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An intelligent evidence-theory-based structural reliability analysis method based on convolutional neural network model

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成果类型:
期刊论文
作者:
Liu, Xin;Wan, Jun;Jia, Weiqiang;Peng, Xiang;Wu, Shaowei;...
通讯作者:
Liu, K
作者机构:
[Wan, Jun; Liu, Kai; Liu, Xin; Wu, Shaowei] Changsha Univ Sci & Technol, Hunan Prov Key Lab Safety Design & Reliabil Techno, Changsha 410114, Peoples R China.
[Jia, Weiqiang] Zhejiang Lab, Hangzhou 311121, Peoples R China.
[Peng, Xiang] Zhejiang Univ Technol, Coll Mech Engn, Hangzhou 310023, Peoples R China.
通讯机构:
[Liu, K ] C
Changsha Univ Sci & Technol, Hunan Prov Key Lab Safety Design & Reliabil Techno, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Evidence theory;Structural reliability analysis;Convolutional neural network;Approximation model;Local-densifying method
期刊:
Computer Methods in Applied Mechanics and Engineering
ISSN:
0045-7825
年:
2024
卷:
421
页码:
116804
基金类别:
CRediT authorship contribution statement Xin LIU: Writing – review & editing, Writing – original draft, Methodology, acquisition, Formal analysis, Data curation, Conceptualization. Jun Wan: Writing – original draft, Validation, Software, Investigation, Formal analysis, Data curation. Weiqiang Jia: Visualization, Validation, Supervision. Xiang Peng: Supervision, Software. Shaowei WU: Software, Project administration. Kai LIU: Writing – review & editing, Software, Methodology, Investigation, acquisition, Data curation.
机构署名:
本校为第一且通讯机构
院系归属:
汽车与机械工程学院
摘要:
Combined with the convolutional neural network (CNN) model, an intelligent structural reliability analysis method based on evidence theory is developed to improve the accuracy and efficiency of reliability analysis. Firstly, the uncertainties in engineering structures are described by the Frame of Discernment (FD) and the Basic Probability Assignment (BPA). Secondly, the Optimal Latin Hypercube Design (OLHD) is adopted to obtain the sample focal elements and the Sequential Quadratic Programming (SQP) method is utilized to carry out the extremum analysis of the sample focal elements. Thirdly, t...

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