Model selection, updating, and averaging for probabilistic fatigue damage prognosis

Xuefei Guan, Ratneshwar Jha, Yongming Liu

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

This paper presents a method for fatigue damage propagation model selection, updating, and averaging using reversible jump Markov chain Monte Carlo simulations. Uncertainties from model choice, model parameter, and measurement are explicitly included using probabilistic modeling. Response measurement data are used to perform Bayesian updating to reduce the uncertainty of fatigue damage prognostics. All the variables of interest, including the Bayes factors for model selection, the posterior distributions of model parameters, and the averaged results of system responses are obtained by one reversible jump Markov chain Monte Carlo simulation. The overall procedure is demonstrated by a numerical example and a practical fatigue problem involving two fatigue crack growth models. Experimental data are used to validate the performance of the method.

Original languageEnglish (US)
Pages (from-to)242-249
Number of pages8
JournalStructural Safety
Volume33
Issue number3
DOIs
StatePublished - May 1 2011
Externally publishedYes

Keywords

  • Bayesian
  • Fatigue
  • Model averaging
  • Model selection
  • Model updating
  • Reversible jump MCMC
  • Uncertainty

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Safety, Risk, Reliability and Quality

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