Markov Chain Monte Carlo-Based Method for Flaw Detection in Beams
Publication: Journal of Engineering Mechanics
Volume 133, Issue 12
Abstract
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.
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Acknowledgments
This work was performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory under Contract No. DOEW-7405-ENG-48.
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© 2007 ASCE.
History
Received: Oct 24, 2005
Accepted: Apr 23, 2007
Published online: Dec 1, 2007
Published in print: Dec 2007
Notes
Note. Associate Editor: Arvid Naess
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