Yuen, Ka-Veng and Beck, James L. and Au, Siu Kui (2004) Structural Damage Detection and Assessment using Adaptive Markov Chain Monte Carlo Simulation. Structural Control and Health Monitoring, 11 (4). pp. 327-347. ISSN 1545-2255. http://resolver.caltech.edu/CaltechAUTHORS:20120817-165443909
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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.
|Additional Information:||Copyright © 2004 John Wiley & Sons, Ltd. Received 11 October 2003, Revised 12 April 2004, Accepted 28 May 2004, Published online 6 September 2004. Part of this work was prepared when the first and third authors were at graduate standing at the California Institute of Technology. This institution’s generous financial support is gratefully acknowledged.|
|Subject Keywords:||Bayesian updating; damage assessment; damage detection; Markov chain Monte Carlo simulation; structural reliability; failure probability|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Carmen Nemer-Sirois|
|Deposited On:||20 Aug 2012 20:16|
|Last Modified:||23 Aug 2016 10:16|
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