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Intelligent Asset Management of

Buildings

Professor Sujeeva Setunge

Deputy Dean, Research and Innovation

School of Engineering

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Outline

• Building life cycle

• Current practice

• Intelligent Asset Management with digital disruption

• Central Asset Management System (CAMS)

• Examples from City of Melbourne

• Funding approved from Smart Cities Program

– City of Kingston, Westall Civil Centre

– City of Brimbank, Park Precinct

– City of Portphillip, St. Kilda TownHall

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Plan

Design

Construct

OperateMaintain

Refurbish

Demolish

Life Cycle of Civil

Infrastructure

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Operate• Risk of failure

• Operating Cost

• Energy/water use

Maintain

• Timing & Method of inspection,

• Maintenance methods

• Cost

• Level of Service

Refurbish

• Refurbish or demolish ?

• Best Material/technique

• Cost

• Sustainability

• Climate change

• Disaster resilience

• Regulatory compliance

• Other ----

Decision Parameters

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Current Practice in Local Government

Basic

Asset inventory at a high level

Replacement value/Depreciation known

Paper based inspections

Reactive management

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Current Practice in Local Government

Reactive – Optimised

Detailed Asset Inventory, Frequent Inspections,

Optimised budget allocation using the data

Basic

Asset inventory at a high level

Replacement value/Depreciation known

Paper based inspections

Reactive management

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Current Practice in Local Government

Advanced

Detailed asset inventory, Granularity of data

Frequent inspections, Digitalised data collection, Predictive modelling,

Scenario based optimised decision making

Integrated Level of Service

Reactive – Optimised

Detailed Asset Inventory, Frequent Inspections,

Optimised budget allocation using the data

Basic

Asset inventory at a high level

Replacement value/Depreciation known

Paper based inspections

Reactive management

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Possibilities with Digital Disruption

▪ Automation of Inspections – UAVs, RFIDs, Image Recognition

▪ BIM and Augmented reality – 3D cameras and visualisation

Catering for spaces which do not have drawings or other

records

▪ Compliance auditing

▪ Advanced modelling of degradation

▪ Level of service/Utilisation capture

▪ Engage community in decision making – live streaming of

utilisation, defects/improvements, Choice modelling

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Industry 4.0 in Infrastructure Management

• BIM

• Degradation mechanisms

• Signs of distress

• UAVs

• Image Recognition

• 3D visualisation

• Structural health monitoring

• Degradation of infrastructure

• Structural capacity

• Material durability

• Data Driven models

• Reliability

• Sensor technologies

• Augmented reality

• Cost of degradation

• Smart materials

• Self healing

• Self diagnosing

• Embedded sensor technologies

• Design for lifecycle

• Sustainability

• Energy efficiency/Energy Harvesting

• Cost

• Level of Service

• Behaviour change

• Return on Investment

• Sweating of Assets

• Engage community in decision making

Decision Making

Smart design

Automated inspections

Predictive Modelling

1010

Smart Cities Grant - current

ARC Linkage project -

completed, six local councils,

MAV

State govt. grant

• Predictive modelling of

building degradation

• Scenario based

analysis

• Dynamic risk

CAMS - Buildings

CAMS – Mobile

CAMS – Report-IT

Six local councils,

Melbourne water, ARC

Linkage project - current

• Predictive modelling of

concrete pipe

degradation

• Optimised inspection

• Life cycle cost

CAMS - Drainage

ARC ITRH – current

VicRoads

BNH CRC – Disaster

resilience, QTMR, RMS,

VicRoads, MAV, Lockyer

Valley

• Predictive modelling of

bridge components

• Prioritisation

• Load rating

• Disaster resilience

CAMS - Bridges

Central Asset Management System - CAMS

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Deterministic analysis

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

5 15 25 35 45 55 65 75 85 95

Pro

bab

ility

Age (Year)

Services - Transient Probabilities (GA)

Cond. 1

Cond. 2

Cond. 3

Cond. 4

Cond. 5

Predictive modelling using condition data

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Deterioration Prediction

• Markov Chain

• Non-linear Optimisation Technique – Monte Carlo Analysis

• Direct Absolute Value Difference – Genetic Algorithm

• Validation– Pearson’s Chi-Square Test for Goodness of Fit

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CAMS - Buildings: workflow

RMIT University©2014 School of Civil, Environmental & Chemical Engineering

Create your building component register

Upload your component data

Assign condition data to components

Customize forecasting parameters

Generate forecast reports

CAMS Mobile

Excel Import

Excel Import

Page 10

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CAMS for Buildings - Features

1. Database management

2. Data exploration

3. Deterioration prediction

4. Budget calculation

5. Backlog estimation

6. Risk management

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Example: City of Melbourne

Three scenarios

C1,C2 Replace with 0% threshold

C1,C2 Replace with 25% threshold

C1,C2 Replace with 45% threshold

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Condition Distribution

▪ Overall average condition

distribution for the portfolio

with different thresholds are

shown. The first graph shows

the distribution without

intervention

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Cost distribution

▪ Further knowledge on the expenditure can be explored

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CAMS Mobile

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RFID Tagging in City of Melbourne

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Awards – During research stage

Engineers Australia, Asset Management Council Postgraduate Research Awards

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CAMS Awards

Received by end users after implementation

2017Australian Financial review, Facilities Innovation Award40 year Life Cycle

2017Facilities Management AustraliaExcellence Award – RMIT Property Services

Current Capability

➢ Data Driven Models for

700 components

➢ Cost and other input

➢ Scenarios Analysis

➢ Risk-cost Relationship

CAMS TECHNOLOGY - Buildings

Research In Progress

❖ Visual Inspection

❖ Inspection progress

❖ RFIDs for asset

tracking

❖ Previous Data

❖ Plans / Photos / Defects

/ Asbestos etc.

✓ Physical degradation

modelling – improve

accuracy

✓ Cost for defects,

intermediate conditions,

works order, optimised

repair

✓ Level of service for

Decision Making

✓ Sensor technologies

✓ Compliance Auditing

✓ BIM Integration

✓ Utilisation/Level of

service/User

Feedback

✓ Automated mapping

CAMS

Life-Cycle

Modelling

CAMS

Mobile

Cloud-based Database

Multi-objective Decision Making

Smart Cities$871,000Kingston,

Brimbank, Port Phillip+Hendry

Group

Next stage

▪ UAVs

▪ Augmented

Reality

▪ Laser

scanning

RMIT - $260,000Hendry Group + City

of Melbourne

Asset Management in Smart Cities

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