofing system

Stairs Precast concrete, wood, steel

Railings Wood, precast concrete, aluminum

Cladding Brick, steel, stucco, vinyl, wood, limestone, concrete

Wall structure Concrete block, cast-in-place concrete, steel stud wall, curtainwall: opaque glazing, curtainwall:

metal spandrel panel

Window frame Aluminum, steel, PVC, wood-metal, wood

Exterior doors Steel, wood-glass-steel, wood-steel

Partitions Steel stud wall, concrete block

Interior Doors Wood, steel

Wall coverings Ceramic wall tile, vinyl

Flooring surface Ceramic floor tile, stone floor tile, cement facing tile with fiber, cement cast plaster floor, neoprene,

polyacrylate with ground granite, polyurethane with vinyl chips, carpet tile with nylon, laminated

veneer wood

Floor insulation Blown cellulose, extruded polystyrene, polystyrene foam slab, cork slab, foam glass, glass wool

mat, glass wool (fleece), rock wool, rock wool (fleece), rock wool (packed), tube insulation

(elastomere), urea formaldehyde foam slab, urea formaldehyde in situ foaming

Duct insulation Blown cellulose, extruded polystyrene, polystyrene foam slab, cork slab, foam glass, glass wool

mat, glass wool (fleece), rock wool, rock wool (fleece), rock wool (packed), tube insulation

(elastomere), urea formaldehyde foam slab, urea formaldehyde in situ foaming

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Material(s) associated with each building component and material

properties used to quantify building component embodied impacts

Material Building Component(s) Density (kg/m3) Embodied impact factor (kg

CO2e/kg material)

Concrete Mat foundation 2400 .050

Concrete Footing 2400 5.15

Concrete Piles, pile caps 2400 0.12

Concrete (precast) Grade beams, stairs, railings,

Cladding

2400 0.121

Concrete Slab on grade 2400 .065

Steel Rebar 8000 1.03

Steel Piles, stairs, cladding 8000 1.89

Steel (stainless) Gutter 8000 3.38

Brass Service equipment (various) 8400 2.34

Cast iron Service equipment (various) 7000 2.34

Limestone Cladding 2500 0.019

Limestone Flooring 2500 0.37

Stucco Cladding 800 0.070

Copper Gutter 8930 1.81

Brick Cladding 2403 1.13

Neoprene Flooring 1230 2.46

Polyethylene Vapor barrier 950 1.58

Fiberglass Pipe insulation 26 1.50

Flat glass Glazing 2310 1.06

Polyvinyl butyral Glazing 1100 6.98

Vinyl Gutter, cladding 580 2.37

Vinyl Wall coverings 1360 1.80

Ceramic tile Wall coverings, flooring 1360 0.74

Acrylic paint Wall coverings 1200 2.66

Aluminum Gutter, railings 2700 11.4

Wood (Douglas fir) Formwork 600 1.02

Wood (Douglas fir) Gutter 600 0.29

Wood Stairs, railings, cladding, flooring 600 0.33

Polyurethane foam (high density) Flooring 400 4.04

Polypropylene (vapor retarder) Vapor barrier 946 1.68

Polyacrylate terrazzo Flooring 1054 3.23

Carpet tile Flooring 74 6.78

Blown cellulose Floor insulation, duct insulation 56 0.35

Extruded polystyrene Floor insulation, duct insulation 30 10.1

Polystyrene foam slab Floor insulation, duct insulation 30 3.36

Cork slab Floor insulation, duct insulation 110 1.09

Foam glass Floor insulation, duct insulation 110 1.50

Glass wool mat Floor insulation, duct insulation 40 1.40

Glass wool, fleece Floor insulation, duct insulation 40 2.79

Rock wool Floor insulation, duct insulation 46 1.00

Rock wool, fleece Floor insulation, duct insulation 28 1.12

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Rock wool, packed Floor insulation, duct insulation 100 1.05

