Implications on the Use of Continuous Dynamic Monitoring Data for Structural Evaluation Implications on the Use of Continuous Dynamic Monitoring Data for Structural Evaluation N.A. Londoño and D.T.Lau S.L. Desjardins N.A. Londoño and D.T.Lau Ottawa-Carleton Bridge Research Centre Dept of Civil & Environmental Engineering Carleton University Ottawa, Canada Ottawa, Canada International Collaboration Joint NCREE/JRC Workshop Earthquake Disaster Mitigation Research Methodologies, Facilities, Projects and Networking November 2003, Taipei, Taiwan

Overview Introduction Objectives Data processing SSI System Identification Verification Study Results Variability Study –Method & Scope –Data & SSI parameters –Results –Observations & Implications Real-Time Data Processing and Analysis Conclusions

Introduction Location of Confederation Bridge

-Confederation Bridge km long 100 year design service life Pre-stressed haunched single box-girder concrete superstructure –43 main spans of 250 m –40 m above sea level

-Typical Structural Unit- Rigid frame (192 m double cantilevers + 52 m rigid drop-in) 60 m simply supported expansion drop-in

-Dynamics Monitoring-  76 accelerometers  Data sampling rate: Hz  Hardware low-pass filter frequency: 50 Hz (anti-aliasing)  Data sample duration: 90 sec - 15 min (inc. 30 sec pre-trigger)  Operation modes: normal mode triggered mode

- Data Transmission -

Unique structure (length, loads, long service life)  Design assumptions  Verification of design parameters Structural condition  dynamic properties Advance towards the use of monitoring data for health monitoring Ideal setting for research Utilize data for more efficient operations of the facilities - Motivations -

Objectives Determine extent of variability of identified properties; Evaluate feasibility of using monitoring data for condition assessment/health monitoring of the bridge; Rapid and accurate condition assessment of the monitored structure –continuous basis –extreme events (e.g. wind storm, earthquake, ship impact)

Data Processing  Data checking & repair  Sampling gaps  Data duplication  Baseline adjustment  Low-pass filter  8 th Order Chebyshev Type I, 16.7 Hz cut-off  Data down-sampling

Anti-aliasing hardware filter 8-pole Bessel Low-Pass Filter No pass-band ripple Monotonic roll-off in pass-band & stop- band Constant delay in pass-band Low-harmonic distortion

Offline Anti-aliasing Filter Low pass-band ripple Steep monotonic roll-off Data filtered in forward and reverse directions to remove all phase distortion 8 th Order Chebyshev Type I Low-Pass

Offline High-Pass Filter 6 th order Chebyshev Type II -60 dB at 0.1 Hz dB at 0.3 Hz Zero pass-band ripple Applied in forward and reverse directions to remove phase distortion

System Identification - Stochastic Subspace Method - Pre-processed data Block Hankel Matrix Singular Value Decomposition (SVD) Stochastic State-Space Model Matrices Eigenvalue decomposition of A Frequencies Mode Shapes Damping Ratios Data cross-correlations w.r.t. reference channels Stochastic state-space model Observability Controllability

Verification Study -Monitoring Data- Wind-triggered ambient vibration Truck Traffic induced vibration Wind storm Nov

Verification Study -Results-

 Mode type: transverse  Dataset: wind-triggered Experimental 0.47 Hz Analytical 0.46 Hz Verification Study -Mode Shapes I-

 Mode type: vertical  Dataset: wind-storm Experimental 0.68 Hz Analytical 0.62 Hz Verification Study -Mode Shapes II-

 Mode type: vertical  Dataset: traffic Experimental 3.47 Hz Analytical 3.15 Hz Verification Study -Mode Shapes III-

Verification Study - Observations - Experimental Frequencies consistently higher than predicted Significant variability in the extracted structural dynamic properties

Variability Study Possible sources of variability: –Environmental Effects (e.g. temperature) –Differences in loading scenarios –Modeling & computational assumptions –Stiffness degradation ↔ deterioration/damage

Variability Study - Method & Scope - Ten datasets –Ambient operational responses –Similar environmental conditions & loading scenarios Determination of baseline level of variability –Attributable to numerical accuracy of data and identification process

Variability Study - Data & SSI parameters - Duration: 900 s Number of samples: 41.7 Hz number of response channels: 17

Variability Study - Typical Data -

SSI Stabilization Diagram

Wind Data - Hurricane Juan -

Traffic Data - Summer

Variability Study - Results - Modal Assurance Criterion MAC average

Variability Study - Results II - Localized Mode Shape discrepancies

Variability Study - Observations & Implications - Highly consistent modal frequencies –Positive finding for condition assessment Uncertainty in damping estimates –High complexity of damping behaviour Localized mode shape discrepancies –Challenge for mode-shape based damage location Ongoing research on variability due to environment and loading scenarios

Real-Time Data Processing and Analysis - Motivation & Objective - Accelerate data processing Provide timelier analysis results Facilitate engineering interpretation Develop continuous condition assessment platform Achieve more efficient operations and maintenance

Real-Time Data Processing and Analysis - Application Modules- Processing Module –Input: accelerometer measurements –Pre-processing –Double integration –Output: processed acceleration & displacements –Automatic error correction & data piping

Visualization Module –Real-time animation of 3D bridge model –Integrated plotting –Flexible user interaction Scaling factor View angle Playback speeds Recordings (avi format) Real-Time Data Processing and Analysis - Application Modules-

Analysis Module –Extensive plotting Time histories at each stage of processing –Power Spectral Density analysis –System Identification –Empirical Mode Decomposition – Hilbert Spectrum

Structural Health Monitoring of Plaza Bridge David T. Lau Dept of Civil and Environmental Eng Carleton University

Data Processing and Management Transducers Data Loggers Field Computer Remote Monitoring

Conclusions A better understanding of data variability and trends; Advance towards achieving continuous condition assessment objective; Development of powerful research and practical data processing and engineering information interpretation application tools; Application provides timely information to facilitate operations and life cycle maintenance of the monitored facilities.

Acknowledgements Funding support and assistance –Natural Sciences and Engineering Research Council Canada (NSERC); –Public Works and Government Services Canada (PWGSC); –Strait Crossing Bridge Ltd.