LOCATION-BASED LEADING INDICATORS

IN BIM FOR CONSTRUCTION

SAFETY

by

XU SHEN

ERIC MARKS, COMMITTEE CHAIR

ROBERT G. BATSON

STEPHANIE VEREEN

GARY P. MOYNIHAN

ZHENG O’NEILL

A DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

in the Department of Civil, Construction and Environmental Engineering

in the Graduate School of

The University of Alabama

TUSCALOOSA, ALABAMA

2017

Copyright Xu Shen 2017

ALL RIGHTS RESERVED

ii

ABSTRACT

The US construction industry continues to experience a high number of injuries and

fatalities in comparison to other US industrial sectors (BLS 2013). Although the U.S. construction

accounts for only 4% of total employment, the industry experiences a disproportionate 19% of the

total fatalities experienced by the U.S. workforce (BLS 2014). An enhanced understanding of

safety leading indicators for construction sites can be an influential factor in mitigating existing

hazards and predicting future hazards (Hinze et al. 2013, Hinze 2006). Although construction

companies in the U.S. are required by OSHA regulation to report all fatalities, injuries, and

illnesses that occur on construction sites, a more concerted effort including research and

implementation is required for safety leading indicators including near miss reporting and hazard

identification.

This research seeks to test the hypothesis that it is feasible to collect, analyze, and

disseminate safety leading indicators through location-based information and visualization.

Because one of the most impactful transitions in the construction industry in the past decade has

been a transition to digitized construction documents with visualization of construction processes

through Building Information Modeling (BIM), BIM provides a real-time visualization and

communication platform for construction stakeholders (Azhar 2011, Eastman et al. 2011).

Furthermore, the construction industry is transitioning from lagging or reactive safety data

collection (i.e., injuries, illnesses and fatalities) to pro-active or leading indicator safety data

collection (i.e., near misses and hazard identification) (Hallowell et al. 2013). This research

iii

advocates for the effective retrieval, analysis, visualization and dissemination of safety leading

indicator data through created databases, algorithms and BIM functionality.

Since a large majority of function components in a BIM are location-based, the outcome

of this research is limited to location-based safety leading indicators (i.e., leading indicator safety

data that can be assigned to a specific location). The research approach is divided into three major

components: 1) near miss reporting, 2) automatic hazardous proximity zone generation, and 3) site

location optimization. The framework will be evaluation in controlled laboratory settings as well

as active construction sites. Throughout the research methodology, feedback and mentorship from

construction engineering and management employees will be collected and integrated. This

research connects the capabilities of BIM to safety data collection, storage, analysis and

visualization.

iv

DEDICATION

This dissertation is dedicated to my parents, Yanzhong Shen and Linhong Lv.

v

LIST OF ABBREVIATIONS AND SYMBOLS

2D ……………………………………………………………………... Two Dimensional

3D …………………………………………………………………… Three Dimensional

4D ……………………………………………………………………. Four Dimensional

API ………………………………………………… Application Programming Interface

BIM …………………………………………………….. Building Information Modeling

BLS …………………………………………………………... Bureau of Labor Statistics

CII …………………………………………………..…… Construction Industry Institute

CSP ………………………………………………………... Certified Safety Professional

GIS ……………………………………………………... Geographic Information System

GPS …………………………………………………………... Global Positioning System

OSHA ………………………………...... Occupational Safety and Health Administration

PPE …………………………………………………….... Personal Protective Equipment

PSM ……………………………………………………. Physiological Status Monitoring

PtD ………………………………………………………...… Prevention-through-Design

RFID …………………………………………………….. Radio Frequency Identification

TRIR ……………………………………………………...Total Recordable Incident Rate

VBA ……………………………………………………..... Visual Basic for Applications

vi

ACKNOWLEDGEMENTS

I would like to acknowledge mentorship by Dr. Eric Marks during my time at the

University of Alabama. His passion for research and teaching inspired and motivated me to pursue

a career in academia. I would like to thank other members of my doctoral committee including Dr.

Robert G. Batson, Dr. Stephanie Vereen, Dr. Gary P. Moynihan, and Dr. Zheng O’Neill for their

time and advice.

Much of my success in graduate school can be attributed to my fellow laboratory

colleagues. The support of these individuals including Siyuan Song and Ibukun Awolusi was

critical to my accomplishments. I look forward to continuing professional relationships as well as

friendships with these great people.

I am indebted to my parents and sisters for instilling work ethic and strong morals within

me and supporting me through this time. My parents’ passion for education and impacting the lives

of others was transferred to their children. None of this would be possible without the

unconditional support from my family and friends.

vii

CONTENTS

ABSTRACT……………………………………………………………………………. ii

DEDICATION ………………………………………………………………………… iv

LIST OF ABBREVIATIONS AND SYMBOLS……………………………………...... v

ACKNOWLEDGEMENTS …………………………………………………………… vi

LIST OF TABLES ……………………………………………………………………... x

LIST OF FIGURES ……………………………………………………………………. xi

CHAPTER 1 INTRODUCTION ……………………………………………………….. 1

1.1 Safety …………………………………………………………………… 1

1.2 BIM ……………………………………………………………………... 1

1.3 Safety Leading Indicators ………………………………………………. 2

1.4 Motivation ……………………………………………………………… 3

CHAPTER 2 LITERATURE REVIEW ………………………………………………... 5

2.1 Construction Safety …………………………………………………….. 5

2.1.1 Construction Industry Safety Statistics ………………………... 5

2.1.2 Current Standards and Regulations ……………………………. 6

2.2 Safety Leading Indicators ………………………………………………. 6

2.2.1 Near Miss ………………………………………………………. 7

2.2.2 Human-Equipment Interaction ………………………………… 8

viii

2.2.3 Construction Site Layout ……………………………………... 11

2.3 BIM …………………………………………………………………… 12

2.4 Problem and Needs Statement ………………………………………… 14

CHAPTER 3 RESEARCH METHODOLOGY AND RESULTS ……………............. 15

3.1 Research Objective, Scope and Hypothesis …………………………... 15

3.2 Near Miss Reporting for BIM ………………………………………… 16

3.2.1 Near Miss Information Visualization in BIM ………………... 18

3.2.2 Implementation Strategy ……………………………………... 23

3.2.3 Case Study ……………………………………………………. 24

3.2.4 Expert Panel Review …………………………………………. 26

3.3 Hazardous Proximity Zone ……………………………………………. 28

3.3.1 Hazardous Proximity Zone Design for Heavy Equipment …… 28

3.3.2 Automatic Creation of Hazardous Proximity Zone in BIM ….. 35

3.3.3 Implementation Strategy and Validation ……………………... 36

3.4 Site Location Optimization ……………………………………………. 39

3.4.1 Optimization of Tower Crane’s Location in BIM ……………. 39

3.4.2 Case Studies and Validation ………………………………….. 47

3.4.3 Expert Panel Review …………………………………………. 54

3.5 Summary ……………………………………………………………… 55

CHAPTER 4 CONCLUSIONS ………………………………………………………. 57

4.1 Review of Objective ………………………………………….……….. 57

4.2 Conclusions …………………………………………………………… 57

4.2.1 Near Miss Reporting …………………………………………. 57

ix

4.2.2 Hazardous Proximity Zone …………………………………… 58

4.2.3 Site Location Optimization …………………………………... 59

4.3 Limitations ……………………………………………………………. 60

4.4 Contributions and Impacts ……………………………………………. 60

REFERENCES ………………………………………………………………………... 62

APPENDIX A: CONSTRUCTION EXPERT INTERVIEW QUESTIONS ………….. 73

APPENDIX B: GEOMETRIES OF EQUIPMENT HAZARD ZONE IN AUTOCAD 75

APPENDIX C: CODE EXAMPLES (PROGRAMMING) …………………………… 77

x

LIST OF TABLES

Table 1: Safety Leading Indicator Examples……………………………………………. 3

Table 2: Categories of Reported Accidents……………………………………………... 6

Table 3: User Interface Functions ………………………………………………………17

Table 4: Select Categories for Near Miss Data Input ………………………………….. 19

Table 5: Safety Expert Review Panel Responses ……………………………………… 27

Table 6: Hazard Zone Results for Each Piece of Evaluated Equipment ………………..35

Table 7: Dimensions of Created Construction Site ……………………………………..39

Table 8: Statistical Summary of Internal Validation Results …………………………...49

Table 9: Dimensions of the Simple Case Study Construction Site ……………………..50

Table 10: Dimensions of the Complex Case Study Construction Site ………………….52

Table 11: Expert Review Panel Responses .…………………………………………….54

xi

LIST OF FIGURES

Figure 1: Construction Fatalities Caused by Contact with Objects or Equipment ……... 9

Figure 2: Filtering Capabilities of Reported Near Misses …………………………….. 17

Figure 3: User interface initial screen for the near-miss visualization tool …………… 18

Figure 4: Information template for reported near misses within BIM ………………... 20

Figure 5: (a) Three-dimensional and (b) two-dimensional view of the near-miss

sphere in circle ……………………………………………………………... 21

Figure 6: Exported near-miss database (Partial) ……………………………………… 22

Figure 7: (a) Filtering capabilities of reported near misses by severity; (b) Searching

near miss by unique identification number ………………………………… 23

Figure 8: Near-miss events visualized within a BIM …………………………………. 25

Figure 9: Steps for determining the hazardous zone around a piece of construction

Equipment ………………………………………………………………….. 28

Figure 10: Equipment footprint for a dump truck …………………………………….. 29

Figure 11: Initial safety boundary for a typical three-axle, tandem rear axle

dump truck …………………………………………………………………. 30

Figure 12: Hazard zone based on dump truck function on a construction site ………... 31

Figure 13: Hazard zone of a typical three-axle, tandem rear axle dump truck ………... 33

Figure 14: Hazard zone of a typical backhoe loader ………………………………….. 34

Figure 15: Hazard zone of a typical excavator ………………………………………... 35

Figure 16: User interface for hazard zone creation …………………………………… 36

Figure 17: Hazard zone of backhoe excavator (also called rear actor) in AutoCAD…...37

Figure 18: Verification of the proposed hazard zone …………………………………. 38

xii

Figure 19: Designed typical construction site layout ………………………………….. 40

Figure 20: Crane Location Optimization Interface ……………………………………. 41

Figure 21: Construction site layout in MATLAB by binary image …………………… 41

Figure 22: Logic framework for optimized decision criteria …………………………. 42

Figure 23: Details and prioritization of constraints within the tower crane

optimization tool …………………………………………………………… 44

Figure 24: Computed tower crane boom coverage area ………………………………. 45

Figure 25: Tower crane optimization location resulting output ………………………. 46

Figure 26: Relationships between optimization tool functions in BIM, text file

and MATLAB ……………………………………………………………... 47

Figure 27: Results Summary of Internal Validation Results ………………………….. 49

Figure 28: Active construction site (simple case study) for external validation (a)

and plan view of the BIM model with the actual tower crane location (b) ... 51

Figure 29: Output of tower crane optimal position tool for external validation

construction site (simple case study) ……………………………………… 52

Figure 30: Output of tower crane optimization tool in BIM (a) and screenshot (b)

of construction site (complex case study) ………………………………….. 53

Figure 31: Framework of this research ………………………………………………... 56

1

CHAPTER 1 INTRODUCTION

This research focuses on construction safety, specifically with safety leading indicator

data and resulting collection, analysis, visualization, and dissemination of this data through

Building Information Modeling (BIM) platforms. This chapter discusses the overview and

challenges of construction safety and a transition of the construction industry from measuring

lagging indicators to leading indicators. BIM is also reviewed as a platform to host and visualize

data for construction safety management. A research motivation statement is presented at the

conclusion of this chapter.

1.1 Safety

Although the construction industry accounts for 4% of the total U.S. employed

workforce, the industry experienced 19% of the fatalities (BLS 2014). Even though the number

of fatalities on construction sites has decreased over time, the rate of workplace fatalities has

levelled of in recent years (OSHA 2011a).

Traditional measures of safety are after-the-fact measures which are measured after

injuries have already occurred because they rely on retrospective data (Choudhry et al. 2007).

This means the success of safety control is measured by the failures (Cohen 2002). Recognizing

such shortcoming, a more proactive and upstream (e.g., leading indicators) should be advocated.

2

1.2 BIM

BIM has been described as one of the most promising recent developments in the

construction industry because of its ability to provide a design, communication, and project

monitoring platform throughout the lifecycle of a project (Azhar 2011, Eastman et al. 2011).

Construction project stakeholders are using BIM to integrate many aspects of a project including

building life cycle, designing (Penttila 2007), planning, construction (Kymmell 2008), and

operation (Akcamete et al. 2010). Due to the hazardous nature of construction sites, safety

management is a critical component to a successful project. The growing use and implementation

of BIM within the construction industry is transforming the way safety data can be collected,

edited, and retrieved. For example, BIM provides a platform to aid in safety management and

eventually enhance safety performance of construction site personnel.

1.3 Safety Leading Indicators

Safety leading indicators differ from lagging indicators (i.e., injuries, illnesses, and

fatalities) because safety leading indicators do not require a negative safety event to occur.

Safety leading indicators are measurements of processes, activities, and conditions that define

performance and are capable to predict future results (Hinze et al. 2013, Hinze 2006). Active

safety leading indicators can periodically monitor the level of employee safety performance on

construction sites (Hinze et al. 2013). This includes evaluating progress of newly implemented

components of a safety program or innovative safety implements (Hallowell and Gambatese

2009). Safety leading indicators have been linked with data visualization and communication

platforms to improve safety planning and performance (Chantawit et al. 2005). Location of an

event has been identified as a needed safety measure for identifying and mitigating hazards in a

3

visualization environment (Chantawit et al. 2005). Table 1 shows the examples of safety leading

indicators that enhance construction safety.

Table 1: Safety Leading Indicator Examples

Safety Leading Indicator Description

Behavior-based safety Implementing the science of human behavior to improve

construction worker safety performance (Fang and Huang

2004, Choudhry et al. 2007, Zohar and Luria 2003)

Jobsite Hazard Analysis Technique that focuses on job tasks as a way to identify

hazards before they occur (Hallowell and Gambatese 2009,

Rozenfield et al. 2010, OSHA 2002a)

Safety Training The action of training employees in safety regulations, best

safety behavior practices, common hazards and other safety-

related material (Agnew and Daniels 2011, Burke et al. 2006,

O’Connor et al. 2005)

Risk Quantification A quantities analysis of assessing potential risk present on a

given construction site (Hallowell and Gambatese 2009,

Fortunato et al. 2011, Ho and Dzeng 2010)

1.4 Motivation

OSHA regulations require construction companies to record and periodically report

safety lagging indicators (OSHA 2013). However, measures of safety lagging indicators are

incapable of predicting hazards, assigning severity of an event, or identifying event causation

(Flin et al. 2000). Additional effort is required to be invested in managing safety leading

indicators to prevent and control injury before potential hazardous events happen.

Previous research ventures have identified the need for incorporating BIM with safety in

construction (Ku and Mills 2010). Although a substantial body of research exists for applications

of digital technologies to construction safety issues, very few tools and application have been

created for construction safety (Zhou et al. 2012). Although several technological applications

have been created and implemented for BIM, safety data and information have yet to be included

in the communication and visualization capabilities of BIM. A need exists in the construction

4

industry to visualize safety data and analyze safety information (specifically near misses) within

a BIM. By positively impacting current practices in safety planning, technological systems

including BIM can provide pivotal information to reduce safety incident rates (Teizer et al. 2010,

Cheng et al. 2011).

