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dc.contributor.authorBabuška, Ivo
dc.contributor.authorSawlan, Zaid A
dc.contributor.authorScavino, Marco
dc.contributor.authorSzabó, Barna
dc.contributor.authorTempone, Raul
dc.date.accessioned2016-02-24T09:34:40Z
dc.date.available2016-02-24T09:34:40Z
dc.date.issued2016-02-23
dc.identifier.citationBayesian inference and model comparison for metallic fatigue data 2016 Computer Methods in Applied Mechanics and Engineering
dc.identifier.issn00457825
dc.identifier.doi10.1016/j.cma.2016.02.013
dc.identifier.urihttp://hdl.handle.net/10754/597084
dc.description.abstractIn this work, we present a statistical treatment of stress-life (S-N) data drawn from a collection of records of fatigue experiments that were performed on 75S-T6 aluminum alloys. Our main objective is to predict the fatigue life of materials by providing a systematic approach to model calibration, model selection and model ranking with reference to S-N data. To this purpose, we consider fatigue-limit models and random fatigue-limit models that are specially designed to allow the treatment of the run-outs (right-censored data). We first fit the models to the data by maximum likelihood methods and estimate the quantiles of the life distribution of the alloy specimen. To assess the robustness of the estimation of the quantile functions, we obtain bootstrap confidence bands by stratified resampling with respect to the cycle ratio. We then compare and rank the models by classical measures of fit based on information criteria. We also consider a Bayesian approach that provides, under the prior distribution of the model parameters selected by the user, their simulation-based posterior distributions. We implement and apply Bayesian model comparison methods, such as Bayes factor ranking and predictive information criteria based on cross-validation techniques under various a priori scenarios.
dc.description.sponsorshipZ. Sawlan, M. Scavino and R. Tempone are members of King Abdullah University of Science and Technology (KAUST) SRI Center for Uncertainty Quantification in Computational Science and Engineering.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0045782516300354
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computer Methods in Applied Mechanics and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods in Applied Mechanics and Engineering, 23 February 2016. DOI: 10.1016/j.cma.2016.02.013
dc.subjectMetallic fatigue data
dc.subjectFatigue life prediction
dc.subjectRandom fatigue–limit models
dc.subjectMaximum likelihood methods
dc.subjectBayesian computational techniques for model calibration/ranking
dc.subjectPredictive accuracy for Bayesian models
dc.titleBayesian inference and model comparison for metallic fatigue data
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalComputer Methods in Applied Mechanics and Engineering
dc.eprint.versionPost-print
dc.contributor.institutionICES, The University of Texas at Austin, Austin, USA
dc.contributor.institutionWashington University in St. Louis, St. Louis, USA
dc.contributor.institutionInstituto de Estadística (IESTA), Universidad de la República, Montevideo, Uruguay
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.arxivid1512.01779
kaust.personSawlan, Zaid A
kaust.personScavino, Marco
kaust.personTempone, Raul
refterms.dateFOA2018-02-23T00:00:00Z
dc.date.published-online2016-02-23
dc.date.published-print2016-06


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