Structural reliability analysis by line sampling

A Bayesian active learning treatment

verfasst von

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

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.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Technische Universität Dortmund
Northwestern Polytechnical University
The University of Liverpool
International Joint Research Center for Engineering Reliability and Stochastic Mechanics
Tongji University

Typ

Artikel

Journal

Structural safety

Band

104

ISSN

0167-4730

Publikationsdatum

09.2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Tief- und Ingenieurbau, Bauwesen, Sicherheit, Risiko, Zuverlässigkeit und Qualität

Elektronische Version(en)

https://doi.org/10.1016/j.strusafe.2023.102351 (Zugang: Geschlossen)