5

CHAPTER 2 LITERATURE REVIEW

The U.S. construction industry continues to experience a high number of workplace

fatalities (BLS 2014). An abundance of research has been performed in construction safety,

including safety leading indicators and safety applications for BIM, in an attempt to improve

construction worker safety performance and resulting lagging indicator values. These academic

and industry research results are presented as part of this literature review. The following review

also covers current injuries and fatalities incidents associated with construction industry. Current

industry practices and research of the mentioned location-based leading indicators are also

presented. A research “needs statement” is derived from the findings to conclude this section.

2.1 Construction Safety

The construction industry continues to be one of the most dangerous work environments

in the U.S (BLS 2014). This section discusses worker safety performance in construction and the

current safety regulations.

2.1.1 Construction Industry Safety Statistics

The construction industry experienced 874 fatalities in 2014 which represented 20.6% of

all fatalities experienced by the U.S. private sector (BLS 2014). These values were slightly lower

for 2013 with 856 fatalities that made up 18.7% of all fatalities experienced by the U.S.

construction private sector (BLS 2013). Although fatalities are the most negative safety event,

injuries and illnesses also negatively impact the success of a construction project. The U.S.

6

Bureau of Labor Statistics (BLS) recorded that the construction industry experienced 117

recordable cases for every 10,000 workers. In 2013, the median number of work days missed

resulting from injury or illness was approximately eight days (BLS 2014). The construction

industry experienced 200,900 non-fatal injuries which was marginally lower than the non-fatal

injuries reported in 2013 (BLS 2013).These recordable incidents may have a negative impact on

a construction project’s success through lost work time, decreased productivity, increased

medical costs and in some cases, loss of life.

2.1.2 Current Standards and Regulations

Construction companies in the U.S. are required to report all fatalities, injuries, and

illnesses that occur during the work day or results from the work environment (OSHA 2011b).

These categories are characterized as safety lagging indicators because their measurement and

assessment occur after a negative safety event occurs (OSHA 2013). Typical categories of safety

lagging indicators reported by construction companies are summarized in Table 2. Measures of

safety lagging indicators are incapable of predicting hazards, assigning severity of an event or

identifying event causation (Flin et al. 2000).

Table 2: Categories of Reported Accidents (OSHA 2011b)

Recordable Category Description

Total recordable cases Sum of all recordable occupational injuries and illnesses, including

lost-workday cases and nonfatal cases without lost workdays

Total lost-workday

cases

Sum of cases involving days away from work and/or days of

restricted activity

Non-fatal cases without

lost workdays

Sum of cases which are recordable injuries or illnesses which do not

result in death or lost workdays, either days away from work or days

of restricted activity

Deaths Sum of fatality wounded employees resulting of an unsafe act or the

workplace environment

7

2.2 Safety Leading Indicators

OSHA defines a leading indicator as a measure preceding or indicating a future event

used to drive and measure activities carried out to prevent and control injury (OSHA 2002a).

Safety leading indicators provide a pro-active approach for safety management with construction

companies (Hallowell et al. 2013). Although these indicators periodically monitor the safety

performance of construction workers, there are few methods to collect and analyze pro-active

data for safety (Hinze et al. 2013). The following three data types are examples categorized as

safety leading indicators:

Near Miss: Measurements of processes, activities, and conditions that define

performance and can predict future accidents (Hallowell et al. 2013).

Hazardous Proximity Zone: Area around a piece of construction equipment in

which a pedestrian worker is in danger due to the movement and functionality of

the piece of construction equipment

Site Layout Analysis: Belong to the Jobsite Hazard analysis which is a technique

that focuses on job tasks as a way to identify hazards before they occur (Hallowell

and Gambatese 2009, Rozenfield et al. 2010, OSHA 2002a).

2.2.1 Near Miss

The Occupational Safety and Health Administration (OSHA) defines a near miss as “an

incident where no property was damaged and no personal injury sustained, but where, given a

slight shift in time or position, damage and/or injury easily could have occurred” (OSHA 2002b).

OSHA currently does not require construction companies to record or report near misses (OSHA

2011b). Safety leading indicators, including near miss reporting and analysis, provide an

8

additional metric of employee safety performance without requiring an illness, injury or fatality

event.

The “Safety Pyramid” created by Heinrich provides the motivation for collecting and

analyzing near misses. Heinrich hypothesized that a multitude of near misses are a prerequisite

for a workplace injury or fatality to occur (OSG 2009). Consequently, safety managers desire to

identify and mitigate near miss events before an injury, illness, or fatality occurs. Other industrial

sectors including manufacturing, medical, and energy production have adopted near miss

reporting in an attempt to improve employee safety performance (Cambraia et al. 2010, Sullivar

and Sheffrin 2003, Henneman and Gawlinksi 2004, Schaaf and Kanse 2004). Private sector

companies in the UK are required to submit near miss reports as part of their safety records (HSE

2015).

Near misses are typically measured as single events or instances rather than hours of

exposure as with other hazards (Hinze and Godfrey 2003). Near misses should be quantifiable,

easily understood, perceived as credible, and signal the need for action (Hallowell et al. 2013).

Near miss reporting and analysis enables construction workers to be educated on strategies to

prevent future lagging indicators (Hinze 2002). Near miss reports provide opportunities for

safety managers to identify areas of improvement within safety performance (Hinze 2006). By

measuring and assessing near miss reports, safety managers strive to successfully prevent all

serious injuries (Huang and Hinze 2006, Hecker et al. 2005).

2.2.2 Human-Equipment Interaction

In 2014, the Bureau of Labor Statistics (CFOI 2015) reported the construction industry

experienced 899 fatalities of which 15% (135 fatalities) resulted from workers coming into

contact with objects or construction equipment. Since 2003, the construction industry has

9

averaged 191 fatalities resulting from construction equipment or other objects striking workers

per year (BLS 2015). Figure 1 provides the total construction fatalities and those causes by

ground workers contacted with objects or equipment between 2003 and 2014 (CFOI 2015).

Figure 1: Construction Fatalities Caused by Contact with Objects or Equipment

One longitudinal study identified minimal significant change in fatalities resulting from

contact collisions between construction equipment and ground workers between 1985 and 2009

(Hinze and Coates 2011). Although the number of fatalities resulting from contact collisions

decreased during this duration, the ratio when compared to total construction fatalities remained

largely unchanged. Even in highway work zones, more worker fatalities are caused by struck-by

events from pieces of construction equipment rather than commuting vehicles (Pegula 2010).

10

A majority of previous research in hazardous proximity situations is largely concentrated

in worker behavior. One study identified two general problems resulting in hazardous proximity

issues between heavy construction equipment and ground workers (Fosbroke 2004):

1) Workers and equipment operators: Outdated or never implemented policies, a lack of

knowledge of existing specific risk factors, and repetitive work tasks;

2) Incident investigation: All incident causation data is collected after-the-fact resulting

in no or limited real-time incident information.

Researchers have also attempted to implement technology to provide alerts to workers

during hazardous proximity situations and record proximity breaches for worker safety education

and training. Proximity detection and alert devices were deployed on construction equipment and

ground workers to evaluate their reliability and effectiveness to alert ground workers and

equipment operators when they were in too close proximity (Park et al. 2015). Several proximity

detection and alert devices have been evaluated for their ability to be implemented into various

industrial sectors to enhance safety (Kim et al. 2006, Ruff 2007, Teizer et al. 2010). Although

these systems are able to provide alerts during hazardous conditions, these systems are currently

incapable of accurately covering the designated hazard area.

Standards and regulations required by the Occupational Safety and Health Administration

(OSHA) are imperative to enhance safety in construction (OSHA 2013a). As per OSHA

regulations, construction equipment must provide alerts when moving in the reverse direction

(OSHA 2013b). Research has found that these alerts can desensitize workers to existing hazards

(Duchon and Laage 2011). Other OSHA regulations require construction site personnel to wear

hard hats, reflective safety vests, and other personnel PPE (OSHA 2013b). Safety training and

other required safety regulations can increase the awareness of hazards associated with proximity

11

issues between construction equipment operators and ground workers (Goldenhar et al. 2001,

Huang and Hinze 2006). Construction accident statistics indicate that back-up alerts and PPE are

incapable of preventing contact collisions between workers and construction equipment.

Furthermore, there are currently no regulations or recommendations for establishing a hazardous

area around a piece of heavy construction equipment. By creating this hazard zone around a

piece of construction equipment, ground workers can be reminded of the hazards that are

associated with being located in too close of a proximity.

2.2.3 Construction Site Layout

Construction site layout is composed of the existence, positioning, and schedule of

construction resources required to complete a construction project (Mawdesley et al. 2002). Due

to its immense impact on construction productivity, project schedules, and safety, a significant

amount of research has focused on construction site planning (Tam et al. 2002). Specifically, the

locations of tower cranes, material staging areas, and travel pathways have a meaningful impact

on construction productivity (Elbeltagi et al. 2001) and construction safety (Sadeghpour and

Andayesh 2015).

In 2013, cranes on construction sites contributed to 590 injuries and 21 fatalities

including 11 fatalities that were directly caused by a pedestrian being struck by objects or

equipment associated with cranes (BLS 2013). Cranes were the direct cause of 18% of

construction-related fatalities between 1992 and 2006 (McCann and Gittleman 2009).

Consequently, the proximity of pedestrian workers to cranes is regarded as a severe safety issue

(Teizer et al. 2008). The positioning and reach of a crane with respect to construction equipment

travel paths and pedestrian worker walking paths should be considered.

12

At the same time, construction site layout planning has a major impact on construction

productivity of operations, especially in limited site space situations (Elbeltagi et al. 2001). The

accessibility of constructed structures plays a significant role in productivity. The total operation

cost of transporting heavy materials can be minimized by locating the tower crane and associated

material supply points (e.g., material staging area) in desired positions (Huang et al. 2011). The

distance between facilities and a tower crane has been found to be essential to construction

productivity (Rodriguez-Ramos et al. 1983). Once located, it is typically not desirable to relocate

a tower crane due to impracticality or a significant cost and time required to relocate (El-Rayes

and Said 2009). It is estimated that the optimal positioning of a tower crane can eliminate 20% to

40% of a crane hook’s travel time (Zhang et al. 1996). To increase production, a crane’s jib

should reach and access any part of the constructed structure (Huang et al. 2011). The

optimization of a tower crane’s location at the onset of a construction project can drastically

impact productivity throughout a project’s duration.

2.3 BIM

Building Information Modeling (BIM) is an intelligent 3D model-based process that

equips architecture, engineering, and construction professionals with the insight and tools to

more efficiently plan, design, construct, and manage buildings and infrastructure (Eastman et al.

2011). BIM allows designers and contractor personnel to manage a project through the whole

building life cycle including designing (Penttila 2007), planning, construction (Kymmell 2008),

and operation (Akcamete et al. 2010).

The construction industry has recently started implementing technological systems to

promote safety. Safety application has been created within information and communication

technologies including BIM, virtual design, and Geographic Information Systems (GIS) (Bansal

13

2011, Kunz and Fischer 2009, Zhang et al. 2013). Other researchers have utilized the resource

tracking capabilities of Global Positioning System (GPS) to detect spatial conflicts between

construction workers and heavy equipment which can benefit from 4-Dimensional visualization

for identification of hazardous areas (Behzadan et al. 2008, Pradhananga and Teizer 2013).

Virtual reality technology was selected to simulate typical construction tasks based on actual

construction site conditions for hazard planning (Su et al. 2013). These advances and

applications of technological systems were implemented to assist in improving safety

performance on construction sites.

Construction project stakeholders are utilizing BIM to integrate many components of a

construction project including building life cycle (Azhar 2011), designing (Penttila 2007),

planning, construction (Kymmell 2008), and operation (Akcamete et al. 2010). Several advances

in applications for BIM platforms exist such as 4-dimensional structural clash detection

(Eastman et al. 2011) and automated safety rule checking (Kymmell 2008). These enhancements

enable stakeholders to view project data and generate analysis information in real-time. This

equips project stakeholders to improve decision-making through increased accuracy and

locational awareness of valuable project information.

Past researchers have exploited communication and data analysis capabilities of BIM for

improving safety. Safety concerns in structural design including element conflicts have been

visualized and alleviated in 4-D environments (Zhang and Hu 2011, Hu et al. 2008).

Construction safety researchers have integrated topics of Prevention-through-Design (PtD) into

BIM by creating a tool to evaluate construction worker safety during a project’s design phase

(Sulankivi et al. 2010). Furthermore, BIM has been established as a safety data collection

platform with PtD applications (Hadikusumo and Rowlinson 2004). Other researchers created a

14

framework for simulating and identifying hazards by linking BIM software and a virtual reality

environment (Park and Kim 2013).

2.4 Research Needs Statement

The disproportionately high number of fatalities requires an improvement of safety

engineering and management in the construction industry. Safety leading indicators allow

workers to be able to recognize potential hazards before a lagging indicator (injury, illness or

fatality) occurs. Safety leading indicators can be better managed by the platform of BIM with the

capabilities of real-time and 3D data visualization. Because the location of an event has been

identified as a requirement for identifying and mitigating hazards in a visualization environment

(Chantawit et al. 2005), the following are needed research areas for location-based safety leading

indicators in BIM:

A framework for near miss data collection and visualization;

Automated creation of hazardous proximity zone of heavy equipment for workers’ safety;

A framework and optimization tool to locate the optimal position for placing a tower

crane on a construction site.

15

CHAPTER 3 RESEARCH METHODOLOGY AND RESULTS

This chapter defines the foundational components for this research and describes in detail

the methodology for each of the research components. Section 1 discusses the research objective,

scope and hypothesis. Section 2 explains in detail each methodological step required for near

miss reporting. Section 3 discusses the required steps to create hazardous proximity zone for

heavy equipment on construction jobsites. Location optimization of tower cranes was discussed

in Section 4. Corresponding implementation strategies with case studies were also described.

Methodology required to integrate gathered knowledge into an improved construction safety plan

using location-based leading indicators was discussed in the summary of Section 5.

3.1 Research Objective, Scope and Hypothesis

The primary objective of this research is to create a framework to collect, analyze, and

visualize select location-based safety leading indicators through BIM. The research methodology

to achieve the stated objective is divided into three major components: 1) near miss reporting, 2)

hazardous proximity zone and 3) site location optimization.

Location-based data collected for this research is two-dimensional (2D) and three-

dimensional (3D) depending on the available location information. The scope of this research

includes private construction companies and government entities performing construction in the

U.S.. Information gathered from data sets of private construction companies contains any

proprietary information. This research focuses on managing several location-based safety leading

16

indicators in order to best utilize the 3D visualization environment of BIM for the purpose of

improving construction safety.

After reviewing existing research and current practices regarding safety leading

indicators in the construction environment as well as the technology of BIM, the following

hypotheses were generated by the researcher and were tested using the research methodology

described in the following sections:

Location-based data from construction sites can be collected, analyzed, visualized and

queried in BIM. Safety data visualization, and BIM can assist safety managers and

workers to identify and mitigate potential hazards;

The hazard proximity zone of heavy equipment can be created and visualized in BIM to

increase hazard awareness for workers;

The location of tower cranes can be optimized for safety through the BIM.

3.2 Near Miss Reporting for BIM

A near-miss data visualization tool was created which utilizes a methodology of

information flow from user input (either by manual input or from an external database) to the

near miss database and can be output through three different trajectories: 1) Queried data at the

request of the user, 2) visualization within the BIM in 2D or 3D viewpoints and 3) exported to

external databases. The user-interface and database communicate via programming codes

specifically for near-miss data visualization and management. The flowchart for this information

is shown in Figure 2.

