Presentation on theme: "Implications on the Use of Continuous Dynamic Monitoring Data for Structural Evaluation Implications on the Use of Continuous Dynamic Monitoring Data for."— Presentation transcript:
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 17-18 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
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 -0.01 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 - 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: 37500 @ 41.7 Hz number of response channels: 17
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-
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.
Your consent to our cookies if you continue to use this website.