Markov Chain Monte Carlo-Based Method for Flaw Detection in Beams

A Bayesian inference methodology using a Markov chain Monte Carlo (MCMC) sampling procedure is presented for estimating the parameters of computational structural models. This methodology combines prior information, measured data, and forward models to produce a posterior distribution for the system...

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Bibliographic Details
Published in:Journal of engineering mechanics 2007-12, Vol.133 (12), p.1258-1267
Main Authors: Glaser, Ronald E, Lee, Christopher L, Nitao, John J, Hickling, Tracy L, Hanley, William G
Format: Article
Language:English
Subjects:
Exact sciences and technology
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
Physics
Solid mechanics
Structural and continuum mechanics
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Summary:A Bayesian inference methodology using a Markov chain Monte Carlo (MCMC) sampling procedure is presented for estimating the parameters of computational structural models. This methodology combines prior information, measured data, and forward models to produce a posterior distribution for the system parameters of structural models that is most consistent with all available data. The MCMC procedure is based upon a Metropolis-Hastings algorithm that is shown to function effectively with noisy data, incomplete data sets, and mismatched computational nodes/measurement points. A series of numerical test cases based upon a cantilever beam is presented. The results demonstrate that the algorithm is able to estimate model parameters utilizing experimental data for the nodal displacements resulting from specified forces.
ISSN:0733-9399
1943-7889
DOI:10.1061/(ASCE)0733-9399(2007)133:12(1258)