17

Figure 2: Filtering Capabilities of Reported Near Misses

This visualization tool is functional in a commonly used commercially-available BIM

software for construction design and project management. The tool was created using the open

Application Programming Interface (API) within a widely used BIM software. The API aided in

creating a new set of applications to create and manipulate near miss data. A commonly-used

multi-paradigm programming language (i.e. C#) was used to create and customize the near miss

data visualization tool user interface. Codes snippets can be viewed in Appendix C. Table 3

displays the functions of the user-interface created by coding and existing algorithms within the

API.

Table 3: User Interface Functions

Algorithm Description

Ribbon Add new applications to the software’s original interface

Select Choose or select the near-miss event in 2D or 3D view

TaskDialog Provide warnings or allow end-users to input information

Event Enable a function to notify other functions when something of interest

occurs (e.g. clicking a tab invokes the function of making a query)

Filtering Make a query based on a certain criterion

Windows Forms Create the near-miss report

18

3.2.1 Near Miss Information Visualization in BIM

A near-miss data visualization tool was created to improve decision-making for safety

managers and other construction project stakeholders by accessing and analyzing near-miss

information within an active BIM. The usability of the tool was designed for personnel not

necessarily familiar with the intricacies of a design model such as BIM. For example, all user-

interactions with the tool are simply decision criteria and data entry that require basic computing

skills. The following sections describe the functionality of the near-miss data visualization tool

and discuss specifically how near-miss information would be viewed and analyzed with a BIM.

A functional interface for the BIM near miss visualization tool was created for end users

to facilitate the input and data analysis of near miss information. The user-interface enables

safety managers to do the following functions: 1) create a near miss report, 2) view and edit

information for each existing report, 3) visualize the spatial location of a near-miss report, and 4)

filter near misses by various parameters. To create a near miss, the user accesses the “Near Miss”

tab with a BIM platform as shown in Figure 3. Using this initial interface, the user can create a

near-miss report, assign a severity value, access information of previously reported near misses,

and query near-miss information.

Figure 3: User interface initial screen for the near-miss visualization tool

In total, 22 different categories are available for data entry including the following: Date

and time of the near miss, a unique near-miss identification number, project name, company

19

name, crew involved, employees involved, event description, associated tasks and reviewer

names and dates reviewed. Other available input categories are described in Table 4.

Table 4: Select Categories for Near-Miss Data Input

Category Description

Identification

Classification and

Category

Near misses can be classified based on their cause using the Eindhoven

classification technique that was adopted for the construction industry

(Marks et al. 2014, Kaplan et al. 1998, McKay 2013)

Investigative

Team

Record the members of the near-miss investigative team for each near

miss reported and investigated

Severity User ranks the severity of a near miss on a company-specific scale or

using relative measures to historical near misses. The scale allows for a

range of 3 different severity scoring options.

Root Cause The “chain-of-events” that led to the reported near miss

Contributing

Cause

Any other events or set of conditions that led to the reported near miss

that were not identified as the root cause

Resolution

Description

A summary of the mitigation strategy developed by the investigative

team to be implemented and observed

Picture Users can upload a picture taken of the location of the near miss to

observe existing and surrounding conditions

These categories are assembled in a similar database template created by the Construction

Industry Institute (Marks et al. 2014). Information within this template for each near miss can be

viewed and manipulated within a BIM at the near-miss location. The near-miss template is

shown in Figure 4.

20

Figure 4: Information template for reported near misses within BIM

The created tool allows for near misses to be reported through two different methods: 1)

users can input near-miss information manually, or 2) users can identify a location within a BIM

and start the information input process based on location-based data of the individual near miss.

This location-based data entry within a BIM can be achieved in both 3D and 2D views. In the

prototype shown in Figure 5, a small red sphere represents the location of the near miss. This

figure also shows the location of a near-miss report shown in both 3D and 2D viewpoints.

21

Figure 5 :(a) 3D and (b) 2D view of the near-miss sphere in circle

A database linking input information with the existing model is established. Most BIM

platforms provide query functions that will be accessible by the created visualization tool. The

query capabilities can be exported into common file formats for further analysis by other

software programs if so desired by the user. Likewise, previously generated databases of near

miss information can be uploaded into the BIM platform database for visualization and analysis

using the created tool. For this transition to be effective, external input database information

should be formatted such that each information category fits with the template within the BIM

near-miss visualization tool. A sample screenshot of an exported near-miss database created

using the BIM near-miss visualization tool is displayed in Figure 6.

22

Figure 6: Exported near-miss database (Partial)

Filtering collected near-miss data is an important component to transitioning collected

data into usable information for safety managers. The created user-interface allows for user

filtering of near-miss data by severity level, near-miss identification, time of day, equipment

involved and other quantifiable project specific parameters input by the user. This data filtering

will assist safety managers and other project stakeholders to identify high severity near-miss

reports for immediate mitigation or group only near misses associated with a particular root

cause. Users can also search for a specific reported near miss by filtering by identification

number. Figure 7 shows the user-interface display when implementing a query for a near-miss

data set.

23

Figure 7: (a) Filtering capabilities of reported near misses by severity;

(b) Searching near miss by unique identification number

3.2.2 Implementation Strategy

A prerequisite for implementing and maintaining the near-miss data visualization tool is a

fully integrated and functioning BIM for the design and construction process. Another

prerequisite for experiencing success using the near miss data visualization tool is to have a fully

implemented safety program that assesses safety performance based on safety leading indicator

metrics including near misses. Without an active and foundational safety program, collecting and

analyzing near-miss data would be premature. The near-miss visualization tool can be linked as

an additional component to many commercially-available BIM software and existing database

software.

Depending on the company’s specific near-miss reporting program, the safety personnel

and other managers should provide adequate training to all site personnel concerning the near-

miss reporting process. Training materials and time for the near-miss data visualization tool

should be limited to personnel with accessibility to BIM. Effort levels of training for the tool can

24

be tailored towards the specific user. For example, crew supervisors collecting near-miss reports

may not necessarily be interested in how to link external near-miss databases to the BIM. Near-

miss reporters should experience feedback from their near-miss report to encourage future

reporting.

A BIM model from an active construction site was used to test and validate the prototype

near-miss data visualization tool. The BIM software that was used for both the model and near-

miss data visualization tool is Autodesk Revit 2015. A list of actual near-miss events was used to

validate the visualization and other functionality of the created near-miss reporting tool. The near

misses were entered into the visualization tool through both methods:

1) Manual entry through the user-interface (single event entry)

2) Link the external database to the created tool (multiple-event entry)

Each near-miss event was verified if it appeared in the BIM and the corresponding near-

miss information was accessible. Information was queried based on quantifiable data provided in

the created near-miss template. The events were queried by severity, data, company, project, task

associated and near-miss identification number.

3.2.3 Case Study

A sample BIM was used to test and validate the prototype near miss-data visualization

tool. The model was used to create a design, facilitate the construction process, and maintain the

facility during operation of an engineering building on the University of Alabama’s campus in

Tuscaloosa, Alabama. The BIM software used for both the model and near-miss data

visualization tool was Revit. A list of 20 randomly generated near-miss events was structured for

visualization. Information for these near-miss events followed the template shown previously in

25

Figure 4 so that each category was populated. The near misses were entered into the visualization

tool through both methods:

1) Manual entry through the user-interface (single event entry)

2) Link the external database to the created tool (multiple-event entry)

Each near-miss event was verified that it appeared in the BIM and the corresponding

near-miss information was accessible. Information was also queried based on quantifiable data

provided in the created near-miss template. The events were queried by severity, data, company,

project, task associated and near-miss identification number. A similar methodology was used

for another BIM used for an engineering building new construction. The resulting near-miss

visualization within the model is shown in Figure 8.

Figure 8:Near-miss events visualized within a BIM

26

Results from the case study indicate that near-miss events can be captured and visualized

through BIM. New methods were developed to populate and automatically visualize near-miss

information. The near-miss data visualization tool has been successfully implemented on several

real project models and all created capabilities were demonstrated. The conducted research

illustrates that collected safety data can be visualized and accessible to all project stakeholders by

integrating with BIM.

3.2.4 Expert Panel Review

Seven safety managers for construction companies were provided a demonstration of the

created near-miss visualization tool. Members of the expert review panel were all Certified

Safety Professionals (CSP) and had a minimum of eight years of experience as a safety manager.

The safety managers were all employed by construction companies with a minimum of 1,700

employees. The OSHA Total Recordable Incident Rate (TRIR) of these companies ranged from

zero to two with an average value of 0.6.

Each participant was provided a description of the study, a description of the created tool,

and a demonstration of the tool in the BIM shown in Figure 3. The demonstration included

instructions and presentations of all available functions of the near-miss visualization tool.

Additionally, several near-miss reports were randomly generated to demonstrate query functions

and near-miss visualization within the model. After the demonstration, expert review panel

members discussed the feasibility and functionality of the tool as well as respond to survey

statements about the created tool with the research team. This survey can be viewed in

Appendix A.1 of this report. For each statement, panel members were asked to score their

perceptions and understanding of the tool based on the following scale: 1 = strongly disagree, 2 =

27

disagree, 3 = unsure/neutral, 4 = agree and 5 = strongly agree. Table 5 provides a statistical

analysis of each answered question.

Table 5: Safety Expert Review Panel Responses

Posed Statement Description Minimum

Score

Maximum

Score

Average

Score

A need exists to integrate safety data in existing project

management tools

3 5 4.6

It is useful for safety managers should visualize safety

information in 3D models

3 5 4.2

The proposed tool would be easy to use 3 5 3.6

The proposed tool is implementable in my company 3 5 3.8

The proposed tool would enhance near miss reporting 2 5 3.4

The proposed tool would be effective in safety data

management

3 5 3.8

The proposed tool would improve safety management 2 5 3.8

A majority of the safety expert review panel members scored favorably (e.g. agreed or

strongly agreed) with all posed statements including that the proposed tool would improve safety

management, the tool is implementable, and the tool would be easy to use. One barrier identified

by the safety manager is the learning curve and implementation of technology that has proven

difficult for this panelists’ company. This panelist gave the only “two scores” (e.g., disagree)

received in the study.

Members of the safety expert review panel also discussed the tool post-demonstration.

Statements that were repeated and agreed by a majority of the panel are recorded in the

following:

Members identified the value of integrating database capabilities of BIM with safety data

The tool is valuable and could greatly assist companies already doing a satisfactory job of

generating and collecting near-miss reports

28

The tool gives safety managers an easy way to visualize locations where the majority of

incidents are occurring

The concept would be beneficial not only to safety professionals, but to anyone

associated with project management

3.3 Hazardous Proximity Zone

3.3.1 Hazardous Proximity Zone Design for Heavy Equipment

Several variables and situations were considered when calculating the hazard zone

around a piece of construction equipment for a ground worker. The variables required to

calculate a hazard are equipment travel speed, equipment travel direction, equipment physical

dimensions, equipment turning radius, equipment rotational capabilities, operator reaction time,

and equipment braking distance. To create a process for determining the hazardous zone around

a piece of construction equipment, a hierarchy was designed to assure each variable was

considered. Figure 9 presents the methodology implemented to determine the hazard zone for

any piece of construction equipment. Each step is described in detail following Figure 9. A

typical three-axle, tandem rear axle, 23,586 kg haul capacity dump truck is used as an example to

demonstrate how each step is calculated and how the resulting hazard zone is determined

(Harwood 2003, Peterbilt 2014).

Figure 9: Steps for determining the hazardous zone around a piece of construction equipment

29

Step 1: Equipment Footprint -- The outermost position from the centroid of the piece of

construction equipment should be determined for all of the equipment components. To determine

the equipment footprint, the outermost extension at any height for each point 360 degrees around

the equipment should be projected onto a 2D (two dimensional) horizontal plane. The equipment

footprint for a typical three-axle dump truck is shown in Figure 10. The footprint is derived from

the equipment dimensions of a typical three-axle, tandem rear axle dump truck (Harwood 2003,

Peterbilt 2014).

Figure 10: Equipment footprint for a dump truck

Step 2: Initial Safety Boundary -- The initial safety boundary is a two meter distance extended

from the equipment footprint that performs as a safety factor in the event that other hazardous

zone design steps fail to provide protection. A parallel line two meters offset from the equipment

footprint calculated in step 1 creates the initial safety barrier (see Figure 11). The initial safety

boundary was assigned a two meter value to account for a ground worker that may have body

parts (e.g. limbs) horizontally extended (e.g. a worker with an outstretched arm). This safety

boundary extends beyond the length of a person’s horizontally extended arm or leg as well as a

30

person bending horizontally at their torso. This initial safety boundary zone can be modified

depending on a unique set of working conditions.

Figure 11: Initial safety boundary for a typical three-axle, tandem rear axledump truck

Step 3: Equipment Function -- The hazard zone must be designed to align with the specific

function of a piece of construction equipment. For example, the equipment function of a

hydraulic excavator with tracks and resulting hazard zone mainly follows a circular pattern with

the maximum hydraulic arm reach as the circle’s radius. Other pieces of construction equipment

such as a dump truck or scraper will have a hazard zone largely based on their turning radius and

maximum speed on a construction site. The equipment function is taken into consideration to

determine the hazard zone for a typical three-axle dump truck as shown in Figure 12. The area in

which material will fall from the dump truck is contained within the hazard zone. Most pieces of

construction equipment provide specific details about turning radius in the equipment

specifications (Peterbilt 2014).

31

Figure 12: Hazard zone based on dump truck function on a construction site

Step 4: Operator Reaction Distance - This metric is used to determine the travel distance of a

piece of construction equipment during the period in which a construction operator reacts to the

identification of a hazard. An equation typically used for commuter traffic driver reaction time is

implemented to determine the equipment operator reaction time (see Equation 1). An average

operator reaction time of 2.5 seconds to a recognized hazard is utilized (MUTCD 2013). This

value represents the average adult observation time and reaction time (MUTCD 2013). It is

assumed that the reaction time of a commuter vehicle operation is empirically similar to

construction equipment operations. Although the surface terrains can vary from typical highway

32

surfaces to construction sites, the equations provide a valid estimation for reaction distance of an

operator and piece of construction equipment. The resulting reaction distance is plotted at

intersecting points along the existing hazard zone from the previous step 3.

Reaction Distance = 0.278Vt Equation 1

where:

V = velocity and t = time (2.5 s)

Step 5: Braking Distance -- The braking distance of the piece of construction equipment is added

to the operator reaction distance as an additional factor in determining the hazard zone. To

calculate the braking distance, an equation typically used to measure the braking distance of

commuter traffic (including semi-trucks) is used and shown in Equation 2. After the operator

identifies a hazard, a separate reaction time is required for the operator to apply brakes to stop

the equipment. The same average operator reaction time of 2.5 seconds is used as was discussed

in step 4 (MUTCD 2013). The resulting braking distance is plotted at all locations extended

outward from the reaction distance calculated in step 4.

Braking Distance = 0.039(2*V/a) Equation 2

where :

V= velocity and a=acceleration (3.41 m/s2)

Step 6: Determine Hazard Zone - To determine the resulting hazard zone, steps 1 through 5

should be calculated and compared to identify the maximum calculated distance for each point

33

around a piece of construction equipment. It is important to note that each step of determining

the hazard zone builds from the previous step. For example, the initial safety boundary calculated

in step 2 is added to the maximum extent of the equipment footprint for each point calculated in

step 1. The maximum calculated hazard distance along the centerline of forward travel for the

dump truck is the combined reaction time and braking distance of 6.3 meters. This value was

added to the equipment function and initial safety boundary and equipment footprint that were

calculated in step 3 and step 2 respectively. The resulting hazard zone for a typical three-axle,

tandem rear axle dump truck is presented in Figure 13.

By following these six steps, a hazard zone can be created around a piece of construction

equipment. This hazard zone represents an area in which ground workers should avoid in order

to prevent hazards and potential injury.

