Structural reliability analysis: A Bayesian perspective

Chao Dang, Marcos A. Valdebenito, Matthias G.R. Faes, Pengfei Wei, Michael Beer

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation, as opposed to the classical frequentist perspective. For this purpose, a principled Bayesian Failure Probability Inference (BFPI) framework is first developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error. Especially, the posterior variance of the failure probability is derived in a semi-analytical form, and the Gaussianity of the posterior failure probability distribution is investigated numerically. Then, a Parallel Adaptive-Bayesian Failure Probability Learning (PA-BFPL) method is proposed within the Bayesian framework. In the PA-BFPL method, a variance-amplified importance sampling technique is presented to evaluate the posterior mean and variance of the failure probability, and an adaptive parallel active learning strategy is proposed to identify multiple updating points at each iteration. Thus, a novel advantage of PA-BFPL is that both prior knowledge and parallel computing can be used to make inference about the failure probability. Four numerical examples are investigated, indicating the potential benefits by advocating a Bayesian approach to failure probability estimation.

Original languageEnglish
Article number102259
JournalStructural Safety
StatePublished - Nov 2022
Externally publishedYes


  • Bayesian inference
  • Failure probability
  • Gaussian process
  • Numerical uncertainty
  • Parallel computing


Dive into the research topics of 'Structural reliability analysis: A Bayesian perspective'. Together they form a unique fingerprint.

Cite this