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Proceedings Paper

Automated analysis of long-term bridge behavior and health using a cyber-enabled wireless monitoring system
Author(s): Sean M. O'Connor; Yilan Zhang; Jerome Lynch; Mohammed Ettouney; Gwen van der Linden
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Paper Abstract

A worthy goal for the structural health monitoring field is the creation of a scalable monitoring system architecture that abstracts many of the system details (e.g., sensors, data) from the structure owner with the aim of providing “actionable” information that aids in their decision making process. While a broad array of sensor technologies have emerged, the ability for sensing systems to generate large amounts of data have far outpaced advances in data management and processing. To reverse this trend, this study explores the creation of a cyber-enabled wireless SHM system for highway bridges. The system is designed from the top down by considering the damage mechanisms of concern to bridge owners and then tailoring the sensing and decision support system around those concerns. The enabling element of the proposed system is a powerful data repository system termed SenStore. SenStore is designed to combine sensor data with bridge meta-data (e.g., geometric configuration, material properties, maintenance history, sensor locations, sensor types, inspection history). A wireless sensor network deployed to a bridge autonomously streams its measurement data to SenStore via a 3G cellular connection for storage. SenStore securely exposes the bridge meta- and sensor data to software clients that can process the data to extract information relevant to the decision making process of the bridge owner. To validate the proposed cyber-enable SHM system, the system is implemented on the Telegraph Road Bridge (Monroe, MI). The Telegraph Road Bridge is a traditional steel girder-concrete deck composite bridge located along a heavily travelled corridor in the Detroit metropolitan area. A permanent wireless sensor network has been installed to measure bridge accelerations, strains and temperatures. System identification and damage detection algorithms are created to automatically mine bridge response data stored in SenStore over an 18-month period. Tools like Gaussian Process (GP) regression are used to predict changes in the bridge behavior as a function of environmental parameters. Based on these analyses, pertinent behavioral information relevant to bridge management is autonomously extracted.

Paper Details

Date Published: 10 April 2014
PDF: 11 pages
Proc. SPIE 9063, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014, 90630Y (10 April 2014); doi: 10.1117/12.2045244
Show Author Affiliations
Sean M. O'Connor, Univ. of Michigan (United States)
Yilan Zhang, Univ. of Michigan (United States)
Jerome Lynch, Univ. of Michigan (United States)
Mohammed Ettouney, Weidlinger Associates, Inc. (United States)
Gwen van der Linden, SC Solutions, Inc. (United States)


Published in SPIE Proceedings Vol. 9063:
Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014
H. Felix Wu; Tzu-Yang Yu; Andrew L. Gyekenyesi; Peter J. Shull, Editor(s)

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