Structural reliability analysis with extremely small failure probabilities

A quasi-Bayesian active learning method

authored by: Chao Dang, Alice Cicirello, Marcos A. Valdebenito, Matthias G.R. Faes, Pengfei Wei, Michael Beer
Abstract: The concept of Bayesian active learning has recently been introduced from machine learning to structural reliability analysis. Although several specific methods have been successfully developed, significant efforts are still needed to fully exploit their potential and to address existing challenges. This work proposes a quasi-Bayesian active learning method, called ‘Quasi-Bayesian Active Learning Cubature’, for structural reliability analysis with extremely small failure probabilities. The method is established based on a cleaver use of the Bayesian failure probability inference framework. To reduce the computational burden associated with the exact posterior variance of the failure probability, we propose a quasi posterior variance instead. Then, two critical elements for Bayesian active learning, namely the stopping criterion and the learning function, are developed subsequently. The stopping criterion is defined based on the quasi posterior coefficient of variation of the failure probability, whose numerical solution scheme is also tailored. The learning function is extracted from the quasi posterior variance, with the introduction of an additional parameter that allows multi-point selection and hence parallel distributed processing. By testing on four numerical examples, it is empirically shown that the proposed method can assess extremely small failure probabilities with desired accuracy and efficiency.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): TU Dortmund University
Northwestern Polytechnical University
University of Liverpool
Tongji University
University of Cambridge
Type: Article
Journal: Probabilistic Engineering Mechanics
Volume: 76
No. of pages: 12
ISSN: 0266-8920
Publication date: 04.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Statistical and Nonlinear Physics, Civil and Structural Engineering, Nuclear Energy and Engineering, Condensed Matter Physics, Aerospace Engineering, Ocean Engineering, Mechanical Engineering
Electronic version(s): https://doi.org/10.1016/j.probengmech.2024.103613 (Access: Open)