Figure 13: Hazard zone of a typical three-axle, tandem rear axle dump truck

The hazard zone for several pieces of construction equipment was created using the

methodology presented in the previous section. Dimension values for a typical backhoe loader

were used to create a hazard zone for ground workers. The backhoe loaded used was a single tilt

34

loader and was equipped with a multi-purpose 1.0 cubic meters (1.3 cubic yards) front loading

bucket (Caterpillar 2011). The resulting hazard zone for the backhoe loader is presented in

Figure 14.

The hazard zone for an excavator was also created using the previously discussed

methodology. The excavator used was a typical three teeth digging 0.018 cubic meters (0.29

cubic yards) capacity bucket (Caterpillar 2011). The resulting hazard zone for the excavator is

displayed in Figure 15.

Figure 14: Hazard zone of a typical backhoe loader

Resulting hazard zone area data was also calculated based on the creation of the hazard

zone for the dump truck, backhoe loader, and excavator. The net hazard area is calculated from

subtracting the area occupied by the equipment (B) from the full hazard zone area (A). This net

hazard area is the region that should be avoided by construction ground workers during

equipment operation. The resulting hazard zone data is shown in Table 6.

35

Table 6: Hazard Zone Results for Each Piece of Evaluated Equipment

Item Dump Truck Excavator Backhoe Loader

Area of full

hazard zone (A) 169.8 m2 803.7 m2 179.9 m2

Area occupied by

equipment (B) 26.5 m2 23.1 m2 21.0 m2

Net hazard zone

area (A - B) 143.3 m2 780.6 m2 158.9 m2

Figure 15: Hazard zone of a typical excavator

3.3.2 Automatic Creation of Hazardous Proximity Zone in BIM

A user interface was created in AutoCAD to allow safety manager and other site

personnel to alter variables with creating the construction equipment hazard zone. Users are able

36

to select pieces of construction equipment and provide their unique parameters for the hazard

zone. By using Visual Basic for Applications (VBA) and the parametric design function

provided by AutoCAD, a hazard zone around a piece of construction equipment was

automatically generated and available for manipulation within other BIM software applications.

Several input parameters are required including equipment length, width, turning radius, and

desired initial safety boundary distance (Figure 16).

Figure 16: User interface for hazard zone creation

3.3.3 Implementation Strategy and Validation

The hazard zones for several pieces of construction equipment were created using the

methodology presented in the previous section. Available equipment for automatic hazard zone

creation include: 1) dump truck, 2) excavator, 3) backhoe excavator, 4) dozer, 5) wheel loader,

6) scraper, 7) motor grader, 8) forklift, 9) skid steer loader, and 10) compactor. These were

chosen because they were cited as frequently involved with human-equipment interaction

incidents on construction sites (Hinze and Teizer 2011). Figure 16 displays the user interface for

creating the hazard zone. Figure 17 shows the geometries of backhoe excavator with its

37

associated hazard zone in AutoCAD, after users input necessary parameters/variables in the

created interface. In Figure 17, red lines represent the footprint of equipment, while white

represent the hazard zone boundary. The full list of available equipment’s geometries can be

viewed in Appendix B.

Figure 17: Hazard zone of backhoe excavator (also called rear actor) in AutoCAD

Location tracking data of construction resources including personnel and equipment has

been performed in previous research efforts (Pradhananga and Teizer 2014). For this study, an

active construction site was selected with an earthmoving operation involving excavators,

dozers, rollers, dump trucks, and construction ground workers. Position-based datasets were

collected using GPS identification devices calibrated to a frequency of 1 Hz mounted on various

surfaces on pieces of construction equipment and to the hard hats of workers. The data was

subject to being filtered using an existing filtering process (Vasenev et al. 2014).

Figure 18 shows the result of verifying the proposed hazard zone with location-based

data from the construction site. Tracking data for the trajectory of one trip cycle of a single

equipment started from the point it entered the construction site and ended when the truck passed

the exit point. A hazard zone was created around the selected heavy construction equipment for

its position, every second, based on the previously prescribed methodology.

38

Figure 18: Verification of the proposed hazard zone (all measurements in meters)

The tracked dump truck is expected to stay within the computed hazard zone. The

equipment footprint of the dump truck with respect to hazard zone computed for its immediate

previous position was checked for compliance with the hazard zone (For example did the dump

truck remain in the zone after movement?). The boundary of the equipment footprint should

completely lie inside the hazard zone if the computed hazard zone is adequate for the piece of

equipment. In Figure 18, the portion of the truck lying inside the hazard zone is plotted in red.

The gray area represents the hazard zone.

Instances in which the dump truck remained static were excluded from the analysis. A

total of 106 location data points were assessed. Among those points, the equipment footprints

was found to cross the hazard zone boundary 33 times. This implies that the computed hazard

zone was able to envelop the dump truck’s movement approximately two-thirds of the times.

Figure 18 also shows that red areas were only present in cases where the truck performed

forward sharp right turns. Following the analysis, a recommendation on extending the hazard

zone to include very sharp right turns can be implemented. The same methodology for creating

39

hazard zones can be used to iterate through each step to optimize the hazard zone for an

equipment. Safety managers and construction equipment operators are encouraged to further

modify the hazard zone based on their specific experiences on the construction site.

3.4 Site Location Optimization

3.4.1 Optimization of Tower Crane’s Location in BIM

A simulated construction site was designed in Autodesk Revit software to create the

tower crane optimization tool. The designed construction site included one building under

construction, one material staging area, single and multiple travel pathways for construction

equipment and pedestrian workers. All components on the construction site were simplified to

represent orthogonal three-dimensional rectangular spaces. The radius of the tower crane boom

was assumed to be 70 m (200 ft). The two-dimensional planar surface areas of each construction

site component is shown in Table 7.

Table 7: Dimensions of Created Construction Site

Site Component Length Width

Exterior fence 91.4 m (300 ft) 91.4 m (300 ft)

Material staging area 45.7 m (150 ft) 27.4 m (90 ft)

Building boundary 37.2 m (122 ft) 18.9 m (62 ft)

Travel path 61.0 m (200 ft) 3.0 m (10 ft)

40

Figure 19: Simulated typical construction site layout

Figure 19 shows a screenshot of the construction site modeled in BIM design software.

Figure 20 shows the created interface to input user data concerning the known construction site

parameters. The interface enables the user to do the following functions:

1) Input coordinates of the construction site components by clicking each intersection

point for the shapes that outline the exterior boundary, building in boundary, material

staging area and travel path within a model;

2) Automatically select the optimal location of the tower crane;

3) Visualize the tower crane location in a model;

4) Output site layout coordinates a text file

41

Figure 20: Crane location optimization interface

A polygon was created based on the points designated by the user, and the intersection

points and resulting surface area are exported into a text file which is read by MATLAB to create

a binary image of the construction site. The exported binary image of the construction site in

MATLAB is shown in Figure 21. The shaded region in Figure 21 denotes locations available to

placing the tower crane and the unshaded area shows areas unavailable for placing the tower

crane. Each pixel in the Figure 21 represent one meter in the field.

Figure 21: Construction site layout in MATLAB by binary image

42

The goal of the optimization tool is to ensure that the tower crane boom sweep area can

cover most of the constructed building and material staging area while minimizing the boom’s

coverage of the travel pathway. These parameters are typical of decision criteria for management

personnel on construction sites and have been identified as performance metrics in previous

research. The detailed decision criteria framework used to achieve this optimization solution is

shown in Figure 22. The technical computing language and interactive software MATLAB was

used to convert the existing site layout with binary images in which one pixel represents one

square meter. For a 100 meter by 100 meter sized construction site, 10,000 pixels are required.

The algorithm identified unavailable pixels including travel paths, buildings and material staging

areas for the optimization calculations. This framework details how user input data is collected

and analyzed to optimize the two-dimensional location of the tower crane.

Figure 22: Logic framework for optimized decision criteria

43

Two major constraints are implemented to mediate the optimization of the tower crane

location on a construction site. The first and most prioritized constraint implemented by the

optimization algorithm is that the tower crane boom must maximize the access to the constructed

building and material staging area. An estimated threshold of 95% coverage area of constructed

building and material staging area by the tower crane was selected for experimental trials. A user

can modify this threshold for specialized construction site conditions. As the optimization

threshold decreases, the number of available locations for placement of the tower crane

increases. Because all projects are unique and have varying criteria for coverage area of a tower

crane, management personnel are able and encouraged to modify the coverage area threshold

based on their specific project needs using the created user-interface. If this threshold is satisfied,

the optimization algorithm implements a secondary constraint based on the available locations

that satisfy the initial constraint. Figure 23 provides details and prioritizations of each constraint

within the tower crane optimization tool. For constraint 1 in Figure 23, the shaded area

represents the area that should be accessible by the tower crane boom. The shaded area in

constraint 2 in Figure 23 is the area that should be avoided by the tower crane boom.

44

Figure 23: Details and prioritization of constraints within the tower crane optimization tool

The two constraints shown in Figure 23 in the algorithm of the tower crane location

optimization tool were applied to the modeled construction site shown in Figure 19. After

exporting user input data from the tool’s interface into MATLAB, the tower crane’s two

dimensional coordinates were computed based on an optimization function and the two defined

productivity and safety constraints. Figure 24 shows the resulting tower crane boom coverage

area and coordinates for the optimized crane’s tower.

45

Figure 24: Computed tower crane boom coverage area

The percentage of tower crane boom swing area of the constructed structure, material

staging area and travel paths are shown in Equation 3 and 4. The calculated solution of each of

these equations is automatically stored in a text file and used to identify the tower crane location

in BIM.

PCBM = (NWP1 / TNWP1)(100%) Equation 3

where:

PCBM is the percent tower crane boom covers of building and material staging area; NWP1 is

the number of white pixels the tower crane boom overlaps the building and material staging

area; and TNWP1 is the number of white pixels that cover the building and material staging

area

PCT = (NWP2 / TNPW2)(100%) Equation 4

where:

PCT is the percent tower crane boom covers the travel path; NWP2 is the number of white pixels

the tower crane boom overlaps the travel path; and TNWP2 is the number of white pixels that

cover the travel path

46

The final step of the interface is for a user to select “Show tower crane location”. Once

this selection as been made, the optimized 2D location of the tower crane is shown in the BIM

model and the (x,y) coordinates of this location are provided as shown in Figure 25. The area

inside of the dashed circle and construction site boundary is the area accessible by the tower

crane boom. All objects inside of this circular area can be reached by the tower crane boom. The

optimized tower crane location shown in Figure 25 provide a 99.3% tower crane boom coverage

area of the building and material staging area and a 29.5% coverage of the equipment and

pedestrian travel path.

Figure 25: Tower crane optimization location resulting output

The tower crane optimization tool provides three categories of output: 1) the visual

display of the optimum location for a tower crane; 2) the (x,y) coordinates of the optimized

tower crane location; and 3) the tower crane boom coverage area of the building, material staging

47

area, and travel path at the optimized location. Figure 26 details the individual objective

functions of algorithm to compute the optimized tower crane location through the BIM (Revit),

the text file, and MATLAB. This data transfer organizational structure allows for construction

safety personnel to receive results simply by navigating the created user interface in an existing

BIM.

Figure 26: Relationships between optimization tool functions in BIM, text file and MATLAB

3.4.2 Case Studies and Validation

The created tower crane optimization tool for construction sites was validated internally

to assess the robustness of the tool and externally through implementation of a case study.

Internal validation verified the robustness of the algorithms and functions of the tower crane

optimization tool while external validation implemented the created optimization tool on active

construction sites to evaluate the feasibility of actual use in the construction industry.

Specifically, this validation strategy evaluates the functionality of the created optimization tool,

that is, tests that verify the algorithms within the tool are capable of calculating and identifying

48

the optimal tower crane location based on input parameters. The following section details the

methods and results from both validation studies.

Internal Validation:

An internal validation effort was completed to assess the functionality of the created

tower crane optimization tool. The functionality refers to the ability of the created optimization

tool and algorithms to calculate and identify the optimal location of a tower crane based on a set

of input parameters. The performance metrics for this validation effort are based on success or

failure of the tool to identify the accurate location of the tower crane. The tower crane location

optimization tool was internally validated through 50 independent trials. These trials were

deemed necessary as an internal validation method for the functionality of the created tower

crane location optimization tool. Each trial consisted of a random and unique construction

layout of the constructed building, material staging area, and multiple travel paths within the

construction site layout shown in Figure 19. The dimensions of the travel paths remain constant,

but the dimensions of the constructed building and material staging area vary randomly with

each trial. The percent coverage area of the tower crane boom over the constructed building and

material staging area and the percent coverage of the tower crane boom over the travel paths are

both 1) automatically computed by the created optimization tool, and 2) manually calculated

through basic geometric tools in BIM design software. The trial was deemed successful if the

automated and manual calculation methods were identical and if both the optimization criteria

are met. As previously stated, the optimization criteria are: 1) the percent coverage of the tower

crane boom over the constructed building and material staging area is greater than 95%, and 2)

the percent coverage of the tower crane boom over the travel paths is minimized. In every trial,

calculations between the automated and manual calculation agreed as well as both optimization

49

constraints were satisfied. Figure 27 presents the results of several sample trials including a plan

view of the site layout, a plan view of the tower crane boom coverage area, and the optimization

constraint metrics.

Figure 27: Results summary of internal validation results

A statistical analysis of the internal validation results was also performed on the two

measured optimization constraints. Results of this analysis are shown in Table 8.

Table 8: Statistical Summary of Internal Validation Results

Statistical Metric PCBM PCT

Average 97.36% 29.45%

Minimum 95.20% 0.00%

Maximum 100% 59.71%

50

External Validation Case Study:

Two case studies were performed to implement the created tower crane optimization

framework into active construction sites. The existing tower crane locations for both case studies

were selected based on judgment and intuition of the general contracting company’s project

managers.

The first case study was a new classroom building construction project on a public

university campus located in the Southeastern U.S. The project started in August of 2012 and

was completed in May of 2015. This construction site was designed in a commonly used BIM

software (Autodesk Revit). Construction stakeholders accessed the BIM throughout the design

and construction phase of the project to assess and communicate concepts and issues. The tower

crane optimization tool was used by an impartial user on the existing BIM to determine the

optimal tower crane location based on the input construction site layout polygons and previous

stated optimization constraints. Figure 28 shows a picture of the construction site and the BIM

model including the current placement of the tower crane. The tower crane’s actual location

provided 76.5% coverage area for the constructed building and material staging area and covered

24.0% of the equipment and pedestrian travel path.

Table 9: Dimensions of the Simple Case Study Construction Site

Site Component Length Width

Exterior fence 106.7 m (350 ft) 106.7 m (350 ft)

Material staging area 30.5 m (100 ft) 27.4 m (100 ft)

Building boundary 65.5 m (215 ft) 32.0 m (105 ft)

Travel path 70.1 m (230 ft) 4.6 m (15 ft)

51

Figure 28: Active construction site (simple case study) for external validation (a) and plan view

of the BIM model with the actual tower crane location (b)

The construction site had one main equipment and pedestrian travel path, one building

under construction and one materials staging area. Table 9 provides the dimensions to each

element needed for the optimization tool in the actual construction site.

Outputs of the tower crane optimization tool identified a location to position the tower

crane which was different that the position selected by site personnel. Although it was

impractical to change the tower crane location due to productivity concerns, project management

personnel unanimously agreed to implement the tower crane optimization tool for their next

construction site layout pre-planning process. The optimization tool’s tower crane location

increased the percent of coverage area of the tower crane boom over the constructed building and

material staging area from 76.5% (actual tower crane location) to 98.4% (calculated optimized

tower crane location) and reduced the tower crane boom coverage area over the travel path from

24.0% (actual tower crane location) to 0.0% (calculated optimized tower crane location). The

52

optimal location in regards to the applied productivity and safety constraints is shown in Figure

29.

