Bayesian inference and model comparison for metallic fatigue data
Name:
1-s2.0-S0045782516300354-main.pdf
Size:
2.868Mb
Format:
PDF
Description:
Accepted Manuscript
Type
ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2016-02-23Online Publication Date
2016-02-23Print Publication Date
2016-06Permanent link to this record
http://hdl.handle.net/10754/597084
Metadata
Show full item recordAbstract
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. 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.Citation
Bayesian inference and model comparison for metallic fatigue data 2016 Computer Methods in Applied Mechanics and EngineeringSponsors
Z. 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.Publisher
Elsevier BVarXiv
1512.01779Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S0045782516300354ae974a485f413a2113503eed53cd6c53
10.1016/j.cma.2016.02.013