Intelligent Asset Management of Buildings. Professor Sujeeva Setunge Deputy Dean, Research and Innovation School of Engineering

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1 Intelligent Asset Management of Buildings Professor Sujeeva Setunge Deputy Dean, Research and Innovation School of Engineering 1

2 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

3 Plan Demolish Design Life Cycle of Civil Infrastructure Refurbish Construct Maintain Operate 3

4 Decision Parameters Operate Risk of failure Operating Cost Energy/water use Sustainability Climate change Disaster resilience Regulatory compliance Other ---- Maintain Timing & Method of inspection, Maintenance methods Cost Level of Service Refurbish Refurbish or demolish? Best Material/technique Cost 4

5 Current Practice in Local Government Basic Asset inventory at a high level Replacement value/depreciation known Paper based inspections Reactive management 5

6 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

7 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

8 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

9 Industry 4.0 in Infrastructure Management Cost Level of Service Behaviour change Return on Investment Sweating of Assets Engage community in decision making Decision Making Smart design Smart materials Self healing Self diagnosing Embedded sensor technologies Design for lifecycle Sustainability Energy efficiency/energy Harvesting Degradation of infrastructure Structural capacity Material durability Data Driven models Reliability Sensor technologies Augmented reality Cost of degradation Predictive Modelling Automated inspections BIM Degradation mechanisms Signs of distress UAVs Image Recognition 3D visualisation Structural health monitoring 9

10 Central Asset Management System - CAMS Smart Cities Grant - current ARC Linkage project - completed, six local councils, MAV State govt. grant CAMS - Buildings CAMS Mobile CAMS Report-IT Predictive modelling of building degradation Scenario based analysis Dynamic risk CAMS - Drainage Six local councils, Melbourne water, ARC Linkage project - current Predictive modelling of concrete pipe degradation Optimised inspection Life cycle cost CAMS - Bridges ARC ITRH current VicRoads BNH CRC Disaster resilience, QTMR, RMS, VicRoads, MAV, Lockyer Valley Predictive modelling of bridge components Prioritisation Load rating Disaster resilience 10

11 Probability Predictive modelling using condition data Deterministic analysis Services - Transient Probabilities (GA) Cond. 1 Cond. 2 Cond. 3 Cond. 4 Cond Age (Year) 11

12 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

13 CAMS - Buildings: workflow Excel Import Excel Import CAMS Mobile Create your building component register Upload your component data Assign condition data to components Customize forecasting parameters Generate forecast reports RMIT University School of Civil, Environmental & Chemical Engineering Page 10

14 CAMS for Buildings - Features 1. Database management 2. Data exploration 3. Deterioration prediction 4. Budget calculation 5. Backlog estimation 6. Risk management 14 14

15 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

16 Condition Distribution Overall average condition distribution for the portfolio with different thresholds are shown. The first graph shows the distribution without intervention 16

17 Cost distribution Further knowledge on the expenditure can be explored 17

18 CAMS Mobile 18 18

19 RFID Tagging in City of Melbourne 19

20 Awards During research stage Engineers Australia, Asset Management Council Postgraduate Research Awards 20

21 CAMS Awards Received by end users after implementation 2017 Australian Financial review, Facilities Innovation Award 40 year Life Cycle 2017 Facilities Management Australia Excellence Award RMIT Property Services 21

22 CAMS TECHNOLOGY - Buildings Current Capability Data Driven Models for 700 components Cost and other input Scenarios Analysis Risk-cost Relationship Cloud-based Database Visual Inspection Inspection progress RFIDs for asset tracking Previous Data Plans / Photos / Defects / Asbestos etc. CAMS Life-Cycle Modelling CAMS Mobile RMIT - $260,000 Hendry Group + City of Melbourne Research In Progress Multi-objective Decision Making 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 Next stage UAVs Augmented Reality Laser scanning Smart Cities $871,000 Kingston, Brimbank, Port Phillip+Hendry Group

23 Asset Management in Smart Cities

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