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Fast Bayesian updating of large-scale finite element models using CMS technique and surrogate models
dc.creator | Papadimitriou, C. | en |
dc.creator | Papadioti, D. C. | en |
dc.date.accessioned | 2015-11-23T10:42:57Z | |
dc.date.available | 2015-11-23T10:42:57Z | |
dc.date.issued | 2012 | |
dc.identifier.isbn | 9781622768257 | |
dc.identifier.uri | http://hdl.handle.net/11615/31688 | |
dc.description.abstract | A Bayesian probabilistic framework for parameter estimation is applied for updating large-order finite element models of structures using response measurements. Fast and accurate component mode synthesis (CMS) techniques are proposed, consistent with the finite element model parameterization, to achieve drastic reductions in computational effort. Further computational savings are achieved by adopting heuristic approximations based on surrogate models. The computational efficiency and accuracy of the proposed techniques is demonstrated by updating a finite element model of a bridge involving hundreds of thousands of degrees of freedom. © (2012) by the Katholieke Universiteit Leuven Department of Mechanical Engineering All rights reserved. | en |
dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-84906330332&partnerID=40&md5=fc9b715e3d66d2690bbbd176858bb33c | |
dc.subject | Computer simulation | en |
dc.subject | Modal analysis | en |
dc.subject | Structural dynamics | en |
dc.subject | Bayesian probabilistic frameworks | en |
dc.subject | Bayesian updating | en |
dc.subject | Component mode synthesis | en |
dc.subject | Computational effort | en |
dc.subject | Computational savings | en |
dc.subject | Heuristic approximations | en |
dc.subject | Response measurement | en |
dc.subject | Surrogate model | en |
dc.subject | Finite element method | en |
dc.title | Fast Bayesian updating of large-scale finite element models using CMS technique and surrogate models | en |
dc.type | conferenceItem | en |
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