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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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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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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
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