Tube insulation, elastomere Floor insulation, duct insulation 75 4.16

Urea formaldehyde foam slab Floor insulation, duct insulation 20 2.71

Urea formaldehyde foam, in situ

foaming

Floor insulation, duct insulation 20 2.85

Embodied impact heuristics developed for each building component

Uniformat element Assembly Sub-component (a)Material quantity formula

A: Substructure (9) piles piles density * (b)slab area * slab depth

vapor barrier density * thickness * ((b)slab area + (c)perimeter * (d)footing depth)

caps 48 * density *((c)number of interior grid intersections + (c)number of

exterior grid intersections)

slab-on-grade (b)slab area * thickness

grade beam 4 * density * (c)perimeter

rebar 8 * (b)slab area

formwork 4 * thickness * (c)perimeter

footings footings 0.2 * density * slab area

mat foundation foundation (b)slab area * slab depth

B: Shell (20) columns and beams columns and beams (e)gross floor area + roof area

floor floor structure gross floor area – (b)slab area

roof roof structure (b)slab area

membrane (b)slab area

insulation (b)slab area

paint (b)slab area

stairs stairs 0.00017 *density *slab area

railing 0.91 *density *slab area * (number of floors – 1)

cladding cladding density * thickness * (1-WWR) * (c)perimeter * (e)height

exterior walls wall structure (1-WWR) * (c)perimeter * (e)height

insulation (1-WWR) * (c)perimeter * (e)height

membrane (1-WWR) * (c)perimeter * (e)height

gypsum (1-WWR) * (c)perimeter * (e)height

paint (1-WWR) * (c)perimeter * (e)height

glazing glass 2 * density * thickness * (f)glazing area

polyvinyl butyral density * thickness * (f)glazing area

frame (f)glazing area

hardware (f)glazing area

doors door 0.00098 * gross floor area

hardware 0.00098 * gross floor area

C: Interiors (12) partitions partition structure 1.5 * gross floor area

gypsum 1.5 * gross floor area

paint 1.5 * gross floor area

doors door 0.0039 * gross floor area

hardware 0.0039 * gross floor area

wall finishes covering density * thickness * (2.55 * gross floor area + (c)perimeter *

(e)height)

paint density * thickness * (c)perimeter * (e)height

flooring surface density * thickness * gross floor area

insulation density * thickness * gross floor area

ceiling plaster gross floor area + roof area

gypsum gross floor area + roof area

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paint gross floor area + roof area (g) D: Services (61) mechanical (17) air conditioner 29,687 air handler 8,347 gland ball valve 164 boiler 8,430 chiller 26,466 condenser 6,788 direct digital

controls

720

ductwork 11,425 duct insulation 20,026 fan and motor

exhaust fan

(ceiling)

2,020

exhaust fan (roof

mounted)

277

flow control valve 109 HVAC control

panel

1,120

thermostat 22.5 VAV control box 103 pipe and fittings 120 gages and valves 200

electrical (16) bus duct and fittings 26,113

circuit breaker 0.45

disconnect switch 48

emergency lighting

pack, 2 lights

with battery

(entire)

3,463

emergency lighting

pack, 2 lights

with battery

(lamps)

413

exit lighting fixture,

w/battery

307

ballast and lamps,

fluorescent

lighting fixture,

T8, 32W (glass

tube)

364

ballast and lamps,

fluorescent

lighting fixture,

T8, 32W (steel)

57,543

heat detector 267

main switchgear 18,084

motor starter 1165.5

power panel board 1194

receptacle 1,448

secondary

transformer

9,059

TV cable outlet 80

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wiring device

(switch)

