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Proceedings Paper

A video assisted approach for structural health monitoring of highway bridges under normal traffic
Author(s): Yangbo Chen; Chin-An Tan; Maria Q. Feng; Yoshio Fukuda
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Paper Abstract

Structural condition assessment of highway bridges is traditionally performed by visual inspections or nondestructive evaluation techniques, which are either slow, unreliable or detects only local flaws. Instrumentation of bridges with accelerometers and other sensors, however, can provide real-time data useful for monitoring the global structural conditions of the bridges due to ambient and forced excitations. This paper reports a video-assisted approach for structural health monitoring of highway bridges, with results from field tests and subsequent offline parameter identification. The field tests were performed on a short-span instrumented bridge. Videos of vehicles passing by were captured, synchronized with data recordings from the accelerometers. For short-span highway bridges, vibration is predominantly due to traffic excitation. A stochastic model of traffic excitation on bridges is developed assuming that vehicles traversing a bridge (modeled as an elastic beam) form a sequence of Poisson process moving loads and that the contact force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements. Basic information of vehicle types, arrival times and speeds are extracted from video images to develop a physics-based simulation model of the traffic excitation. This modeling approach aims at circumventing a difficulty in the system identification of bridge structural parameters. Current practice of system identification of bridge parameters is often based on the measured response (or system output) only, and knowledge of the input (traffic excitation) is either unknown or assumed, making it difficult to obtain an accurate assessment of the state of the bridge structures. Our model reveals that traffic excitation on bridges is spatially correlated, an important feature that is usually incorrectly ignored in most output-only methods. A recursive Bayesian filtering is formulated to monitor the evolution of the state of the bridge. The effectiveness and viability of this video-assisted approach are demonstrated by the field results.

Paper Details

Date Published: 11 April 2006
PDF: 18 pages
Proc. SPIE 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 61741V (11 April 2006); doi: 10.1117/12.658881
Show Author Affiliations
Yangbo Chen, Univ. of California/Irvine (United States)
Chin-An Tan, Wayne State Univ. (United States)
Maria Q. Feng, Univ. of California/Irvine (United States)
Yoshio Fukuda, Univ. of California/Irvine (United States)

Published in SPIE Proceedings Vol. 6174:
Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
Masayoshi Tomizuka; Chung-Bang Yun; Victor Giurgiutiu, Editor(s)

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