Abstract
This paper presents an efficient analytical Bayesian method for reliability and system response updating without using simulations. The method includes additional information such as measurement data via Bayesian modeling to reduce estimation uncertainties. Laplace approximation method is used to evaluate Bayesian posterior distributions analytically. An efficient algorithm based on inverse first-order reliability method is developed to evaluate system responses given a reliability index or confidence interval. Since the proposed method involves no simulations such as Monte Carlo or Markov chain Monte Carlo simulations, the overall computational efficiency improves significantly, particularly for problems with complicated performance functions. A practical fatigue crack propagation problem with experimental data, and a structural scale example are presented for methodology demonstration. The accuracy and computational efficiency of the proposed method are compared with traditional simulation-based methods.
Original language | English (US) |
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Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | Reliability Engineering and System Safety |
Volume | 97 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2012 |
Externally published | Yes |
Keywords
- Bayesian
- FORM
- First-order reliability method
- Inverse FORM
- Inverse reliability method
- Laplace
- Reliability updating
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering