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Refined parallel adaptive Bayesian quadrature for estimating small failure probabilities

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成果类型:
期刊论文
作者:
Wang, Lei;Hu, Zhuo;Dang, Chao;Beer, Michael
通讯作者:
Wang, L
作者机构:
[Wang, Lei; Hu, Zhuo] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China.
[Beer, Michael; Dang, Chao; Hu, Zhuo] Leibniz Univ Hannover, Inst Risk & Reliabil, Callinstr 34, Hannover 30167, Germany.
[Beer, Michael] Univ Liverpool, Inst Risk & Uncertainty, Liverpool L69 7ZF, England.
[Beer, Michael] Tongji Univ, Int Joint Res Ctr Resilient Infrastructure, Shanghai 200092, Peoples R China.
[Beer, Michael] Tongji Univ, Int Joint Res Ctr Engn Reliabil & Stochast Mech, Shanghai 200092, Peoples R China.
通讯机构:
[Wang, L ] C
Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Bayesian active learning;Small failure probability;Gaussian process;Importance ball sampling;Parallel computing
期刊:
Reliability Engineering & System Safety
ISSN:
0951-8320
年:
2024
卷:
244
基金类别:
National Key Research and Devel-opment Program of China [2021YFB2600900]; Graduate Student Research Innovation Project of Hunan Province (CSUST) , China [CX20200843]; China Scholarship Council
机构署名:
本校为第一且通讯机构
院系归属:
土木工程学院
摘要:
Bayesian active learning methods have emerged for structural reliability analysis, showcasing more attractive features compared to existing active learning methods. The parallel adaptive Bayesian quadrature (PABQ) method, as a representative of them, allows to efficiently assessing small failure probabilities but faces the problem of empirically specifying several important parameters. The unreasonable parameter settings could lead to the inaccurate estimates of failure probability or the non -convergence of active learning. This study proposes a refined PABQ (R-PABQ) method by presenting thre...

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