357

plumbing (23) pipe and fittings,

cast iron

879

pipe and fittings,

copper

5,622

pipe and fittings,

PVC

1,402

pipe and fittings,

steel

879

gasket and bolts, 1” 80

gasket and bolts, 3” 24

pipe insulation 820.5

backflow preventer 1,295

bathtub and shower

enclosure –

diverter valve

57.12

bathtub and shower

enclosure

3,234

bathtub and shower

enclosure –

faucet washer

and clean shower

heat

8.4

bathtub and shower

enclosure – valve

set

42

circulator pumps 125.3

floor drain 755

flush tank water

closet

8,916

lavatory 9,010

lavatory valve 901

lavatory faucet

washer and clean

trap

270.3

lavatory washer and

spud connection

180.2

strainer service sink 321

service sink faucet

washer and clean

trap

9.63

service sink valve

set

32.1

water heater 79.8

fire (4) manual pull station 145

smoke detector 349

fire extinguisher 800

fire sprinkler head 250

conveying (1) elevator 850 (a) Material quantities may be multiplied by number of buildings in a project. This assumes each building is uniform in terms of all variables. (b) Slab area = gross floor area / number of floors.

204

(c) Variable determined by length and width parameters. (d) Constraint values are given in Chapter 2. (e) Height = number of floors * floor to floor height. (f) Glazing area = building length * building height * WWR. Note glazing area is dependent on building length for each façade. (g) Chapter 3 describes how an online facility operations reference database takes as inputs gross floor area, constraints,

and service life assumptions outlined in Chapter 2, scales the given numerical quantities presented here according to peak building load, and

returns impacts.


Variables and variable values used as inputs for environmental impact

feedback method

Design variable Values

Window-to-wall ratio 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%

Has fins? true, false

Has overhangs? true, false

Fin depth (m) 0.30, 0.91, 1.5, 2.1

Overhang depth (m) 0.30, 0.91, 1.5, 2.1

Building shape a: 10, 15, 20, 25, 30

b: 10, 15, 20, 25, 30

c: 5, 10, 15, 20, 25, 30

d: 10, 15, 20, 25, 30, 35

e: 5, 10, 15, 20, 25

af

Number of buildings 3, 4

Number of floors 5, 6, 7, 8

Orientation 0, 5, 10, …, 345, 350, 355

Substructure system mat foundation, piles, footings

Material variable Values

Cladding brick, steel, stucco, vinyl, wood, limestone, concrete

Roof precast hollow-core concrete, precast concrete double-T,

suspended concrete slab, open-web steel joist with steel

decking, open-web steel joist with wood decking, glulam joist

with plank decking, wook I-joist with WSP decking, solid

wood joist with WSP decking, wood chord/steel web truss with

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WSP decking, wood truss (flat) with WSP decking

Wall concrete block, cast-in-place concrete, 2x4 steel stud 16”

o.c., 2x4 steel stud 24” o.c.

Partitions steel stud 16” o.c. + gypsum board, steel stud 24” o.c. +

gypsum board, concrete block + gypsum board, concrete block

Columns and beams concrete column/concrete beam, concrete column/glulam

beam, concrete column/LVL beam, concrete column/WF

beam, HSS column/glulam beam, HSS column/LVL beam,

HSS column/WF beam, WF column/glulam beam, WF

column/LVL beam, WF column/WF beam

Floor finishes ceramic floor tile, stone floor tile, cement facing tile with fiber,

cement cast plaster floor, neoprene, polyacrylate with ground

granite, polyurethane with vinyl chips, carpet tile + nylon,

laminated veneer wood

Floor insulation blown cellulose, extruded polystyrene, polystyrene foam slab,

cork slab, foam glass, glass wool mat, glass wool (fleece), rock

wool, rock wool (fleece), rock wool (packed), tube insulation

(elastomere), urea formaldehyde foam slab, urea formaldehyde

foam

Floor structure glulam + plywood decking, precast hollowcore + concrete

topping, wood I-joist + plywood decking, open-web steel joist

+ concrete topping, open-web steel joist + plywood decking,

precast double-T + concrete topping, steel joist + concrete

topping, steel joist + plywood decking, suspended concrete

slab + concrete topping, wood joist + plywood decking, wood

chord and steel web truss + plywood decking, wood truss +

plywood decking

Piles steel pipe, precast concrete

Shading device steel, aluminum, concrete

Wall finishes ceramic wall tile + acrylic paint, vinyl wall covering + acrylic

paint

Window frame aluminum, steel, PVC, wood-metal, wood

Thickness Variable Values

Ceiling (m) 0.0064, 0.011, 0.015, 0.019

Cladding (m) 0.025, 0.067, 0.11, 0.15

Floor finishes (m) 0.0064, 0.011, 0.015, 0.019

Floor insulation (m) 0.089, 0.13, 0.17, 0.22

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Mat foundation (m) 0.20, 0.44, 0.67, 0.90

