A 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 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.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 936952
- Report Number(s):
- UCRL-JRNL-225176
- Journal Information:
- Journal of Engineering Mechanics, vol. 133, no. 12, December 1, 2007, pp. 1258-1267, Journal Name: Journal of Engineering Mechanics, vol. 133, no. 12, December 1, 2007, pp. 1258-1267 Journal Issue: 12 Vol. 133
- Country of Publication:
- United States
- Language:
- English
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