Technical Paper
Jan 5, 2016

Reliability-Based Damage Identification Using Dynamic Signatures

Publication: Journal of Bridge Engineering
Volume 21, Issue 3

Abstract

Because of the influence of the stochastic nature of structural properties and field measurements, the identification result of damage extent is, in essence, uncertain, causing possible deviations and affecting the reliability of the identification result. To give a probabilistic measure on the identification result of damage extent, a reliability-based damage identification method based on dynamic signatures is proposed in this study. The Monte Carlo simulation in conjunction with the response surface method is used to perform the reliability analysis of damage identification with given accuracy, and a trial-and-error procedure is proposed for conducting damage identification with acceptable reliability. The influence of both structural uncertainties and measurement noises on damage identification can be taken into account in the present approach. Meanwhile, the proposed method is capable of assessing the robustness of different types of dynamic signatures against noise and the sensitivity of various random parameters involved in structural damage identification. The present approach is applied to the probabilistic damage identification of a simply supported beam and a long-span arch bridge, indicating the feasibility and effectiveness of the proposed method for both simple structural components and complex structural systems.

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Acknowledgments

This work was funded by the National Natural Science Foundation of China (51078150) and the State Key Laboratory of Subtropical Building Science, South China University of Technology (2013ZA01).

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Go to Journal of Bridge Engineering
Journal of Bridge Engineering
Volume 21Issue 3March 2016

History

Received: Aug 15, 2014
Accepted: Jun 15, 2015
Published online: Jan 5, 2016
Published in print: Mar 1, 2016
Discussion open until: Jun 5, 2016

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Authors

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Cheng Su, Ph.D. [email protected]
Professor, School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China Univ. of Technology, 381 Wushan Road, Tianhe, Guangzhou 510640, People’s Republic of China (corresponding author). E-mail: [email protected]
Wei Liao
Ph.D. Candidate, School of Civil Engineering and Transportation, South China Univ. of Technology, Tianhe, Guangzhou 510640, People’s Republic of China.
Lin Tan, Ph.D.
Graduate student, School of Civil Engineering and Transportation, South China Univ. of Technology, Tianhe, Guangzhou 510640, People’s Republic of China.
Ran Chen
Ph.D. Candidate, School of Civil Engineering and Transportation, South China Univ. of Technology, Tianhe, Guangzhou 510640, People’s Republic of China.

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