Handle URI:
http://hdl.handle.net/10754/624791
Title:
Bayesian inference and model comparison for metallic fatigue data
Authors:
Babuska, Ivo; Sawlan, Zaid A; Scavino, Marco ( 0000-0001-5114-853X ) ; Szabó, Barma; Tempone, Raul ( 0000-0003-1967-4446 )
Abstract:
In 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. 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
Issue Date:
6-Jan-2016
Type:
Poster
Appears in Collections:
Posters; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

Full metadata record

DC FieldValue Language
dc.contributor.authorBabuska, Ivoen
dc.contributor.authorSawlan, Zaid Aen
dc.contributor.authorScavino, Marcoen
dc.contributor.authorSzabó, Barmaen
dc.contributor.authorTempone, Raulen
dc.date.accessioned2017-06-08T06:32:26Z-
dc.date.available2017-06-08T06:32:26Z-
dc.date.issued2016-01-06-
dc.identifier.urihttp://hdl.handle.net/10754/624791-
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. 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.en
dc.subjectBayesianen
dc.titleBayesian inference and model comparison for metallic fatigue dataen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateJanuary 5-10, 2016en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)en
dc.conference.locationKAUSTen
dc.contributor.institutionUniversity of Texas at Austinen
dc.contributor.institutionUniversidad de la Repúblicaen
dc.contributor.institutionWashington University in St.Louisen
kaust.authorSawlan, Zaid Aen
kaust.authorScavino, Marcoen
kaust.authorTempone, Raulen
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