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Research Article

Structural damage detection and assessment by adaptive Markov chain Monte Carlo simulation

  1. Ka-Veng Yuen1,
  2. James L. Beck2,*,
  3. Siu Kui Au3

Article first published online: 6 SEP 2004

DOI: 10.1002/stc.47

Issue

Structural Control and Health Monitoring

Structural Control and Health Monitoring

Volume 11, Issue 4, pages 327–347, October/December 2004

Additional Information

How to Cite

Yuen, K.-V., Beck, J. L. and Au, S. K. (2004), Structural damage detection and assessment by adaptive Markov chain Monte Carlo simulation. Struct. Control Health Monit., 11: 327–347. doi: 10.1002/stc.47

Author Information

  1. 1

    Department of Civil and Environmental Engineering, University of Macau, China

  2. 2

    Division of Engineering and Applied Science, California Institute of Technology, Mail Code 104-44, Pasadena, CA 91125, U.S.A.

  3. 3

    School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798

*Division of Engineering and Applied Science, California Institute of Technology, Mail Code 104-44, Pasadena, CA 91125, U.S.A.

Publication History

  1. Issue published online: 3 DEC 2004
  2. Article first published online: 6 SEP 2004
  3. Manuscript Accepted: 28 MAY 2004
  4. Manuscript Revised: 12 APR 2004
  5. Manuscript Received: 11 OCT 2003

Funded by

  • California Institute of Technology

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Keywords:

  • Bayesian updating;
  • damage assessment;
  • damage detection;
  • Markov chain Monte Carlo simulation;
  • structural reliability;
  • failure probability

Abstract

This paper uses Bayesian updating of dynamic models of structures to perform all four levels of structural damage detection and assessment: damage indication, its location and severity, and its impact on the structural reliability. The numerical integration that is required in Bayesian updating is known to be computationally prohibitive for problems with high dimensions. The proposed approach uses Markov chain Monte Carlo simulation based on the Metropolis–Hastings algorithm to tackle this problem in conjunction with an adaptive concept to obtain information about the important regions of the updated probability distribution in an efficient manner. The Markov chain samples are then used to estimate the damage probabilities by statistical averaging for damage detection and assessment. The proposed approach is illustrated using the ASCE-IASC four-storey benchmark structure for various amounts of modal data that produce globally identifiable, locally identifiable and unidentifiable cases. Copyright © 2004 John Wiley & Sons, Ltd.

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