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作者:

Zhou, Tong (Zhou, Tong.) | Peng, Yongbo (Peng, Yongbo.)

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摘要:

An active-learning reliability method called the AEM-PDEM is proposed that combines adaptive ensemble of metamodels (EM) and the probability density evolution method (PDEM). Three critical aspects are addressed in this method. First, the ensemble of three diverse metamodels, i.e., the polynomial chaos Kriging (PCK), the lowrank approximation (LRA) and the support vector regression (SVR), is built by weighted combination according to their global error measures, which enables to provide both predicted value and variance. Second, the EM predictions at the training samples are replaced by the true computational model responses, so as to secure the accuracy of failure probability estimate. Third, according to the PDEM-oriented expected improvement function (PEIF), a multi-point enrichment process is developed based on the EM and the three component metamodels. Then, three numerical examples are investigated and comparisons are made between the AEM-PDEM and other existing reliability methods. Results demonstrate that, in comparison with the existing APCK-PDEM, the AEMPDEM needs roughly 85-95% of the number of computational model evaluations. More importantly, it only requires approximately 30-45% of the number of iterations during the active-learning process. As a result, it just consumes nearly 35-50% of computational time of the APCK-PDEM, especially in high-dimensional dynamic problems and practical complex engineering problems.

关键词:

Probability density evolution method function Active -learning Reliability analysis PDEM-oriented expected improvement Ensemble of metamodels Multi -point enrichment strategy

作者机构:

  • [ 1 ] [Zhou, Tong]Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai 200092, Peoples R China
  • [ 2 ] [Peng, Yongbo]Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai 200092, Peoples R China
  • [ 3 ] [Zhou, Tong]Tongji Univ, Coll Civil Engn, Shanghai 200092, Peoples R China
  • [ 4 ] [Peng, Yongbo]Tongji Univ, Shanghai Inst Disaster Prevent & Relief, Shanghai 200092, Peoples R China
  • [ 5 ] [Peng, Yongbo]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China

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来源 :

RELIABILITY ENGINEERING & SYSTEM SAFETY

ISSN: 0951-8320

年份: 2022

卷: 228

8 . 1

JCR@2022

8 . 1 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:49

JCR分区:1

中科院分区:1

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SCOPUS被引频次: 18

ESI高被引论文在榜: 0 展开所有

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