Bayesian model selection and parameter estimation for fatigue damage progression models in composites

J. Chiachío, M. Chiachío, A. Saxena, S. Sankararaman, G. Rus, K. Goebel

Research output: Contribution to journalArticle

33 Citations (Scopus)
13 Downloads (Pure)

Abstract

A Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites. Candidate models, that are first parameterized through a Global Sensitivity Analysis, are ranked based on estimated probabilities that measure the extent of agreement of their predictions with observed data. A case study is presented using multi-scale fatigue damage data from a cross-ply carbon–epoxy laminate. The results show that, for this case, the most probable model class among the competing candidates is the one that involves the simplest damage mechanics. The principle of Ockham's razor seems to hold true for the composite materials investigated here since the data-fit of more complex models is penalized, as they extract more information from the data.
Original languageEnglish
Pages (from-to)361-373
Number of pages13
JournalInternational Journal of Fatigue
Volume70
Early online date21 Aug 2014
DOIs
Publication statusPublished - 31 Jan 2015

Keywords

  • Bayesian models
  • parameter estimation
  • fatigue damage
  • composites

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  • Prizes

    Extraordinary PhD Award

    Juan Chiachio-Ruano (Recipient), Nov 2018

    Prize: Prize (including medals and awards)

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