Journal Article10.1016/j.strusafe.2022.102259
Structural reliability analysis: A Bayesian perspective
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TL;DR: In this article , a principled Bayesian Failure Probability Inference (BFPI) framework is developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error.
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About: This article is published in Structural Safety. The article was published on 01 Nov 2022. The article focuses on the topics: Reliability (semiconductor) & Bayesian probability.
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Citations
Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review
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TL;DR: In this paper , an extensive review of Monte Carlo methods along with insightful summaries of developments of existing numerical methods, ranging from the general formulation, sub-categories and variants, to their combined uses with other simulation techniques and surrogate models, as well as the key advantages and assumptions.
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Structural reliability analysis by line sampling: A Bayesian active learning treatment
TL;DR: In this article , the authors proposed a more complete Bayesian active learning treatment of line sampling, resulting in a new method called "Bayesian Active Learning Line Sampling" (BAL-LS), which is capable of evaluating extremely small failure probabilities with desired efficiency and accuracy.
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Estimation of small failure probabilities by partially Bayesian active learning line sampling: Theory and algorithm
TL;DR: Wang et al. as mentioned in this paper proposed a partially Bayesian active learning line sampling (PBAL-LS) method to evaluate the failure probability integral in the LS method, which allows to incorporate prior knowledge and model the discretization error.
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Bayesian updating with two-step parallel Bayesian optimization and quadrature
TL;DR: In this article , a Bayesian updating approach, called parallel Bayesian optimization and quadrature (PBOQ), is proposed to make the full use of prior knowledge and parallel computing to reduce the computational burden of model updating.
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Semi-Bayesian active learning quadrature for estimating extremely low failure probabilities
Chao Dang,Michael Francis Beer +1 more
- 01 Mar 2024
TL;DR: This study introduces semi-Bayesian active learning quadrature (SBALQ) for estimating extremely low failure probabilities, leveraging the Bayesian failure probability inference framework with a novel stopping criterion and learning function to improve efficiency and accuracy.
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