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. 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.
Original language | English (US) |
---|---|
Pages (from-to) | 171-196 |
Number of pages | 26 |
Journal | Computer Methods in Applied Mechanics and Engineering |
Volume | 304 |
DOIs | |
State | Published - Feb 23 2016 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: 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.
ASJC Scopus subject areas
- General Physics and Astronomy
- Mechanics of Materials
- Mechanical Engineering
- Computational Mechanics
- Computer Science Applications