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Structural reliability analysis: A Bayesian perspective
https://orcid.org/0000-0001-7412-6309
Abstract Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation, as opposed to the classical frequentist perspective. For this purpose, a principled Bayesian Failure Probability Inference (BFPI) framework is first developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error. Especially, the posterior variance of the failure probability is derived in a semi-analytical form, and the Gaussianity of the posterior failure probability distribution is investigated numerically. Then, a Parallel Adaptive-Bayesian Failure Probability Learning (PA-BFPL) method is proposed within the Bayesian framework. In the PA-BFPL method, a variance-amplified importance sampling technique is presented to evaluate the posterior mean and variance of the failure probability, and an adaptive parallel active learning strategy is proposed to identify multiple updating points at each iteration. Thus, a novel advantage of PA-BFPL is that both prior knowledge and parallel computing can be used to make inference about the failure probability. Four numerical examples are investigated, indicating the potential benefits by advocating a Bayesian approach to failure probability estimation.
Highlights Estimation of the failure probability integral is treated as a Bayesian inference problem A principled Bayesian failure probability inference (BFPI) framework is proposed for the first time Posterior variance and distribution of the failure probability are derived and numerically investigated A parallel adaptive-Bayesian failure probability learning (PA-BFPL) method is proposed PA-BFPL enables to select multiple points at each iteration and supports parallel distributed processing
Structural reliability analysis: A Bayesian perspective
https://orcid.org/0000-0001-7412-6309
Abstract Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation, as opposed to the classical frequentist perspective. For this purpose, a principled Bayesian Failure Probability Inference (BFPI) framework is first developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error. Especially, the posterior variance of the failure probability is derived in a semi-analytical form, and the Gaussianity of the posterior failure probability distribution is investigated numerically. Then, a Parallel Adaptive-Bayesian Failure Probability Learning (PA-BFPL) method is proposed within the Bayesian framework. In the PA-BFPL method, a variance-amplified importance sampling technique is presented to evaluate the posterior mean and variance of the failure probability, and an adaptive parallel active learning strategy is proposed to identify multiple updating points at each iteration. Thus, a novel advantage of PA-BFPL is that both prior knowledge and parallel computing can be used to make inference about the failure probability. Four numerical examples are investigated, indicating the potential benefits by advocating a Bayesian approach to failure probability estimation.
Highlights Estimation of the failure probability integral is treated as a Bayesian inference problem A principled Bayesian failure probability inference (BFPI) framework is proposed for the first time Posterior variance and distribution of the failure probability are derived and numerically investigated A parallel adaptive-Bayesian failure probability learning (PA-BFPL) method is proposed PA-BFPL enables to select multiple points at each iteration and supports parallel distributed processing
Structural reliability analysis: A Bayesian perspective
https://orcid.org/0000-0001-7412-6309
Abstract Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation, as opposed to the classical frequentist perspective. For this purpose, a principled Bayesian Failure Probability Inference (BFPI) framework is first developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error. Especially, the posterior variance of the failure probability is derived in a semi-analytical form, and the Gaussianity of the posterior failure probability distribution is investigated numerically. Then, a Parallel Adaptive-Bayesian Failure Probability Learning (PA-BFPL) method is proposed within the Bayesian framework. In the PA-BFPL method, a variance-amplified importance sampling technique is presented to evaluate the posterior mean and variance of the failure probability, and an adaptive parallel active learning strategy is proposed to identify multiple updating points at each iteration. Thus, a novel advantage of PA-BFPL is that both prior knowledge and parallel computing can be used to make inference about the failure probability. Four numerical examples are investigated, indicating the potential benefits by advocating a Bayesian approach to failure probability estimation.
Highlights Estimation of the failure probability integral is treated as a Bayesian inference problem A principled Bayesian failure probability inference (BFPI) framework is proposed for the first time Posterior variance and distribution of the failure probability are derived and numerically investigated A parallel adaptive-Bayesian failure probability learning (PA-BFPL) method is proposed PA-BFPL enables to select multiple points at each iteration and supports parallel distributed processing
Structural reliability analysis: A Bayesian perspective
Dang, Chao (author) / Valdebenito, Marcos A. (author) / Faes, Matthias G.R. (author) / Wei, Pengfei (author) / Beer, Michael (author)
Structural Safety ; 99
2022-07-02
Article (Journal)
Electronic Resource
English
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