Figure 29: Output of tower crane optimal position tool for external validation construction site

(simple case study)

The second case study was a construction site for a children’s hospital located in Akron,

Ohio. The construction project duration was from April 2013 to August 2015. The site consisted

of a total of three travel paths for construction equipment and pedestrians, one building, and two

material staging areas. Table 10 provides the dimensions to each element required for

optimization of this active construction site.

Table 10: Dimensions of the Complex Case Study Construction Site

Site Component Length Width

Exterior fence 124.1 m (407 ft) 106.6 m (349 ft)

Material staging area (1) 64 m (210 ft) 33.3 m (108 ft)

Material staging area (2) 60.2 m (196 ft) 45 m (148 ft)

Building boundary 78.2 m (256 ft 68.7m (225 ft)

Travel path (1) 100.5 m (330 ft) 4.6 m (15 ft)

Travel path (2) 80.3 m (263 ft) 4.6 m (15 ft)

Travel path (3) 70 m (230 ft) 4.6 m (15 ft)

53

Because the created algorithms were created to optimize the location of one tower crane,

the user divided the construction site into two zones depending on the location and desired reach

locations of each tower crane. This practice is typical on construction sites largely due to

improving productivity and safety between tower cranes. Figure 30 presents a screenshot of the

tower crane optimization tool output in BIM (part a) and the actual construction site (part b). In

the BIM (part a) of Figure 30, the vertical dotted line denotes the cutting plane line implemented

by the created optimization tool. The user can select this cutting plane line, deploy the

optimization tool, and then combine results of each construction site component.

Figure 30: Output of tower crane optimization tool in BIM (a) and screenshot (b) of construction

site (complex case study)

When compared to the actual selected locations of the two tower cranes shown in Figure

30, the optimization tool’s tower crane location increased the coverage area of the tower crane

boom over the constructed building and material staging area by 7.8%. The combined tower

crane boom coverage area over the travel paths were reduced by 16.8% when changing the actual

location of the tower cranes to the locations selected by the tower crane optimization tool.

54

3.4.3 Expert Panel Review

Five construction managers in the industry were given a questionnaire interview about

the top constraints that would influence the selection of tower crane’s location. The interview

questions can be viewed in Appendix A.2. Members of the expert review panel were all project

managers/superintendents and had a minimum of 15 years of experience as a construction

personnel. Each participant was provided a description of the study, a description of the created

tool. For each statement in the interview, panel members were asked to score their perceptions

and understanding based on the following scale: 1 = strongly disagree, 2 = disagree, 3 =

unsure/neutral, 4 = agree and 5 = strongly agree. Table 11 provides a statistical analysis of each

answered question.

Table 11: Expert Review Panel Responses

Posed Statement Description Minimum

Score

Maximum

Score

Average

Score

There is a need to create a CAD-aided crane location

optimization tool

2 5 4.0

It will be useful for safety managers to visualize crane

location in 3D models

4 5 4.6

The tower crane will remain in the same location

during the entire construction process

4 5 4.8

From this panel review, all the three statements in Table 11 were scored favorably which

means there is a need to create a CAD-aided tool for optimizing and visualizing the location of

tower crane. A majority of the panel members agree with the hypothesis that the tower crane will

not be moved during the entire construction lifecycle in most cases. The expert review panel also

indicated their individual top constraint criteria for determining a tower crane’s location. Results

55

of the most prioritized constraint criteria are provided in the following listed in descending order

of importance):

Crane boom’s length and sweep area

Crane’s height

Conflict with structures, utilities or other crane

Crane boom’s accessibility to the in-built structures

Crane boom’s in-accessibility to workers’ working zone, equipment pathway, etc.

Crane boom’s accessibility to material staging area

Clearance of operator’s line-of-sight

Most of the constraints (all but two) listed are accounted for in the created formwork. It

is not practical to include all the optimization constraints in this research, but the research shows

that the created BIM tower crane optimization tool is able to digitalize the information needed

for the selection of tower crane’s location.

3.5 Summary

The researcher demonstrated that collecting, analyzing and visualizing safety leading

indicators through BIM in the proposed research methodology can be integrated in construction

safety management. Figure 31 summarizes the framework of how the implemented

methodologies could contribute to the construction safety management. By testing the stated

hypotheses, this research identified and explored the increased capabilities of construction safety

managers through visualized safety data and resulting dissemination of this information.

56

Figure 31: Framework of the tower crane optimization tool

57

CHAPTER 4 CONCLUSIONS

This chapter reviews the outcome of the previously stated research objectives.

Conclusions made were derived from the literature review, research methodologies, and case

studies. This chapter also discusses impacts and contributions of the work as well as limitations.

4.1 Review of Objective

The motivation of this research is to enhance construction safety management and

engineering by leveraging data visualization and BIM capabilities for construction safety. The

primary objective of this research is to create a framework to collect, analyze, and visualize

select location-based safety leading indicators through BIM. To achieve this objective, the

research work was divided into three major components of safety leading indicators: near-miss

reporting, hazardous proximity zone identification, and site location optimization.

4.2 Conclusions

4.2.1 Near miss reporting

A methodology for a near-miss data visualization tool to collect, manipulate and

disseminate near-miss information was provided. Near-miss reporting and analysis provides an

additional metric for assessing worker safety performance. The created tool enables safety

managers and other project stakeholders to visualize and communicate about safety data within a

project BIM. The feasibility of this approach has been shown by integrating the near-miss data

visualization tool in an existing BIM design software. By utilizing this tool, hazardous situations

58

and conditions can be reported as near misses and visualized by anyone accessing the BIM. This

visualization of construction safety data during a project’s duration promotes safety within a

company and within an active construction site. From a safety management viewpoint, time and

effort of safety staff and engineers can be focused and optimized by visualizing critical safety

data such as near-miss reports.

4.2.2 Hazardous Proximity Zone

The safety practices currently used in the construction industry for ground workers and

heavy equipment operating in close proximity has been proven inadequate by the continued

injuries and fatalities resulting from workers being struck by equipment or objects. Although

unique situations on construction sites require workers to be close to heavy equipment (i.e.,

mobile highway asphalt pavement rehabilitation operations), construction site personnel should

avoid entering the hazard zone of a piece of equipment during operations. The created hazard

zone can be used in site planning and safety education for construction site workers.

The purpose of this research was to create and test a methodology to design hazard zones

around construction equipment. These hazard zones are areas that should be avoided by ground

workers during construction operation. The methodology created includes six steps that include

the following: 1) equipment footprint, 2) initial safety boundary, 3) equipment function, 4)

operation reaction time, 5) equipment braking distance, and 6) creation of the hazard zone. The

hazard zone was created for three pieces of construction equipment (dump truck, backhoe loader,

and excavator) using the presented methodology. Results of the created hazards zones indicate

that hazard zones can be designed and used to increase awareness of dangers around construction

equipment for construction site personnel. Location-based data for a dump truck was used to

evaluate the created hazard zone methodology. By creating hazard zones around construction

59

equipment, ground workers can be informed of and avoid dangerous areas around heavy

equipment on construction sites. Hazard zones can be integrated into existing worker safety

education and training as well as improve construction site planning to enhance safety

performance.

4.2.3 Site Location Optimization

Tower crane location placement can drastically impact the productivity and safety of a

construction project. This research creates an optimization tool capable of locating an optimized

location placement for a tower crane given various construction site parameters and governing

constraints. The created tool allows designs and construction project managers to location and

optimal position of a tower crane through an existing BIM during construction pre-planning. The

tool assumes management personnel want to place the tower crane one time during the

equipment mobilization phase of the project. The tool was implemented successfully in multiple

simulation and actual construction site situations. It was validated through an internal robustness

verification process and implemented on actual construction sites. Unlike existing site layout

strategies, the tower crane location optimization tool implements safety and productivity

thresholds as a determining mechanism. The contribution of this research is a framework in BIM

that can automatically identify the optimal location of a tower crane for productivity and safety

applications as well as scientific evaluation data of this framework on an active construction site.

By locating the optimal tower crane position in BIM during the construction pre-planning phase

of a project, construction management personnel can more readily anticipate for other project

aspects including material transportation schedules, loading and unloading of materials, and

safety concerns.

60

4.3 Limitations

The main limitations of this study are the following:

The research investigated only three location-based safety leading indicators. Future

research could address more indicators (i.e., behavior-based leading indicators or total

site optimization for safety in BIM).

The hazardous proximity zone design was implemented on a select pieces of construction

equipment and represented in 2D, not 3D.

Site layout optimization was limited to tower cranes; other layout components should be

further analyzed by future researchers.

Only safety and productivity were considered as optimization parameters for a tower

crane’s location.

Coding and algorithm creation were performed in one BIM software (Autodesk Revit).

Future development should consider the feasibility on other commercially-available BIM

software.

4.4 Contributions and Impacts

Several contributions can be identified from the successful execution of this proposed

research. These contributions can supplement and enhance existing information in both research

and the construction industry concerning construction safety issues. The follow are specific

contributions of this work:

A framework for near miss data collection and visualization in BIM;

Automated creation of hazardous proximity zone of heavy equipment for workers’ safety;

A framework and optimization tool to locate the optimal position for placing a tower

crane on a construction site.

61

These contributions have potential to impact the construction industry by improving

safety engineering and management by collecting, analyzing; and visualizing location-based

safety leading indicators in BIM. Envisioned supplemented beneficial impacts include: 1)

methods for overcoming barriers for implementation of safety technologies in BIM, 2)

educational implementation of research deliverables for safety management and engineering, 3)

enhanced safety training for workers through visualization applications, and 4) promotion of

safety benchmarking through safety leading indicators.

62

REFERENCES

Acharya, R., Kannathal, N., and Krishnan, S. (2004). “Comprehensive analysis of cardiac health

using heart rate signals.” Physiological Measurement, 25(5), 1139.

Agnew, J., and Daniels, A. (2011). “Development high-impact leading indicator for safety.” The

Performance Management Magazine, <http://aubreydaniels.com/pmezine/developing-

high-impact-leading-indicators-safety> (Feb. 18, 2015).

Akcamete, A., Burcu, A., and Garret, J. (2010). “Potential utilization of building information

models for planning maintenance activities.” Proceedings of the International

Conference of Computing in Civil and Building Engineering.

American Heart of Association (2015). “All about heart rate (pulse)” American Heart of

Association,<http://www.heart.org/HEARTORG/Conditions/More/MyHeartandStrokeNe

ws/All-About-Heart-Rate-Pulse_UCM_438850_Article.jsp > (July 30, 2015).

Anumba, C., and Bishop, G. (1997). “Importance of safety considerations in site layout and

organization.” Canadian Journal of Civil Engineering, 24(2), 229-236.

Arditi, D., Ayrancioglu, M., and Jingsheng Shi, J. (2005). “Worker safety issues in night-time

highway construction.” Engineering, Construction and Architectural Management, 12(5),

487-501.

Astrand, P., and Rodahl, K. (1986). Textbook of work physiology: Physiological bases of

exercise, McGraw-Hill, New York.

Azhar, S. (2011). “Building Information Modeling (BIM): Trends, benefits, risks, and challenges

for the AEC industry.” Leadership Management Engineering, 11(3), 241-252.

Bansal, V. (2011). "Application of geographic information systems in construction safety

planning." International Journal of Project Management, 29(1), 66-77.

Behzadan, A., Zeeshan, A., Anumba, C. and Kamat, V. (2008). "Ubiquitous location tracking for

context-specific information delivery on construction sites." Automation in Construction,

17(6), 737-748.

Bureau of Labor Statistics (2013). “Nonfatal occupational injuries and illnesses requiring days

away from work, 2011.” U.S. Department of

Labor,<http://www.bls.gov/news.release/osh2.nr0.htm> (Nov. 10, 2014).

63

Bureau of Labor Statistics. (2013). “Industry at a glance: Construction.” U.S. Department of

Labor,<http://www.bls.gov/iag/tgs/iag23.htm> (June 16, 2014).

Bureau of Labor Statistics. (2014). “Injuries, Illnesses, and Fatalities.” U.S. Department of

Labor,<http://www.bls.gov/iif/oshsum.htm> (Dec. 14, 2014).

Burke, M. Sarpy, S., Smith-Crowe, K., Chan-Serafin, S., Salvador, R. and Islam, G. (2006).

"Relative effectiveness of worker safety and health training methods." American Journal

of Public Health, 96(2), 315.

Cambraia, F., Saurin, T., Formoso, C. (2010). Identification, analysis and dissemination of

information on near misses: A case study in the construction industry.” Safety Science,

48(1), 91-99.

Caterpillar. (2011). “Caterpillar Performance Handbook.” Caterpillar,

<http://www.cashmanequipment.com> (May 1, 2014).

Census of Fatal Occupational Injuries. (2011). “Current and Revised Data.” U.S. Bureau of

Labor Statistics, <http://www.bls.gov/news.release/osh2.htm> (May 7, 2014).

Centers for Disease Control and Prevention (CDC) (2015). “Assessing your weight.” Division of

Nutrition, Physical Activity, and

Obesity,<http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html> (May.

15, 2015).

Cohen, J. M. (2002). “Measuring safety performance in construction.”

Occup. Hazards, 64(6), 41–44.

Chantawit, D., Hadikusumo, B., Charoenngam, C. and Rowlinson, S. (2005). “4DCAD-Safety:

Visualizing project scheduling and safety planning.” Construction Innovation, 5(2), 99-

114.

Cheng, T., Migliaccio, G., Teizer, J., and Gatti, U. (2012). “Data fusion of real-time location

sensing and physiological status monitoring for ergonomics analysis of construction

workers.” Journal of Computing in Civil Engineering.

Cheng, T., Venugopal, M., Teizer, J. and Vela, P. (2011). “Performance evaluation of ultra

wideband technology for construction resource location tracking in harsh environments.”

Automation in Construction, 20(8), 1173-1184.

Choudhry, R., Fang, D., and Mohamed, S. (2007). “Developing a model of construction safety

culture.” Journal of Management in Engineering, 23(4), 207-212.

Christensen, E. H. (1983). ‘‘Physiology of work.’’ Encyclopedia of occupational health and

safety, L. Parmeggiani, ed., International Labor Organization, Switzerland.

64

Construction Industry Institute. (2012) “2011 Safety Report. BMM2011-2.” The University of

Texas at Austin.

Cretikos, M., Bellomo, R., Hillman, K., Chen, J., Finfer, S., and Flabouris, A. (2008).

“Respiratory rate: the neglected vital sign.” Medical Journal of Australia, 188(11), 657.

David, G. (2005). “Ergonomic methods for assessing exposure to risk factors for work-related

musculoskeletal disorders.” Occupational Medicine, 55(3), 190-199.

Duchon and Laage. (2011). “The Consideration of Human Factors in the Design of a Backing-up

Warning System.” Human Factors and Ergonomics Society Annual Meeting

Proceedings, <http://www.ingentaconnect.com/> (Sept. 19, 2014).

Eastman, C., Teicholz, P., Sacks, R. and Liston, K. (2011). BIM handbook: A guide to building

information modeling for owners, managers, designers, engineers and contractors. John

Wiley & Sons.

Elbeltagi, E., Hegazy, T., and Eldosouky, A. (2004). “Dynamic layout of construction temporary

facilities considering safety.” Journal of Construction Engineering and Management

130(4), 534-541.

Elbeltagi, E., Hegazy, T., Hosny, A. and Eldosouky, A. (2001). “Schedule-dependent evolution

of site layout planning.” Construction Management and Economics, 19(7), 689-697.

