Structural Health Monitoring of Small Bridges

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Magdalene Fields
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1 Structural Health Monitoring of Small Bridges November 2017 Peter Runcie Business Leader New Industries and Platforms Phone:

2 Topics 1. Intro to Data61 2. Continuous Monitoring 3. Data Analysis 4. Small Bridges Project 5. Lessons so far 6. Future Work Program

CSIRO Focus Areas Agriculture Energy Food

and Water Manufacturing Mineral Resources

3 CSIRO Focus Areas Agriculture Energy Food and Nutrition Health and Biosecurity Land and Water Manufacturing Mineral Resources Oceans and Atmosphere Astronomy and Space Science Australian Animal Health Laboratory Data and Digital Marine National Facility National Computing Infrastructure National Research Collections of Australia

4 Structural Health Monitoring Autonomous Drones Predictive Analytics Transport Systems Optimisation

10 1958 Photo, Lindsay Bridge, Creative Commons

5 Photo, Lindsay Bridge, Creative Commons License

Sydney Harbour Bridge Objectives Extend service life of bridge

Continuously monitor 800 structural joints Provide early warning

management system (3200 sensors) Machine learning and other data

6 Sydney Harbour Bridge Objectives Extend service life of bridge deck without significant increase in maintenance costs Continuously monitor 800 structural joints Provide early warning of maintenance needs Technology Large scale sensing and data management system (3200 sensors) Machine learning and other data analysis to detect damage and structural anomalies Web and mobile decision support tools

7 Long Term Continuous Monitoring Enables condition based maintenance Detects incidents as they happen - overloading, bridge strike,.. Diagnose structural condition immediately after strike, flooding Provides more data for numerical engineering models Measure loading over long period of time Enables predictive maintenance

8 Data Analysis

9 1) Damage Identification Big Data approach Data-Driven analysis Complimentary to numerical modelling (FE modelling and analysis) Useful when numerical model may not be available or accurate. Data-driven approach establishes model from data, using machine learning techniques. Unsupervised or One Class machine learning classifier Data corresponding to damage are often not available. A trained model is built using only healthy data. New data not conforming with trained model are considered as damage. 14

5-2 0 50 100 150 200 250 300 350 400 450

10 Decision values Damage Identification 0.5 Joint 5 Joint Localisation Test event index Detection Severity Assessment 15

. Suitable for studying the dynamic behaviour of bridges without disruption to

11 2) Operational modal analysis (OMA) Extraction of structural modal features such as natural frequencies, damping ratios and mode shapes.. Suitable for studying the dynamic behaviour of bridges without disruption to traffic. Use ambient vibration. OMA results used for SHM and for numerical analysis i.e. finite element analysis 16

3) Traffic monitoring and characterisation Live traffic data collection is used for pavement life prediction, fatigue estimation, vibration control, condition assessment and maintenance planning

12 3) Traffic monitoring and characterisation Live traffic data collection is used for pavement life prediction, fatigue estimation, vibration control, condition assessment and maintenance planning Bridge weigh-in-motion (BWIM) is an approach through which the axle and gross weight of trucks travelling at normal highway speed are identified using the response of an instrumented bridge. Event Data Acquisition Number of axles. Axles spacing. Speed estimation. Axles weights. Gross weight. Traffic characterisation Signal Processing 18

13 4) Load Cycle Counting Fatigue life assessment of a structure subjected to a non-constant amplitude loading can be performed in the time domain using rainflow cycle counting. The rainflow method is used for counting the fatigue cycles (stressreversals) and to obtain equivalent constant amplitude cycles from the measured strain data. 19

14 Governor Macquarie Drive Bridge, NSW Double Culvert (2 spans, 3 shear walls) ~4m spans Sensors Strain gauges Accelerometers Thermocouple See conference paper for detail.

15 Bridge over Great Western Highway (NSW) 46m span 16 Stay Cables semi fan Single Tower Composite steel-concrete deck Sensors Accelerometers (uni and tri-axial) Shear rosettes Strain Gauges See conference paper for detail.

16 Damage Identification for Cable-Stayed Bridge A car and a bus were parked on the bridge to simulate damage Ambient vibration data - 2 second acceleration samples Using tensor analysis for data fusion and one-class SVM for anomaly detection Detect and assess the severity of damage (bus vs car damage ))

17 OMA for Cable-Stayed Bridge 26

18 BWIM for Cable-Stay Bridge Using same sensors for SHM for axle spacing, loading and GVM Theoretical Model Lab Test Rig Small Bridge

19 Lessons so far..

20 Costs Instrumentation is not the only cost Need to consider: Road closures (traffic control) Installation labor Provision of power Access equipment hire (eg: elevated work platforms) Networking costs - 4G, ADSL, fibre Ongoing maintenance Sensor removal and re-installation after maintenance 29

21 Sensors Installation is time-consuming, requires training 1 2 hours per strain measurement 30 mins 1 hour per accelerometer Important to consider sensor reliability vs cost Sensors may be difficult to access, making repair/replacement expensive Ensure sensors are rated to at least IP67 where possible Prefer differential output, use shielded cables (where applicable) Difficult to predict noise levels/sources for a given site Effectively eliminates noise from the most common sources Shielded cables cost very little and have a significant impact (for single-ended output sensors) 30

22 Instrumentation Instrumentation specifications have a big impact on analysis capabilities Low sample rates and noise can lead to important features/information being missed Small strain signals in concrete necessitate high resolution, low noise instrumentation Full system bench-testing should be carried out before installation Unforeseen issues will be discovered - easier/cheaper to diagnose and resolve 2-4 weeks of stable, issue-free operation indicates the system is ready for install Instrumentation accessibility is more important than short sensor cables Reduces difficulty (and cost) of routine maintenance Software for instrumentation equipment generally not very flexible In some cases, data can only be output to files (i.e. new file every X minutes) In many cases, limited or no support for any OS other than Windows 31

23 Data Communications Bandwidth requirements for continuous raw data transfer limited connectivity options 3G upload speeds were too slow, 4G or landline broadband were required Poor cellular modem reliability lead to frequent down-time Out of 3 modems, only 1 was still working after 1 year in the field Fortunately the other bridge had broadband access

24 Data Large volume of raw data - roughly 30GB per day per bridge For research purposes, the aim is to capture all raw data In practice this will be less Data Compression is very effective for raw sensor data 30-40% can often be achieved in real-time Automated solutions needed to continuously transfer data from the bridges to local storage and compute facilities 33

25 Future Work

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28 Organisational Considerations What is the business case for monitoring of small bridges? What obstacles are there for monitoring of small bridges and how can they be overcome?

29 Thank You Peter Runcie Business Leader New Industries and Platforms Phone:

Case Studies: Structural Health Monitoring for Real-Time Asset Management of Small Bridges

Case Studies: Structural Health Monitoring for Real-Time Asset Management of Small Bridges Peter Runcie 1, Mehrisadat Makki Alamdari 2, Tim Pitman 3, Nguyen Lu Dang Khoa 4 1,3,4 {peter.runcie, tim.pitman,