Wall finishes (m)

Wall tile 0.0064, 0.011, 0.015, 0.019

Vinyl wall covering 0.0016, 0.0021, 0.0026, 0.0032

b,c

Glazing thickness (m) 0.0032, 0.0064, 0.0095, 0.0127, 0.0159, 0.0191

aShape parameter “f” is dependent on the values for a, b, c, d, and e and ranges from 2m to 30m

bU-factor (W/m

2*K) associated with each glazing thickness is: 0.46, 0.23, 0.16, 0.12, 0.092, 0.077

cSolar heat gain coefficient is 0.32 and visible transmittance is 0.62 for each glazing thickness


Variables and variable values used as inputs for environmental

impact and cost feedback

Design variable Values

Window-to-wall ratio 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% Has fins? true, false Has overhangs? true, false Fin depth (m) 0.30, 0.91, 1.5, 2.1 Overhang depth (m) 0.30, 0.91, 1.5, 2.1 Building shape a: 10, 15, 20, 25, 30

b: 10, 15, 20, 25, 30

c: 5, 10, 15, 20, 25

d: 10, 15, 20, 25, 30, 35, 40, 45, 50

e: 5, 10, 15, 20, 25

af

Number of buildings 3, 4 Number of floors 5, 6, 7, 8 Orientation 0, 5, 10, …, 345, 350, 355 Substructure system mat foundation, piles, footings

Material variable Values

Cladding brick, steel, stucco, vinyl, wood, limestone, concrete Roof precast hollow-core concrete, suspended concrete slab, open-web steel joist with

steel decking with steel decking Partitions steel stud 24” o.c. + gypsum board, concrete block + gypsum board Columns and beams concrete column/concrete beam, concrete column/WF beam, WF column/WF

beam beam Floor finishes ceramic floor tile, stone floor tile, cement facing tile with fiber, cement cast plaster floor, neoprene, polyacrylate with ground granite, polyurethane with vinyl chips, carpet tile + nylon, laminated veneer wood Floor insulation blown cellulose, extruded polystyrene, polystyrene foam slab, foam glass, glass

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wool mat, rock wool Floor structure precast hollowcore + concrete topping, open-web steel joist + concrete topping, steel joist + concrete topping, suspended concrete slab + concrete topping Piles steel pipe, precast concrete Shading device steel, aluminum, concrete Wall finishes ceramic wall tile + acrylic paint, vinyl wall covering + acrylic paint Glazing material regular, low-e Thickness Variable Values

Ceiling (m) 0.0064, 0.011, 0.015, 0.019 Cladding (m) 0.025, 0.067, 0.11, 0.15

Material properties used to quantify building component embodied

impacts

Material Building Component(s) Density (kg/m3) Embodied impact factor (kg

CO2e/kg material)

Concrete Mat foundation 2400 .050

Concrete Footing 2400 5.2

Concrete Piles, grade beams, cladding,

shading

2400 0.12

Concrete Slab on grade 2400 .065

Concrete Columns + beams n/a 7.9

Concrete (precast hollowcore) Roof structure n/a 14

Concrete (precast hollowcore) +

concrete topping

Floor structure n/a 8.7

Concrete block (6”) + gypsum

board

Partitions n/a 1.7

Concrete (suspended slab) Roof structure n/a 21

Concrete (suspended slab) +

concrete topping

Floor structure n/a 16

Concrete + steel Columns + beams (wide

flange)

n/a 5.0

Cement facing tile with fiber Floor finishes 2400 1.1

Cement cast plaster floor Floor finishes 2400 0.37

Steel Rebar 8000 1.0

Steel Piles, cladding, shading 8000 1.9

Steel Columns (wide flange) +

beams (wide flange)