El-Rayes, K. and Said, H. (2009). “Dynamic site layout planning using approximate dynamic

programming.” Journal of Computing in Civil Engineering, 23(2), 119-127.

English, W. (1994). “Crane Hazards and Their Prevention.” Professional Safety, 39(3), 53.

Fang, X. and Huang, H. (2004). “Factor analysis-based studies on construction workplace safety

management in China.” International Journal of Project Management, 22(1), 43-49.

Flin, R., Mearns, K., O'Connor, P., and Bryden, R. (2000). "Measuring safety climate:

Identifying the common features." Safety Science, 34(1), 177-192.

Fortunato III, B. Hallowell, M., Behm, M. and Dewlaney, K. (2011). "Identification of safety

risks for high-performance sustainable construction projects." Journal of Construction

Engineering and Management 138(4), 499-508.

Fosbroke, D.E. (2004). “NIOSH Reports! Studies on Heavy Equipment Blind Spots and Internal

Traffic Control.” NIOSH,<https://www.workzonesafety.org> (May 16, 2014).

Fullerton, C., Allread, B., and Teizer, J. (2009). “Pro-active real-time personnel warning

system.” Proceedings of the Construction Research Congress, Seattle, Washington, 31-

40.

65

Gatti, U., Migliaccio, G., Schneider, S. (2011). “Wearable physiological status monitors for

measuring and evaluating worker’s physical strain.” 2011 ASCE Workshop of Computing

in Civil Engineering, ASCE, Reston, VA, 24.

Gillen, M., Baltz, D., Gassel, M., Kirsch, L. and Vaccaro, D. (2002). “Perceived safety climate,

job demands, and coworker support among union and nonunion injured construction

workers.” Journal of Safety Research, 33(1), 33-51.

Goldenhar, L., Moran, S., and Colligan, M. (2001). “Health and safety training in a sample of

open-shop construction companies.” Journal of Safety Research, 32(2), 237-252.

Hadikusumo, B. and Rowlinson, S. (2004). "Capturing safety knowledge using design-for-

safety-process tool." Journal of Construction Engineering and Management, 130(2), 281-

289.

Hallowell, M. and Gambatese, J. (2009). "Construction safety risk mitigation." Journal of

Construction Engineering and Management, 135(12), 1316-1323.

Hallowell, M., Hinze, J., Baud, K., and Wehle, A. (2013). “Pro-Active construction safety

control: Measuring, monitoring, and responding to safety leading indicators.” Journal of

Construction Engineering and Management, 139(10).

Health and Safety Executive (2015). “Type of Reportable Incidents.” Health and Safety

Executive. < http://www.hse.gov.uk/riddor/reportable-incidents.htm> (May 27, 2015).

Hecker, S., Jambatese, J., and Weinstein, M. (2005). “Designing for worker safety: Moving the

construction safety process upstream.” Professional Safety, 23-44.

Henneman, E. and Gawlinski, A. (2004). “A near-miss model for describing the nurse’s role in

the recovery of medical errors,” Journal of Professional Nursing, 20(3) 196-201.

Hinze, J. (2002). “Safety plus: Making zero accidents a reality.” Construction Industry Institute,

Report 160-11, The University of Texas at Austin.

Hinze, J. (2006). Construction safety. Second edition, Prentice Hall, Gainesville, FL.

Hinze, J. and Coates, W. (2011). “Trends in construction work fatalities.” Proceedings of CIB

W099 Prevention: Means to the End of Injuries, Illnesses, and Fatalities. CIB World,

Washington, DC.

Hinze, J. and Godfrey, R. (2003). “An evaluation of safety performance measures for

construction projects,” Journal of Construction Research, 4(1) 5-15.

Hinze, J. and Teizer, J. (2011). “Visibility-related fatalities related to construction equipment.”

Journal of Safety Science, 49(5), 709-718.

66

Hinze, J., Thurman, S., and Wehle, A. (2013). “Leading indicators of construction safety

performance.” Safety Science, 51(1), 23-28.

Hjortskov, N., Rissen, D., Blangsted, A., Fallentin, N., Lundberg, U., and Sogaard, K. (2004).

“The effect of mental stress on heart rate variability and blood pressure during computer

work.” European Journal of Applied Physiology, 92(1), 84-89.

Ho, C. and Dzeng, R. (2010). "Construction safety training via e-Learning: Learning

effectiveness and user satisfaction." Computers & Education, 55(2), 858-867.

Hu, Z., Zhang, J., and Deng, Z. (2008). “Construction process simulation and safety analysis

based on building information model and 4D technology.” Tsinghua Science and

Technology, 13(S1), 266-272.

Huang, C., Wong, C. and Tam, C. (2011). “Optimization of tower crane and material supply

locations in a high-rise building site by mixed-integer linear programming.” Automation

in Construction, 20(5), 571-580.

Huang, X. and Hinze, J. (2006). “Owner’s role in construction safety.” Journal of Construction

Engineering and Management, 132(2), 164-173.

Irizarry, J. and Karan, E. (2012). “Optimizing location of tower cranes on construction sites

through GIS and BIM integration.” Journal of Information Technology in Construction,

17(1), 351-366.

Kang, S., Chi, H. and Miranda, E. (2009). “Three-dimensional simulation and visualization of

crane assisted construction erection processes.” Journal of Computing in Civil

Engineering, 23(6), 363-371.

Kantor, L., Endler, N., Heslegrave, R., Kocovski, N. (2001). “Validating self-report measures of

state and trait anxiety against a physiological measure.” Current Psychology, 20(3), 207-

215.

Kaplan, H., Battles, J., Schaaf, T., Shea, C. and Mercer, S. (1998). "Identification and

classification of the causes of events in transfusion medicine." Transfusion, 38(11), 1071-

1081.

Kim, C., Haas, C., Liapi, K., and Caldas, C. (2006). “Human-assisted obstacle avoidance system

using 3D workspace modeling for construction equipment operation.” Journal of

Computing in Civil Engineering, 20(3), 177-186.

Kristal-Boneh, E., Silber, H., Harari, G., & Froom, P. (2000). “The association of resting heart

rate with cardiovascular, cancer and all-cause mortality.” European Heart Journal, 21(2),

116-124.

67

Ku, K. and Mills, T. (2010). "Research needs for building information modeling for construction

safety." International Proceedings of Associated Schools of Construction 45th Annual

Conference, Boston, MA.

Kumar, S. and Cheng, J. (2015). “A BIM-based automated site layout planning framework for

congested construction sites.” Automation in Construction, 59(1), 24-37.

Kunz, J. and Fischer, M. (2009). "Virtual design and construction: themes, case studies and

implementation suggestions." Center for Integrated Facility Engineering (CIFE),

Stanford University.

Kymmell, W. (2008). Building information modeling: planning and managing construction

projects with 4D CAD and simulations. New York: McGraw-Hill.

Lee, G., Cho, J., Ham, S., Lee, T., Lee, G., Yun, S. H. and Yang, H. (2012). “A BIM-and sensor-

based tower crane navigation system for blind lifts.” Automation in construction, 26(1),

1-10.

Lusk, S., Gillespie, B., Hagerty, B., and Ziemba, R. (2004). “Acute effects of noise on blood

pressure and heart rate.” Archives of Environmental Health: An International Journal,

59(8), 392-399.

Magari, S., Hauser, R., Schwartz, J., Williams, P., Smith, T., and Christiani, D. (2001).

“Association of heart rate variability with occupational and environmental exposure to

particulate air pollution.” Circulation, 104(9), 986-991.

Manual on Uniform Traffic Control Devices. (2013). “2009 Edition Chapter 2C. Warning Signs

and Object Markers.” Federal Highway

Administration,<http://mutcd.fhwa.dot.gov/htm/2009/part2/part2c.htm> (May 30, 2014).

Marks, E. and Teizer, J. (2013). “Method for testing proximity detection and alert technology for

safe construction equipment operation.” Construction Management and Economics,

31(6), 636-646.

Marks, E., Teizer, J., and Hinze, J. (2014). "Near-miss reporting program to enhance

construction worker safety performance." Construction Research Congress, 2315-2324

Marx, A., and Hootegem, G. (2007). “Comparative configurational case analysis of ergonomic

injuries.” Journal of Business Research, 60(5), 522-530.

Mawdesley, M., Al-Jibouri, S. and Yang, H. (2002). “Genetic algorithms for construction site

layout in project planning.” Journal of Construction Engineering and Management,

128(5), 418-426.

McCann, M., and Gittleman, J. (2009). “Crane-related deaths in construction and

recommendations for their prevention.” The Center of Construction Research and

68

Training, < http://www.cpwr.com/research/crane-related-deathsconstruction-and-

recommendations-their-prevention> (Nov. 5, 2015).

McKay, B. (2013). “Measures of effect: Near miss reporting on construction site injuries.”

Master’s Thesis, The University of Alaska.

Meijer, G., Westerterp, K., Koper, H. (1989). “Department of Human Biology, University of

Limburg, The Netherlands.” Medicine and Science in Sports and Exercise, 21(3), 343-

347.

Neitzel, R., Seixas, N. and Ren, K. (2001). “A review of crane safety in the construction

industry.” Applied Occupational and Environmental Hygiene, 16(12), 1106-1117.

O’Connor, T. Loomis, D., Runyan, C. Abboud dal Santo, J., and Schulman, M. (2005).

"Adequacy of health and safety training among young Latino construction workers."

Journal of Occupational and Environmental Medicine, 47(3), 272-277.

Occupational Safety and Health Administration (2003). “Safety and Health Topics.” U.S.

Department of Labor, <https://www.osha.gov/SLTC/aed/index.html> (July 14, 2015).

Occupational Safety and Health Administration (2011a). “Mission and Vision.” U.S. Bureau of

Labor Statistics,<http://www.osha.gov/StratPlanPublic/strategicmanagementplan-

final.html> (Sep 10, 2014).

Occupational Safety and Health Administration (2011b). “OSHA injury and illness

recordkeeping and reporting requirements.” U.S. Bureau of Labor

Statistics,<https://www.osha.gov/recordkeeping> (Aug. 14, 2013).

Occupational Safety and Health Administration, (2002a). “Job hazard analysis.” U.S. Bureau of

Labor Statistics,< http://www.osha.gov/Publications/osha3071.pdf > (Nov. 6, 2014).

Occupational Safety and Health Administration (2002b). Job Hazard Analysis. U.S. Department

of Labor.

Occupational Safety and Health Administration. (1996). “Crane and Hoist Safety.” U.S.

Department of Labor, <https://www.osha.gov/archive/oshinfo/priorities/crane.html>

(Oct. 23, 2015).

Occupational Safety and Health Administration. (2013). “Commonly used statistics.” U.S.

Bureau of Labor Statistics, <http://www.osha.gov/oshstats/commonstats.html> (Sep. 3,

2014).

Occupational Safety and Health Administration. (2013a). “Occupational Safety and Health

Administration.” U.S. Department of Labor,<https://www.osha.gov> (June 2, 2014).

69

Occupational Safety and Health Administration. (2013b). “Personal Protective Equipment

(PPE).” U.S. Department of

Labor,<https://www.osha.gov/SLTC/personalprotectiveequipment> (Sept. 5, 2014).

Occupational Safety and Health Administration. (2014). “Regulations (Standards - 29 CFR) -

Table of Contents.” U.S. Department of Labor,<https://www.osha.gov/pls/oshaweb/>

(May 28, 2014).

Overdyk, F., Carter, R., Maddox, R., Callura, J., Herrin, A. and Henriquez, C. (2007).

“Continuous oximetry/capnometry monitoring reveals frequent desaturation and

bradypnea during patient-controlled analgesia.” Anesthesia & Analgesia, 105(2), 412-

418.

Overseas Shipholding Ground, Inc. (2009). “Near Miss Reporting Improvements.” OSG

Newsletter,<http://www.osg.com/index.cfm?pageid=74&itemid=32> (Aug. 16, 2013).

Park, C. and Kim, H. (2013). “A framework for construction safety management and

visualization system.” Automation in Construction, 33(1), 95-103.

Park, J., Marks, E., Cho, Y. and Suryanto, W. (2015). “Performance Test of Wireless

Technologies for Personnel and Equipment Proximity Sensing in Work Zones.” Journal of

Construction Engineering and Management, ASCE, 142(1), 0415049-1-0415049-9.

Pegula, S. (2010). “Fatal Occupational Injuries at Road Construction Sites, 2003-07.” U.S.

Bureau of Labor Statistics,<http://www.bls.gov/opub/mlr/2010/11/art3exc.htm> (May

12, 2014).

Penttila, H. (2007). “Early architectural design and BIM." Computer-Aided Architectural Design

Futures (CAADFutures), Springer Netherlands, 291-302.

Pradhananga, N. and Teizer, J. (2013). “Automatic spatio-temporal analysis of construction site

equipment operations using GPS data.” Automation in Construction, 29(1), 107-122.

Pradhananga, N. and Teizer, J. (2014). “Congestion analysis for construction site layout planning

using real-time data and cell-based simulation model.” Computing in Civil and Building

Engineering, 681-688.

Rodriguez-Ramos, W. and Francis, R. (1983). “Single crane location optimization.” Journal of

Construction Engineering and Management, 109(4), 387-397.

Rozenfield, O., Sacks, R., Rosenfeld, Y., and Baum, H. (2010). "Construction job safety

analysis." Safety Science, 48(4), 491-498.

Ruff, T. (2007). “Recommendations for Evaluating and Implementing Proximity Warning

Systems on Surface Mining Equipment.”

70

NIOSH,<http://www.cdc.gov/niosh/mining/pubs/pubreference/outputid2480.htm> (Dec

1, 2014).

Sadeghpour, F. and Andayesh, M. (2015). “The constructs of site layout modeling: an

overview.” Canadian Journal of Civil Engineering, 42(3): 199-212.

Sanad, H., Ammar, M. and Ibrahim, M. (2008). “Optimal construction site layout considering

safety and environmental aspects.” Journal of Construction Engineering and

Management, 134(7), 536–544.

Sarkin J., Nichols J., Sallis J. and Calfas K. (2000). “Self-report measures and scoring protocols

affect prevalence estimates of meeting physical activity guidelines.” Medicine and

Science in Sports and Exercise, 32(1), 149-56.

Sawacha, E., Naoum, S. and Fong, D. (1999). “Factors affecting safety performance on

construction sites.” International journal of project management, 17(5), 309-315.

Schaaf, T. and Kanse, L. (2004). “Biases in incident reporting databases: An empirical study in

the chemical process industry,” Safety Science, 42(1), 57-67.

Schneider, S., and Susi, P. (1994). “Ergonomics and construction: a review of potential hazards

in new construction.” American Industrial Hygiene Association, 55(7), 635-649.

Shikdar, A. and Sawaqed, N. M. (2003). “Worker productivity, and occupational health and

safety issues in selected industries.” Computers & industrial engineering, 45(4), 563-572.

Spielholz, P., Wiker, S. F. and Silverstein, B. (1998). “An ergonomic characterization of work in

concrete form construction.” American Industrial Hygiene Association, 59(9), 629-635.

Sroufe, L. (1971). “Effects of depth and rate of breathing on heart rate and heart rate

variability.” Psychophysiology, 8(5), 648-655.

Stowe, K., Zhang, S., Teizer, J., and Jaselskis, E. J. (2014). “Capturing the Return on Investment

of All-In Building Information Modeling: Structured Approach.” Practice Periodical on

Structural Design and Construction, American Society of Civil Engineers.

Su, X. Talmaki, S., Cai, H. and Kamat, V. (2013). "Uncertainty-aware visualization and

proximity monitoring in urban excavation: a geospatial augmented reality approach."

