Intelligent Asset Management of Buildings

Professor Sujeeva Setunge Deputy Dean, Research and Innovation School of Engineering

1 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

2 Plan

Design Demolish

Life Cycle of Civil Infrastructure

Construct Refurbish

Maintain Operate

3 Decision Parameters

• Risk of failure Operate • Operating Cost • Energy/water use

• Sustainability

• Climate change • Timing & Method of inspection, • Disaster resilience Maintain • Maintenance methods • Cost • Regulatory compliance • Level of Service • Other ----

• Refurbish or demolish ? Refurbish • Best Material/technique • Cost

4 Current Practice in Local Government

Basic Asset inventory at a high level Replacement value/Depreciation known Paper based inspections Reactive management

5 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

6 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

7 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

8 Industry 4.0 in Infrastructure Management

• Smart materials • Cost • Self healing • Level of Service • Self diagnosing • Behaviour change • Embedded sensor • Return on Investment technologies • Sweating of Assets • Design for lifecycle • Engage community in • Sustainability decision making • Energy Decision Smart efficiency/Energy Making design Harvesting

• Degradation of infrastructure Predictive Automated • BIM • Structural capacity Modelling inspections • Degradation • Material durability mechanisms • Data Driven models • Signs of distress • Reliability • UAVs • Sensor technologies • Image Recognition • Augmented reality • 3D visualisation • Cost of degradation • Structural health monitoring

9 Central Asset Management System - CAMS

CAMS - Buildings CAMS – Mobile CAMS - Drainage CAMS - Bridges CAMS – Report-IT

ARC ITRH – current Smart Cities Grant - current Six local councils, ARC Linkage project - Melbourne water, ARC VicRoads completed, six local councils, Linkage project - current BNH CRC – Disaster MAV resilience, QTMR, RMS, State govt. grant VicRoads, MAV, Lockyer Valley • Predictive modelling of • Predictive modelling of • Predictive modelling of building degradation concrete pipe bridge components • Scenario based degradation • Prioritisation analysis • Optimised inspection • Load rating • Dynamic risk • Life cycle cost • Disaster resilience

10 Predictive modelling using condition data

Deterministic analysis 1.00 Services - Transient Probabilities (GA) 0.90

0.80

0.70

0.60 Cond. 1 0.50 Cond. 2

Probability Cond. 3 0.40 Cond. 4 Cond. 5 0.30

0.20

0.10

0.00 5 15 25 35 45 55 65 75 85 95 Age (Year)

11 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

12 12 CAMS - Buildings: workflow

Excel Excel CAMS Import Import Mobile

Create your building Upload your Assign condition component register component data data to components

Customize forecasting Generate forecast parameters reports

1. Database management 2. Data exploration 3. Deterioration prediction 4. Budget calculation 5. Backlog estimation 6. Risk management

14 14 Example: City of Melbourne

Three scenarios

C1,C2 Replace with 0% threshold

C1,C2 Replace with 25% threshold

C1,C2 Replace with 45% threshold

15 Condition Distribution

▪ Overall average condition distribution for the portfolio with different thresholds are shown. The first graph shows the distribution without intervention

16 Cost distribution

▪ Further knowledge on the expenditure can be explored

17 CAMS Mobile

18 18 RFID Tagging in City of Melbourne

19 Awards – During research stage

Engineers Australia, Asset Management Council Postgraduate Research Awards

20 CAMS Awards Received by end users after implementation

2017 Australian Financial review, 2017 Facilities Innovation Award Facilities Management Australia 40 year Life Cycle Excellence Award – RMIT Property Services

21 CAMS TECHNOLOGY - Buildings

Current Capability Research In Progress Next stage

➢ Data Driven Models for Multi-objective CAMS ▪ UAVs 700 components Decision Making ➢ Cost and other input Life-Cycle ▪ Augmented ➢ Scenarios Analysis Modelling ✓ Physical degradation Reality ➢ Risk-cost Relationship modelling – improve ▪ Laser accuracy scanning ✓ Cost for defects, Cloud-based Database intermediate conditions, works order, optimised ❖ Visual Inspection repair ❖ Inspection progress ✓ Level of service for ❖ RFIDs for asset CAMS Decision Making tracking Mobile ❖ Previous Data ✓ Sensor technologies Smart Cities ❖ Plans / Photos / Defects ✓ Compliance Auditing $871,000 / Asbestos etc. ✓ BIM Integration Kingston, ✓ Utilisation/Level of Brimbank, Port RMIT - $260,000 service/User Phillip+Hendry Feedback Group Hendry Group + City ✓ Automated mapping of Melbourne Asset Management in Smart Cities 24