n/a 2.4

Steel stud (24” o.c.) + gypsum

board

Partitions n/a 15.2

Steel joist + concrete topping Floor structure n/a 5.9

Steel joist (open-web) + steel

decking

Roof structure n/a 11

Steel joist (open-web) + concrete

topping

Floor structure n/a 5.4

Limestone Cladding 2500 0.019

Limestone Floor finishes 2500 0.37

Stucco Cladding 800 0.070

Brick Cladding 2403 1.1

Neoprene Floor finishes 1230 2.5

Polyethylene Vapor barrier 950 1.6

208

Regular glass (flat glass) Glazing 2310 0.95

Low-e glass Glazing 2310 1.1

Vinyl Cladding 580 2.4

Vinyl Wall coverings 1360 1.8

Ceramic tile Wall coverings, floor finishes 1360 0.74

Acrylic paint Wall coverings 1200 2.7

Aluminum Shading devices 2700 11

Wood Cladding, floor finishes 600 0.33

Polyurethane foam (high density) Floor finish 400 4.0

Polypropylene (vapor retarder) Vapor barrier 946 1.7

Polyacrylate terrazzo Floor finishes 1054 3.2

Carpet tile Floor finishes 74 6.8

Blown cellulose Floor insulation 56 0.35

Extruded polystyrene Floor insulation 30 10

Polystyrene foam slab Floor insulation 30 3.4

Foam glass Floor insulation 110 1.5

Glass wool mat Floor insulation 40 1.4

Rock wool Floor insulation 46 1.0

Sample cost formulas

Uniformat element Assembly Sub-component (a)Cost formula

A: Substructure pile piles (steel) $58 * ((c)# interior grid intersections + (c)# exterior grid intersections)

* (d)pile depth

piles (concrete) $60.5 * ((c)# interior grid intersections + (c)# exterior grid

intersections) * (d)pile depth

vapor barrier $0.088 * ((b)slab area + (c)perimeter * (d)footing depth)

slab-on-grade $3.75 * (b)slab area

grade beam $70 * (c)perimeter

footings concrete footings +

rebar

$1282.5 * ((c)# interior grid intersections + (c)# exterior grid

intersections)

mat foundation foundation $142 * (b)slab area

B: Shell columns and beams concrete columns

and beams

$143 * ((c)# interior grid intersections + (c)# exterior grid

intersections) * (d)floor-to-floor height * number of floors

floor floor structure

(precast

hollowcore +

concrete topping)

$11.35 * (b)slab area * number of floors

roof roof structure

(precast

hollowcore

concrete)

$13.26 * (b)slab area

cladding cladding (steel) $16.85 * thickness * (1-WWR) * (c)perimeter * (e)height

C: Interiors partitions partition structure

(steel stud +

gypsum

board)

$3.8 * (c)perimeter * (e)height

wall finishes covering (ceramic

wall tile)

$3.99 * (c)perimeter * (e)height

floor finishes surface (stone floor

tile)

$12.05 * gross floor area

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ceiling gypsum + paint $42.50 * (gross floor area + (b)slab area) (f) D: Services mechanical air conditioner gross floor area

electrical compact fluorescent

lighting fixture

ballast and lamps

(glass tube)

gross floor area

plumbing circulator pump gross floor area

fire fire extinguisher gross floor area

conveying elevator gross floor area (a) All formulas multiplied by number of buildings, a parameter given in Table 21. (b) Slab area = gross floor area / number of floors. (c) Perimeter and # grid intersections determined by length and width parameters, as given in Table 21. (d) Assumption described in “Case Study” section of Chapter 6. (e) Height = number of floors * floor to floor height. (f) The “Analysis process” sub-section of the “Methodology” section in Chapter 6 describes how an online facility operations reference

database takes as inputs gross floor area, a service life assumption, and other assumptions described in the “Case Study” section and returns

service costs.

210

References

Aboulnaga, M. (2006). Towards green buildings: Glass as a building element – the use and

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