Visualization in Engineering, 1(1), 1-13.

Sulankivi, K., Kahkonen, K., Makela, T. and Kiviniemi, M. (2010). "4D-BIM for construction

safety planning." Proceedings of W099-Special Track 18th CIB World Building

Congress, 117-128.

Sullivar, A., and Sheffrin, S. (2003). Economics: Principles in action. Upper Saddle River.

Pearson Prentice Hall, New Jersey.

71

Taelman, J., Vandeput, S., Spaepen, A. and Van Huffel, S. (2009). “Influence of mental stress on

heart rate and heart rate variability.” 4th European Conference of the International

Federation for Medical and Biological Engineering, Springer Berlin Heidelberg, 1366-

1369.

Tam, C., Tong, T., Leung, A. and Chiu, G. (2002). “Site layout planning using nonstructural

fuzzy decision support system.” Journal of construction engineering and management,

128(3), 220-231.

Teizer, J. Caldas, C., and Haas C. (2010). “Real-time three-dimensional occupancy grid

modeling for the detection and tracking of construction resources.” Journal of

Construction Engineering and Management, 133(11), 880-888.

Teizer, J., Allread B., Fullerton, C. and Hinze, J. (2010). “Autonomous pro-active real-time

construction worker and equipment operator proximity safety alert system.” Automation

in Construction, 19(5), 630-640.

Teizer, J., Venugopal, M. and Walia, A. (2008). “Ultrawideband for automated real-time three-

dimensional location sensing for workforce, equipment, and material positioning and

tracking.” Transportation Research Record: Journal of the Transportation Research

Board, 208(1), 56-64.

Thayer, J., Yamamoto, S. and &Brosschot, J. F. (2010). “The relationship of autonomic

imbalance, heart rate variability and cardiovascular disease risk factors.” International

Journal of Cardiology, 141(2), 122-131.

Van der Molen, H., Sluiter, J. and Frings-Dresen, M. (2009). “The use of ergonomic measures

and musculoskeletal complaints among carpenters and pavers in a 4.5-year follow-up

study.” Ergonomics, 52(8), 954-963.

Vasenev, A., Pradhananga, N., Bijleveld, F. R., Ionita, D., Hartmann, T., Teizer, J., and Dorée,

A. (2014). “An information fusion approach for filtering GNSS data sets collected during

construction operations.” Advanced Engineering Informatics, 28(4), 297-310.

Watson, A. and Donncha, C. (2000). “A reliable technique for the assessment of posture:

Assessment criteria for aspects of posture.” Journal of Sports Medicine and Physical

Fitness, 40 (3): 260-70.

Zhang, J., and Hu, Z. (2011). "BIM-and 4D-based integrated solution of analysis and

management for conflicts and structural safety problems during construction: 1.

Principles and methodologies." Automation in Construction, 20(2), 155-166.

Zhang, P., Harris, F. and Olomolaiye P. (1996). “A computer‐based model for optimizing the

location of a single tower crane.” Building research and information, 24(2), 113-123.

72

Zhang, S., Teizer, J., Lee, J. K., Eastman, C. M., and Venugopal, M. (2013). “Building

Information Modeling (BIM) and safety: Automatic safety checking of construction

models and schedules.” Automation in Construction, 29(1), 183–195.

Zhou, W. Whyte, J., Sacks, R. (2012). "Construction safety and digital design: A review."

Automation in Construction, 22(1), 102-111.

Zohar, D. and Luria, G. (2003). “The use of supervisory practices as leverage to improve safety

behavior: A cross-level intervention model.” Journal of Safety Research, 34(5), 567-577.

73

APPENDIX A

CONSTRUCTION EXPERT INTERVIEW QUESTIONS

This appendix shows the interview tool administered to construction site personnel on

projects in sections A.1 and A.2. The first set of questions (see A.1) were given to safety managers,

the second set to supervisors including experts on tower crane (see A.2).

A.1 Safety Expert Interview Panel Questions

Rate the following questions based on your past experiences.

Use a 1-5 scale to rate your responses

(1 = strongly disagree, 2 = disagree, 3 = unsure/neutral, 4 = agree, 5 = strongly agree)

1) There is a need to integrate safety data in existing project management tools. ____

2) It will be useful for safety managers to visualize safety information in 3D models. ___

3) The proposed tool would be easy to use. ___

4) The proposed tool is implementable. ___

5) The proposed tool would enhance near miss reporting. ___

5) The proposed tool would be effective in safety data management. ___

6) Overall, the proposed tool would be effective in improving safety management. ___

7) Please provide any questions or comments you may have regarding the tool or research.

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

74

A.2 Crane Expert Interview Panel Questions

Participant Information

1) Participant Job Title: _______________________________________________

2) Number of years filling role as construction personnel: _________________________

3) Level of education (e.g. Master’s Degree): ______________________________

4) Level of safety certification (e.g. CSP): ________________________________

For Question 5 to 7,

Rate the following questions based on your past experiences.

Use a 1-5 scale to rate your responses

(1 = strongly disagree, 2 = disagree, 3 = unsure/neutral, 4 = agree, 5 = strongly agree)

5) There is a need to create a CAD-aided crane location optimization tool. ____

6) It will be useful for safety managers to visualize crane location in 3D models. ___

7) The tower crane will remain in the same location during the entire construction process.____

8) Please select the most two or three important constraints that would influence the selection of

tower crane’s location.

a) Crane boom’s length (Crane boom sweep area)

b) Conflict with structures, utilities or other crane

c) Crane boom’s accessibility to the in-built structures

d) Crane boom’s accessibility to material staging area

e) Crane boom’s in-accessibility to workers’ working zone, equipment pathway, etc.

f) Crane’s height

g) Clearance of operator’s line-of-sight

h) Others (Please list) : _______________

9) Please provide any questions or comments you may have regarding this survey.

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

75

APPENDIX B

GEOMETRIES OF EQUIPMENT HAZARD ZONE IN AUTOCAD

This appendix shows the geometries of available equipment with their associated hazard

zones in AutoCAD, after users inputting necessary parameters/variables in the created interface.

(a) (b)

(c) (d)

76

(e) (f)

(g) (h)

(i) (j)

Figure B:(a) dump truck, (b) excavator, (c) backhoe excavator (also called rear actor), (d) dozer,

(e) wheel loader, (f) scraper, (g) motor grader, (h) forklift, (i) skid steer loader, and (j) compactor

77

APPENDIX C

CODE EXAMPLES (PROGRAMMING)

This appendix shows the coding snippets for this research in sections C.1, C.2, C.3, C.4,

C.5, and C.6. C# was selected as programming language and was written in Visual Studio. The

C.1 was given to safety managers for the near miss reporting user-interface ribbons shown in

Autodesk Revit: 1) create a near miss report (see C.2), 2) view and edit information for each

existing report (also see C.2), 3) visualize the spatial location of a near miss report (see C.3) and

4) filter near misses by various parameters (see C.4). Section C.5 contains the codes for ribbons in

Revit to optimize the crane’s location in a 3-D model. A methodology to read optimized crane’s

coordinates was realized in C.6.

78

C.1

using System; using System.Collections.Generic; using System.Linq; using System.Text; using Autodesk.Revit.DB; using Autodesk.Revit.UI; using Autodesk.Revit.ApplicationServices; using Autodesk.Revit.Attributes; using System.Xaml; using System.Diagnostics; //used for debug using System.IO;// used for reading folders using System.Windows.Media.Imaging;//used for bitmap images namespace Ribbon { [Transaction(TransactionMode.Automatic)] public class UIRibbon:IExternalApplication { const string _introLabName1 = "Demo1"; const string _introLabName2 = "CreateNearMiss"; const string _introLabName3 = "Selection"; const string _uiLabName = "UiCs"; const string _dllExtension = ".dll"; const string _imageFolderName = "Images"; string _introLabPath1, _introLabPath2, _introLabPath3; string _imageFolder; public Autodesk.Revit.UI.Result OnStartup(Autodesk.Revit.UI.UIControlledApplication application) { string dir = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location); _introLabPath1 = Path.Combine(dir, _introLabName1 + _dllExtension); _introLabPath2 = Path.Combine(dir, _introLabName2 + _dllExtension); _introLabPath3 = Path.Combine(dir, _introLabName3 + _dllExtension); if (!File.Exists(_introLabPath1)) { TaskDialog.Show("UIRibbon", "External command assembly not found: "+ _introLabPath1); return Result.Failed; } if (!File.Exists(_introLabPath2)) { TaskDialog.Show("UIRibbon", "External command assembly not found: " + _introLabPath2); return Result.Failed; } _imageFolder = FindFolderInParents(dir, _imageFolderName); if (null == _imageFolder|| !Directory.Exists(_imageFolder))

79

{ TaskDialog.Show("UIRibbon",string.Format("No image folder named '{0}' found in the parent directories of '{1}.",_imageFolderName, dir)); return Result.Failed; } AddRibbonSampler(application); return Result.Succeeded; } public Result OnShutdown(UIControlledApplication app) { return Result.Succeeded; } string FindFolderInParents( string path, string target ) { Debug.Assert( Directory.Exists( path ),"expected an existing directory to start search in" ); string s; do { s = Path.Combine( path, target ); if( Directory.Exists( s ) ) { return s; } path = Path.GetDirectoryName( path ); } while( null != path ); return null; } BitmapImage NewBitmapImage(string imageName) { return new BitmapImage(new Uri(Path.Combine(_imageFolder, imageName))); } public void AddRibbonSampler( UIControlledApplication app ) { app.CreateRibbonTab( "Near Miss" ); RibbonPanel panel1 = app.CreateRibbonPanel( "Near Miss", "Show the information of a Near Miss" ); RibbonPanel panel2= app.CreateRibbonPanel("Near Miss", "Create a new Near Miss"); RibbonPanel panel3= app.CreateRibbonPanel("Near Miss", "Selection Filter"); RibbonPanel panel4 = app.CreateRibbonPanel("Near Miss", "Query by ID"); AddPushButton(panel1, _introLabPath1, _introLabName1, "InfoOfNearMiss.png", "InfoOfNearMiss", ".InformationOfNearMiss", "show the information of a selected near miss"); AddPushButton(panel2, _introLabPath2, _introLabName2, "House.ico", "Create", ".CreateNearMiss", "create a new Near Miss"); AddPushButton(panel4, _introLabPath3, _introLabName3, "Families.ico", "QueryByID", ".QueryID", "make a query by Near Miss ID"); AddSplitButton(panel3);

80

} public void AddPushButton(RibbonPanel panel, string _introLabPath, string _introLabName, string imagename,string buttonname,string classname,string tooltiptext) { PushButtonData pushButtonDataHello = new PushButtonData("PushButton", buttonname, _introLabPath, _introLabName + classname); PushButton pushButtonHello =panel.AddItem(pushButtonDataHello) as PushButton; pushButtonHello.LargeImage = NewBitmapImage(imagename); pushButtonHello.ToolTip = tooltiptext; } public void AddSplitButton(RibbonPanel panel) { // Create three push buttons for split button drop down // #1 PushButtonData pushButtonData1 = new PushButtonData("Split1", "Low Severity", _introLabPath3, ".SelectionLow.SelectionLow"); pushButtonData1.LargeImage = NewBitmapImage("low.png"); // #2 PushButtonData pushButtonData2 = new PushButtonData("Split2", "Moderate Severity", _introLabPath3, ".SelectionModerate.SelectionModerate"); pushButtonData2.LargeImage = NewBitmapImage("moderate.png"); // #3 PushButtonData pushButtonData3 = new PushButtonData("Split3", "Critical Severity", _introLabPath3, ".SelectionCritical.SelectionCritical"); pushButtonData3.LargeImage = NewBitmapImage("critical.png"); // Make a split button now SplitButtonData splitBtnData = new SplitButtonData("SplitButton", "Selection Filter"); SplitButton splitBtn = panel.AddItem(splitBtnData) as SplitButton; splitBtn.AddPushButton(pushButtonData1); splitBtn.AddPushButton(pushButtonData2); splitBtn.AddPushButton(pushButtonData3); } } }

81

C.2

using System; using System.Collections.Generic; using System.Linq; using System.Text; using Autodesk.Revit.DB; using Autodesk.Revit.UI; using Autodesk.Revit.ApplicationServices; using Autodesk.Revit.Attributes; using System.Xaml; using System.IO;// used for reading folders namespace CreateNearMiss { [Transaction(TransactionMode.Automatic)] public class CreateNearMiss:IExternalCommand { Application m_rvtApp; Document m_rvtDoc; public Result Execute(ExternalCommandData commandData,ref string message,ElementSet elements) { UIApplication rvtUIApp = commandData.Application; UIDocument rvtUIDoc = rvtUIApp.ActiveUIDocument; m_rvtApp = rvtUIApp.Application; m_rvtDoc = rvtUIDoc.Document; RevitCommandId CreateNearMiss = RevitCommandId.LookupCommandId("ID_OBJECTS_FAMSYM"); rvtUIApp.PostCommand(CreateNearMiss); return Result.Succeeded; } } }

82

C.3

using System; using System.Collections.Generic; using System.Linq; using System.Text; using Autodesk.Revit.DB; using Autodesk.Revit.UI; using Autodesk.Revit.ApplicationServices; using Autodesk.Revit.Attributes; using Autodesk.Revit.UI.Selection; namespace Demo1 { [Transaction(TransactionMode.Automatic)] public class InformationOfNearMiss : IExternalCommand { Element e; Application m_rvtApp; Document m_rvtDoc; UIApplication rvtUIApp; UIDocument rvtUIDoc; public Result Execute(ExternalCommandData commandData, ref string message, ElementSet elements) { rvtUIApp = commandData.Application; rvtUIDoc = rvtUIApp.ActiveUIDocument; m_rvtApp = rvtUIApp.Application; m_rvtDoc = rvtUIDoc.Document; PickMethod_PickObject(); return Result.Succeeded; } public void PickMethod_PickObject() { Reference r = rvtUIDoc.Selection.PickObject(ObjectType.Element, "Select the Near Miss:"); e = rvtUIDoc.Document.GetElement(r); //ElementId elemTypeId = e.GetTypeId(); //ElementType elemType = (ElementType)m_rvtDoc.GetElement(elemTypeId); ShowParameters(e, "Type Parameters"); } public string val_picture; public string val_Date; public string val_Time; public string val_ID; public string val_Severity; public string val_Project; public string val_Company;

83

public string val_IC; public string val_TA; public string val_CI; public string val_EMI; public string val_EQI; public string val_ED; public string val_Investigator; public string val_IMC; public string val_COC; public string val_CA; public string val_RD; public string val_1R; public string val_1RD; public string val_2R; public string val_2RD; public string val_RR; public string val_RRD; public string val_Identification; public string val_Category; public string val_DCAF; public void ShowParameters(Element elem, string header) { /*ParameterSet paramSet = elem.Parameters; string s = string.Empty; foreach (Parameter param in paramSet) { string name = param.Definition.Name; if (name == "1st Review Date") { string val = ParameterToString(param); if (val == "none") continue; s += name + " = " + val + "\n"; } else continue; }*/ string s = string.Empty; Parameter param_Date = elem.get_Parameter("Date"); //string name = "Date"; val_Date = ParameterToString(param_Date); //s += name + " = " + val + "\n"; Parameter param_Time = elem.get_Parameter("Time"); //name = "Time"; val_Time = ParameterToString(param_Time); //s += name + " = " + val + "\n"; Parameter param_ID = elem.get_Parameter("ID"); //name = "ID"; val_ID = ParameterToString(param_ID);

