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Structural reliability analysis by line sampling: A Bayesian active learning treatment.

Authors :
Dang, Chao
Valdebenito, Marcos A.
Faes, Matthias G.R.
Song, Jingwen
Wei, Pengfei
Beer, Michael
Source :
Structural Safety. Sep2023, Vol. 104, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Line sampling has been demonstrated to be a promising simulation method for structural reliability analysis, especially for assessing small failure probabilities. However, its practical performance can still be significantly improved by taking advantage of, for example, Bayesian active learning. Along this direction, a recently proposed 'partially Bayesian active learning line sampling' (PBAL-LS) method has shown to be successful. This paper aims at offering a more complete Bayesian active learning treatment of line sampling, resulting in a new method called 'Bayesian active learning line sampling' (BAL-LS). Specifically, we derive the exact posterior variance of the failure probability, which can measure our epistemic uncertainty about the failure probability more precisely than the upper bound given in PBAL-LS. Further, two essential components (i.e., learning function and stopping criterion) are proposed to facilitate Bayesian active learning, based on the uncertainty representation of the failure probability. In addition, the important direction can be automatically updated throughout the simulation, as one advantage directly inherited from PBAL-LS. The performance of BAL-LS is illustrated by four numerical examples. It is shown that the proposed method is capable of evaluating extremely small failure probabilities with desired efficiency and accuracy. • Classical line sampling is treated by Bayesian active learning. • Bayesian active learning line sampling is proposed for structural reliability analysis. • Posterior variance of the failure probability is derived in an analytic expression. • A learning function and stopping criterion are proposed to enable Bayesian active learning. • Numerical studies indicate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01674730
Volume :
104
Database :
Academic Search Index
Journal :
Structural Safety
Publication Type :
Academic Journal
Accession number :
164923961
Full Text :
https://doi.org/10.1016/j.strusafe.2023.102351