Yet another Bayesian active learning reliability analysis method

doi:10.1016/j.strusafe.2024.102539

Return

Yet another Bayesian active learning reliability analysis method

2025-01-01

Chao Dang *

Tong Zhou

Marcos A. Valdebenito

Matthias G.R. Faes

DOI：10.1016/j.strusafe.2024.102539

Original

Save

View PDF

Abstract

En 中文

The well-established Bayesian failure probability inference (BFPI) framework offers a solid foundation for developing new Bayesian active learning reliability analysis methods. However, there remains an open question regarding how to effectively leverage the posterior statistics of the failure probability to design the two key components for Bayesian active learning: the stopping criterion and learning function. In this study, we present another innovative Bayesian active learning reliability analysis method, called 'Weakly Bayesian Active Learning Quadrature' (WBALQ), which builds upon the BFPI framework to evaluate extremely small failure probabilities. Instead of relying on the posterior variance, we propose amore computationally feasible measure of the epistemic uncertainty in the failure probability by examining its posterior first absolute central moment. Based on this measure and the posterior mean of the failure probability, anew stopping criterion is devised. A recently developed numerical integrator is then employed to approximate the two analytically intractable terms inherent in the stopping criterion. Furthermore, a new learning function is proposed, which is partly derived from the epistemic uncertainty measure. The performance of the proposed method is demonstrated by five numerical examples. It is found that our method is able to assess extremely small failure probabilities with satisfactory accuracy and efficiency.

Keywords:

Structural reliability analysis

Extremely small failure probability

Bayesian active learning

Stopping criterion

Learning function

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.