84

//s += name + " = " + val + "\n"; Parameter param_Severity = elem.get_Parameter("Severity"); //name = "Severity"; val_Severity = ParameterToString(param_Severity); //s += name + " = " + val + "\n"; Parameter param_Project = elem.get_Parameter("Project"); //name = "Project"; val_Project = ParameterToString(param_Project); //s += name + " = " + val + "\n"; Parameter param_Company = elem.get_Parameter("Company"); //name = "Company"; val_Company = ParameterToString(param_Company); //s += name + " = " + val + "\n"; Parameter param_IC = elem.get_Parameter("Identification Classification"); //name = "Identification Classification"; val_IC = ParameterToString(param_IC); //s += name + " = " + val + "\n"; Parameter param_TA = elem.get_Parameter("Task Associated"); //name = "Task Associated"; val_TA = ParameterToString(param_TA); //s += name + " = " + val + "\n"; Parameter param_CI = elem.get_Parameter("Crew Involved"); //name = "Crew Involved"; val_CI = ParameterToString(param_CI); //s += name + " = " + val + "\n"; Parameter param_EMI = elem.get_Parameter("Employees Involved"); //name = "Employees Involved"; val_EMI = ParameterToString(param_EMI); //s += name + " = " + val + "\n"; Parameter param_EQI = elem.get_Parameter("Equipment Involved"); // name = "Equipment Involved"; val_EQI = ParameterToString(param_EQI); //s += name + " = " + val + "\n"; Parameter param_ED = elem.get_Parameter("Event Description"); //name = "Event Description"; val_ED = ParameterToString(param_ED); //s += name + " = " + val + "\n"; Parameter param_Investigator = elem.get_Parameter("Investigator"); //name = "Investigator"; val_Investigator = ParameterToString(param_Investigator); //s += name + " = " + val + "\n"; Parameter param_IMC = elem.get_Parameter("Immediate Cause"); //name = "Immediate Cause"; val_IMC = ParameterToString(param_IMC); //s += name + " = " + val + "\n"; Parameter param_COC = elem.get_Parameter("Contributing Cause");

85

//name = "Contributing Cause"; val_COC = ParameterToString(param_COC); //s += name + " = " + val + "\n"; Parameter param_CA = elem.get_Parameter("Corrective Action"); //name = "Corrective Action"; val_CA = ParameterToString(param_CA); //s += name + " = " + val + "\n"; Parameter param_RD = elem.get_Parameter("Resolution Description"); //name = "Resolution Description"; val_RD = ParameterToString(param_RD); //s += name + " = " + val + "\n"; Parameter param_1R = elem.get_Parameter("1st Reviewer"); //name = "1st Reviewer"; val_1R = ParameterToString(param_1R); //s += name + " = " + val + "\n"; Parameter param_1RD = elem.get_Parameter("1st Review Date"); //name = "1st Review Date"; val_1RD = ParameterToString(param_1RD); //s += name + " = " + val + "\n"; Parameter param_2R = elem.get_Parameter("2nd Reviewer"); //name = "2nd Reviewer"; val_2R = ParameterToString(param_2R); //s += name + " = " + val + "\n"; Parameter param_2RD = elem.get_Parameter("2nd Review Date"); //name = "2nd Review Date"; val_2RD = ParameterToString(param_2RD); //s += name + " = " + val + "\n"; Parameter param_RR = elem.get_Parameter("Resolution Reviewer"); //name = "Resolution Reviewer"; val_RR = ParameterToString(param_RR); //s += name + " = " + val + "\n"; Parameter param_RRD = elem.get_Parameter("Resolution Review Date"); //name = "Resolution Review Date"; val_RRD = ParameterToString(param_RRD); //s += name + " = " + val + "\n"; Parameter param_Identification = elem.get_Parameter("Identification"); //name = "Identification"; val_Identification = ParameterToString(param_Identification); //s += name + " = " + val + "\n"; Parameter param_Category = elem.get_Parameter("Category"); //name = "Category"; val_Category = ParameterToString(param_Category); //s += name + " = " + val + "\n"; Parameter param_DCAF = elem.get_Parameter("Date Corrective Action Finalized"); //name = "Date Corrective Action Finalized"; val_DCAF = ParameterToString(param_DCAF);

86

//s += name + " = " + val + "\n"; Parameter param_picture = elem.get_Parameter("Picture"); val_picture = ParameterToString(param_picture); Picture picture = new Picture(); if (val_picture != null) { try { picture.getdir(val_picture); } catch { } } picture.TopMost = true; picture.changeparam(val_Date, val_ID, val_IC, val_Time, val_Severity, val_TA, val_Project, val_Company, val_CI, val_Investigator, val_EMI, val_IMC, val_EQI, val_COC, val_ED, val_CA, val_RD, val_1R, val_1RD, val_Identification, val_2R, val_2RD, val_Category, val_RR, val_RRD, val_DCAF); picture.Show(); //TaskDialog.Show(header, s); //picture.Dispose(); } public static string ParameterToString(Parameter param) { string val = "none"; if (param == null) { return val; } switch (param.StorageType) { case StorageType.Double: break; case StorageType.Integer: break; case StorageType.String: string sVal = param.AsString(); val = sVal; break; case StorageType.ElementId: break; case StorageType.None: break; default:

87

break; } return val; } } }

88

C.4

using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Data; using System.Drawing; using System.ComponentModel; using Autodesk.Revit.DB; using Autodesk.Revit.UI; using Autodesk.Revit.ApplicationServices; using Autodesk.Revit.Attributes; using Autodesk.Revit.UI.Selection; using Demo1; namespace SelectionLow { [Transaction(TransactionMode.Automatic)] public class SelectionLow : IExternalCommand { UIApplication _uiApp; UIDocument _uiDoc; Application m_rvtApp; Document m_rvtDoc; public Result Execute(ExternalCommandData commandData, ref string message, ElementSet elements) { _uiApp = commandData.Application; _uiDoc = _uiApp.ActiveUIDocument; m_rvtApp = _uiApp.Application; m_rvtDoc = _uiDoc.Document; PickNearMiss(); return Result.Succeeded; } public void PickNearMiss() { _uiDoc.Selection.Elements.Clear(); SelectionFilterNearMiss selFilterNearMiss = new SelectionFilterNearMiss(); //Reference r = _uiDoc.Selection.PickObject(ObjectType.Element, //selFilterWall, "Select a wall"); IList<Element> elems = _uiDoc.Selection.PickElementsByRectangle(selFilterNearMiss, "Select Near Miss"); // Show it foreach (Element element in elems) { _uiDoc.Selection.Elements.Add(element); Demo1.InformationOfNearMiss showparam = new InformationOfNearMiss(); showparam.ShowParameters(element, "Parameters"); } //ShowBasicElementInfo(e);// Defined in DBElelemt Lab in the Intro Labs }

89

} class SelectionFilterNearMiss : ISelectionFilter { public bool AllowElement(Element e) { if (e.get_Parameter(BuiltInParameter.ELEM_TYPE_PARAM).AsValueString() == "Low severity") { return true; } return false; } public bool AllowReference(Reference reference, XYZ position) { return true; } } } namespace SelectionModerate { [Transaction(TransactionMode.Automatic)] public class SelectionModerate : IExternalCommand { UIApplication _uiApp; UIDocument _uiDoc; Application m_rvtApp; Document m_rvtDoc; public Result Execute(ExternalCommandData commandData, ref string message, ElementSet elements) { _uiApp = commandData.Application; _uiDoc = _uiApp.ActiveUIDocument; m_rvtApp = _uiApp.Application; m_rvtDoc = _uiDoc.Document; PickNearMiss(); return Result.Succeeded; } public void PickNearMiss() { _uiDoc.Selection.Elements.Clear(); SelectionFilterNearMiss selFilterNearMiss = new SelectionFilterNearMiss(); //Reference r = _uiDoc.Selection.PickObject(ObjectType.Element, //selFilterWall, "Select a wall"); IList<Element> elems = _uiDoc.Selection.PickElementsByRectangle(selFilterNearMiss, "Select Near Miss"); // Show it foreach (Element element in elems) { _uiDoc.Selection.Elements.Add(element); Demo1.InformationOfNearMiss showparam = new InformationOfNearMiss(); showparam.ShowParameters(element, "Parameters");

90

} //ShowBasicElementInfo(e);// Defined in DBElelemt Lab in the Intro Labs } } class SelectionFilterNearMiss : ISelectionFilter { public bool AllowElement(Element e) { if (e.get_Parameter(BuiltInParameter.ELEM_TYPE_PARAM).AsValueString() == "Moderate severity") { return true; } return false; } public bool AllowReference(Reference reference, XYZ position) { return true; } } } namespace SelectionCritical { [Transaction(TransactionMode.Automatic)] public class SelectionCritical : IExternalCommand { UIApplication _uiApp; UIDocument _uiDoc; Application m_rvtApp; Document m_rvtDoc; public Result Execute(ExternalCommandData commandData, ref string message, ElementSet elements) { _uiApp = commandData.Application; _uiDoc = _uiApp.ActiveUIDocument; m_rvtApp = _uiApp.Application; m_rvtDoc = _uiDoc.Document; PickNearMiss(); return Result.Succeeded; } public void PickNearMiss() { _uiDoc.Selection.Elements.Clear(); SelectionFilterNearMiss selFilterNearMiss = new SelectionFilterNearMiss(); //Reference r = _uiDoc.Selection.PickObject(ObjectType.Element, //selFilterWall, "Select a wall"); IList<Element> elems = _uiDoc.Selection.PickElementsByRectangle(selFilterNearMiss, "Select Near Miss");

91

// Show it foreach (Element element in elems) { _uiDoc.Selection.Elements.Add(element); Demo1.InformationOfNearMiss showparam = new InformationOfNearMiss(); showparam.ShowParameters(element, "Parameters"); } //ShowBasicElementInfo(e);// Defined in DBElelemt Lab in the Intro Labs } } class SelectionFilterNearMiss : ISelectionFilter { public bool AllowElement(Element e) { if (e.get_Parameter(BuiltInParameter.ELEM_TYPE_PARAM).AsValueString() == "Critical severity") { return true; } return false; } public bool AllowReference(Reference reference, XYZ position) { return true; } } }

92

C.5

using System; using System.Collections.Generic; using System.Linq; using System.Text; using Autodesk.Revit.DB; using Autodesk.Revit.UI; using Autodesk.Revit.ApplicationServices; using Autodesk.Revit.Attributes; using System.Xaml; using System.Diagnostics; //used for debug using System.IO;// used for reading folders using System.Windows.Media.Imaging;//used for bitmap images namespace Ribbon { [Transaction(TransactionMode.Automatic)] public class UIRibbon:IExternalApplication { const string _introLabName1 = "Demo1"; const string _uiLabName = "UiCs"; const string _dllExtension = ".dll"; const string _imageFolderName = "Images"; string _introLabPath1; string _imageFolder; public Autodesk.Revit.UI.Result OnStartup(Autodesk.Revit.UI.UIControlledApplication application) { string dir = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location); _introLabPath1 = Path.Combine(dir, _introLabName1 + _dllExtension); if (!File.Exists(_introLabPath1)) { TaskDialog.Show("UIRibbon", "External command assembly not found: "+ _introLabPath1); return Result.Failed; } _imageFolder = FindFolderInParents(dir, _imageFolderName); if (null == _imageFolder|| !Directory.Exists(_imageFolder)) { TaskDialog.Show("UIRibbon",string.Format("No image folder named '{0}' found in the parent directories of '{1}.",_imageFolderName, dir)); return Result.Failed; } AddRibbonSampler(application); return Result.Succeeded; }

93

public Result OnShutdown(UIControlledApplication app) { return Result.Succeeded; } string FindFolderInParents( string path, string target ) { Debug.Assert( Directory.Exists( path ),"expected an existing directory to start search in" ); string s; do { s = Path.Combine( path, target ); if( Directory.Exists( s ) ) { return s; } path = Path.GetDirectoryName( path ); } while( null != path ); return null; } BitmapImage NewBitmapImage(string imageName) { return new BitmapImage(new Uri(Path.Combine(_imageFolder, imageName))); } public void AddRibbonSampler( UIControlledApplication app ) { app.CreateRibbonTab( "Optimization" ); RibbonPanel panel1 = app.CreateRibbonPanel( "Optimization", "optimize the location of crane" ); AddPushButton(panel1, _introLabPath1, _introLabName1, "InfoOfNearMiss.png", "crane", ".InformationOfNearMiss", "optimize the locacation of crane"); } public void AddPushButton(RibbonPanel panel, string _introLabPath, string _introLabName, string imagename,string buttonname,string classname,string tooltiptext) { PushButtonData pushButtonDataHello = new PushButtonData("PushButton", buttonname, _introLabPath, _introLabName + classname); PushButton pushButtonHello =panel.AddItem(pushButtonDataHello) as PushButton; pushButtonHello.LargeImage = NewBitmapImage(imagename); pushButtonHello.ToolTip = tooltiptext; } } }

94

C.6

using System; using System.Collections.Generic; using System.Linq; using System.Text; using Autodesk.Revit.DB; using Autodesk.Revit.UI; using Autodesk.Revit.ApplicationServices; using Autodesk.Revit.Attributes; using System.Xaml; using System.IO;// used for reading folders using Autodesk.Revit.UI.Selection; namespace Crane_Optimize { [Transaction(TransactionMode.Automatic)] class CreateNearMiss:IExternalCommand { public Result Execute( ExternalCommandData commandData, ref string message, ElementSet elements) { UIApplication uiapp = commandData.Application; UIDocument uidoc = uiapp.ActiveUIDocument; Application app = uiapp.Application; Document doc = uidoc.Document; Reference r = uidoc.Selection.PickObject( ObjectType.Element, "Select one element"); Element e = uidoc.Document.GetElement(r); LocationPoint Ip = e.Location as LocationPoint; String prompt = null; prompt += "\nPoint: (" + Ip.Point.X + ", " + Ip.Point.Y + ", " + Ip.Point.Z + ")"; TaskDialog.Show("Revit",prompt); ShowBasicElementInfo(e, doc); //GetLocationInformation(e); return Result.Succeeded; } void GetLocationInformation(Autodesk.Revit.DB.Element element) { // Get the Location property and judge whether it exists Autodesk.Revit.DB.Location position = element.Location; String prompt = null; if (null == position) { prompt = "No location can be found in element."; } else { // If the location is a point location, give the user information

95

Autodesk.Revit.DB.LocationPoint positionPoint = position as Autodesk.Revit.DB.LocationPoint; if (null != positionPoint) { prompt = "Element has a point location."; } else { // If the location is a curve location, give the user information Autodesk.Revit.DB.LocationCurve positionCurve = position as Autodesk.Revit.DB.LocationCurve; if (null != positionCurve) { prompt = "Element has a curve location."; } } } if (null != prompt) { TaskDialog.Show("Revit", prompt); } } public void ShowBasicElementInfo(Element elem, Document doc) { // let's see what kind of element we got. // string s = "You Picked:" + "\n"; s += " Class name = " + elem.GetType().Name + "\n"; s += " Category = " + elem.Category.Name + "\n"; s += " Element id = " + elem.Id.ToString() + "\n" + "\n"; // and, check its type info. // //Dim elemType As ElementType = elem.ObjectType '' this is obsolete. ElementId elemTypeId = elem.GetTypeId(); ElementType elemType = (ElementType)doc.GetElement(elemTypeId); s += "Its ElementType:" + "\n"; s += " Class name = " + elemType.GetType().Name + "\n"; s += " Category = " + elemType.Category.Name + "\n"; s += " Element type id = " + elemType.Id.ToString() + "\n"; // finally show it. TaskDialog.Show("Basic Element Info", s); } } }