www.data61.csiro.au

Structural Health Monitoring of Small Bridges

November 2017

Peter Runcie | Business Leader – New Industries and Platformspeter.runcie@data61.csiro.auPhone: +61 2 9490 5940

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 AreasAgriculture

Energy

Food and Nutrition

Health and Biosecurity

Land and Water

Manufacturing

Mineral Resources

Oceans and Atmosphere

Astronomy andSpace Science

Australian Animal Health Laboratory

Data and Digital

Marine National Facility

National Computing Infrastructure

National Research Collections of Australia

Structural Health Monitoring Autonomous Drones

Predictive Analytics Transport Systems Optimisation

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1958 Photo, Lindsay Bridge, Creative Commons License https://creativecommons.org/licenses/by/2.0/

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

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

Data Analysis

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.

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Damage Identification

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Severity Assessment

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

-1.5

-1

-0.5

0

0.5

Decis

ion v

alu

es

Test event index

Joint 5

Joint 4

Localisation

Detection

2) Operational modal analysis (OMA)

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• 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

3) Traffic monitoring and characterisation

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Event

Data Acquisition Signal Processing

• Number of axles.

• Axles’ spacing.

• Speed estimation.

• Axles’ weights.

• Gross weight.

Traffic 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.

4) Load Cycle Counting

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• Fatigue life assessment of a structure subjected to a non-constant amplitude loading can be performed in the time domain using rainflowcycle counting.

• The rainflow method is used for counting the fatigue cycles (stress-reversals) and to obtain equivalent constant amplitude cycles from the measured strain data.

Governor Macquarie Drive Bridge, NSW

•Double Culvert (2 spans, 3 shear walls)

• ~4m spans

Sensors

• Strain gauges

• Accelerometers

• Thermocouple

See conference paper for detail.

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.

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”))

OMA for Cable-Stayed Bridge

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BWIM for Cable-Stay BridgeUsing same sensors for SHM for axle spacing, loading and GVM

Lab Test Rig Small BridgeTheoretical Model

Lessons so far..

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

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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)

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Instrumentation

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• 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

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

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

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Future Work

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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?

www.data61.csiro.au

Thank You

Peter Runcie | Business Leader – New Industries and Platformspeter.runcie@data61.csiro.auPhone: +61 2 9490 5940