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Markov chain monte carlo-based method for flaw detection in beams / Ronald E. Glaser in Journal of engineering mechanics, Vol. 133 N°12 (Decembre 2007) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 133 N°12 (Decembre 2007) . - pp.1258–1267 Titre : Markov chain monte carlo-based method for flaw detection in beams Type de document : texte imprimé Auteurs : Ronald E. Glaser, Auteur ; Christopher L. Lee, Auteur ; John J. Nitao, Auteur Année de publication : 2007 Article en page(s) : pp.1258–1267 Note générale : Mécaanique appliquée Langues : Anglais (eng) Mots-clés : Markov  chains  Monte  Carlo  method  Bayesian  analysis  Structural  models  Damage  Cantilevers Résumé : 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 En ligne : http://ascelibrary.org/action/showAbstract?page=1258&volume=133&issue=12&journal [...] [article] Markov chain monte carlo-based method for flaw detection in beams [texte imprimé] / Ronald E. Glaser, Auteur ; Christopher L. Lee, Auteur ; John J. Nitao, Auteur . - 2007 . - pp.1258–1267. Mécaanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 133 N°12 (Decembre 2007) . - pp.1258–1267 Mots-clés : Markov  chains  Monte  Carlo  method  Bayesian  analysis  Structural  models  Damage  Cantilevers Résumé : 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 En ligne : http://ascelibrary.org/action/showAbstract?page=1258&volume=133&issue=12&journal [...]