Probabilistic Failure Analysis by Importance Sampling Markov Chain Simulation
Publication: Journal of Engineering Mechanics
Volume 130, Issue 3
Abstract
A probabilistic approach for failure analysis is presented in this paper, which investigates the probable scenarios that occur in case of failure of engineering systems with uncertainties. Failure analysis can be carried out by studying the statistics of system behavior corresponding to the random samples of uncertain parameters that are distributed as the conditional distribution given that the failure event has occurred. This necessitates the efficient generation of conditional samples, which is in general a highly nontrivial task. A simulation method based on Markov Chain Monte Carlo simulation is proposed to efficiently generate the conditional samples. It makes use of the samples generated from importance sampling simulation when the performance reliability is computed. The conditional samples can be used for statistical averaging to yield unbiased and consistent estimate of conditional expectations of interest for failure analysis. Examples are given to illustrate the application of the proposed simulation method to probabilistic failure analysis of static and dynamic structural systems.
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Published In
Journal of Engineering Mechanics
Volume 130 • Issue 3 • March 2004
Pages: 303 - 311
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Sep 27, 2002
Accepted: May 28, 2003
Published online: Feb 19, 2004
Published in print: Mar 2004
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