基于多目标粒子群优化算法的自适应Kriging高维可靠性评估方法

IF 3.5 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics Pub Date : 2025-09-02 DOI:10.1016/j.probengmech.2025.103827

Qingwei Liang, Cheng Yang, Yuxin Lin, Hancheng Huang, Shanshan Hu

{"title":"基于多目标粒子群优化算法的自适应Kriging高维可靠性评估方法","authors":"Qingwei Liang, Cheng Yang, Yuxin Lin, Hancheng Huang, Shanshan Hu","doi":"10.1016/j.probengmech.2025.103827","DOIUrl":null,"url":null,"abstract":"<div><div>Structural reliability analysis is critical to the design and safety evaluation of engineering structures. However, conventional reliability methods often struggle with high-dimensional problems. This study proposes an adaptive Kriging method for high-dimensional reliability assessment based on multi-objective particle swarm optimization (MOPSO). The method uses the maximum information coefficient (MIC) to build a high-dimensional Kriging surrogate. Training samples for updating the surrogate are selected using MOPSO. Furthermore, a hybrid convergence criterion that incorporates an error-based stopping criterion (ESC) is introduced to ensure efficient termination. Four benchmark examples demonstrate the effectiveness and practicality of the method. The results show clear gains in surrogate modeling efficiency and accuracy for high-dimensional reliability problems.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"82 ","pages":"Article 103827"},"PeriodicalIF":3.5000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Kriging high-dimensional reliability assessment method based on multi-objective particle swarm optimization algorithm\",\"authors\":\"Qingwei Liang, Cheng Yang, Yuxin Lin, Hancheng Huang, Shanshan Hu\",\"doi\":\"10.1016/j.probengmech.2025.103827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Structural reliability analysis is critical to the design and safety evaluation of engineering structures. However, conventional reliability methods often struggle with high-dimensional problems. This study proposes an adaptive Kriging method for high-dimensional reliability assessment based on multi-objective particle swarm optimization (MOPSO). The method uses the maximum information coefficient (MIC) to build a high-dimensional Kriging surrogate. Training samples for updating the surrogate are selected using MOPSO. Furthermore, a hybrid convergence criterion that incorporates an error-based stopping criterion (ESC) is introduced to ensure efficient termination. Four benchmark examples demonstrate the effectiveness and practicality of the method. The results show clear gains in surrogate modeling efficiency and accuracy for high-dimensional reliability problems.</div></div>\",\"PeriodicalId\":54583,\"journal\":{\"name\":\"Probabilistic Engineering Mechanics\",\"volume\":\"82 \",\"pages\":\"Article 103827\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Probabilistic Engineering Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0266892025000992\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Probabilistic Engineering Mechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0266892025000992","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}

引用次数: 0

摘要

结构可靠度分析是工程结构设计和安全评价的重要内容。然而，传统的可靠性方法往往难以解决高维问题。提出了一种基于多目标粒子群优化（MOPSO）的高维可靠性评估自适应Kriging方法。该方法利用最大信息系数（MIC）建立高维克里格代理。使用MOPSO选择更新代理的训练样本。在此基础上，引入了基于误差的停止准则（ESC）的混合收敛准则以保证有效终止。四个基准算例验证了该方法的有效性和实用性。结果表明，在高维可靠性问题的代理建模效率和准确性方面有明显的提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Adaptive Kriging high-dimensional reliability assessment method based on multi-objective particle swarm optimization algorithm

Structural reliability analysis is critical to the design and safety evaluation of engineering structures. However, conventional reliability methods often struggle with high-dimensional problems. This study proposes an adaptive Kriging method for high-dimensional reliability assessment based on multi-objective particle swarm optimization (MOPSO). The method uses the maximum information coefficient (MIC) to build a high-dimensional Kriging surrogate. Training samples for updating the surrogate are selected using MOPSO. Furthermore, a hybrid convergence criterion that incorporates an error-based stopping criterion (ESC) is introduced to ensure efficient termination. Four benchmark examples demonstrate the effectiveness and practicality of the method. The results show clear gains in surrogate modeling efficiency and accuracy for high-dimensional reliability problems.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Probabilistic Engineering Mechanics 工程技术-工程：机械

CiteScore

3.80

自引率

15.40%

发文量

审稿时长

13.5 months

期刊介绍： This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.