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Damage Detection of Bridge Network With Spatiotemporal Pattern Network

[+] Author Affiliations
Chao Liu, Yongqiang Gong, Simon Laflamme, Brent Phares, Soumik Sarkar

Iowa State University, Ames, IA

Paper No. DSCC2016-9858, pp. V001T12A003; 10 pages
doi:10.1115/DSCC2016-9858
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME

abstract

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. In particular, scalable data-driven approaches is of great interest, because it can deal with large volume of streaming data without requiring models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and inference and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is applied to explore spatiotemporal behaviors in bridge network. Data from strain gauges installed on two bridges are simulated by finite element method, and the causality among strain data in spatial and temporal resolutions is analyzed. Case studies are conducted for truck identification and damage detection from simulation data. Results show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, and (iii) detecting and localizing damage via the comparison of behaviors within the bridge network.

Copyright © 2016 by ASME

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