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Efficient structural reliability analysis based on adaptive Bayesian support vector regression

Jinsheng Wang, Chenfeng Li Orcid Logo, Guoji Xu, Yongle Li, Ahsan Kareem

Computer Methods in Applied Mechanics and Engineering, Volume: 387, Start page: 114172

Swansea University Author: Chenfeng Li Orcid Logo

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DOI (Published version): 10.1016/j.cma.2021.114172

Abstract

To reduce the computational burden for structural reliability analysis involving complex numerical models, many adaptive algorithms based on surrogate models have been developed. Among the various surrogate models, the support vector machine for regression (SVR) which is derived from statistical lea...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58159
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spelling 2022-10-31T19:06:40.0571371 v2 58159 2021-09-30 Efficient structural reliability analysis based on adaptive Bayesian support vector regression 82fe170d5ae2c840e538a36209e5a3ac 0000-0003-0441-211X Chenfeng Li Chenfeng Li true false 2021-09-30 CIVL To reduce the computational burden for structural reliability analysis involving complex numerical models, many adaptive algorithms based on surrogate models have been developed. Among the various surrogate models, the support vector machine for regression (SVR) which is derived from statistical learning theory has demonstrated superior performance to handle nonlinear problems and to avoid overfitting with excellent generalization. Therefore, to take the advantage of the desirable features of SVR, an Adaptive algorithm based on the Bayesian SVR model (ABSVR) is proposed in this study. In ABSVR, a new learning function is devised for the effective selection of informative sample points following the concept of the penalty function method in optimization. To improve the uniformity of sample points in the design of experiments (DoE), a distance constraint term is added to the learning function. Besides, an adaptive sampling region scheme is employed to filter out samples with weak probability density to further enhance the efficiency of the proposed algorithm. Moreover, a hybrid stopping criterion based on the error-based stopping criterion using the bootstrap confidence estimation is developed to terminate the active learning process to ensure that the learning algorithm stops at an appropriate stage. The proposed ABSVR is easy to implement since no embedded optimization algorithm nor iso-probabilistic transformation is required. The performance of ABSVR is evaluated using six numerical examples featuring different complexity, and the results demonstrate the superior performance of ABSVR for structural reliability analysis in terms of accuracy and efficiency. Journal Article Computer Methods in Applied Mechanics and Engineering 387 114172 Elsevier BV 0045-7825 Structural reliability analysis, Adaptive surrogate models, Support vector regression, Bayesian inference, Learning function 15 12 2021 2021-12-15 10.1016/j.cma.2021.114172 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2022-10-31T19:06:40.0571371 2021-09-30T14:59:29.6981444 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Jinsheng Wang 1 Chenfeng Li 0000-0003-0441-211X 2 Guoji Xu 3 Yongle Li 4 Ahsan Kareem 5 58159__21064__c41aaeb85b5743bcb40dba4266cb1c9a.pdf 58159.pdf 2021-10-01T11:15:07.5291987 Output 39986038 application/pdf Accepted Manuscript true 2022-09-24T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Efficient structural reliability analysis based on adaptive Bayesian support vector regression
spellingShingle Efficient structural reliability analysis based on adaptive Bayesian support vector regression
Chenfeng Li
title_short Efficient structural reliability analysis based on adaptive Bayesian support vector regression
title_full Efficient structural reliability analysis based on adaptive Bayesian support vector regression
title_fullStr Efficient structural reliability analysis based on adaptive Bayesian support vector regression
title_full_unstemmed Efficient structural reliability analysis based on adaptive Bayesian support vector regression
title_sort Efficient structural reliability analysis based on adaptive Bayesian support vector regression
author_id_str_mv 82fe170d5ae2c840e538a36209e5a3ac
author_id_fullname_str_mv 82fe170d5ae2c840e538a36209e5a3ac_***_Chenfeng Li
author Chenfeng Li
author2 Jinsheng Wang
Chenfeng Li
Guoji Xu
Yongle Li
Ahsan Kareem
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 387
container_start_page 114172
publishDate 2021
institution Swansea University
issn 0045-7825
doi_str_mv 10.1016/j.cma.2021.114172
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description To reduce the computational burden for structural reliability analysis involving complex numerical models, many adaptive algorithms based on surrogate models have been developed. Among the various surrogate models, the support vector machine for regression (SVR) which is derived from statistical learning theory has demonstrated superior performance to handle nonlinear problems and to avoid overfitting with excellent generalization. Therefore, to take the advantage of the desirable features of SVR, an Adaptive algorithm based on the Bayesian SVR model (ABSVR) is proposed in this study. In ABSVR, a new learning function is devised for the effective selection of informative sample points following the concept of the penalty function method in optimization. To improve the uniformity of sample points in the design of experiments (DoE), a distance constraint term is added to the learning function. Besides, an adaptive sampling region scheme is employed to filter out samples with weak probability density to further enhance the efficiency of the proposed algorithm. Moreover, a hybrid stopping criterion based on the error-based stopping criterion using the bootstrap confidence estimation is developed to terminate the active learning process to ensure that the learning algorithm stops at an appropriate stage. The proposed ABSVR is easy to implement since no embedded optimization algorithm nor iso-probabilistic transformation is required. The performance of ABSVR is evaluated using six numerical examples featuring different complexity, and the results demonstrate the superior performance of ABSVR for structural reliability analysis in terms of accuracy and efficiency.
published_date 2021-12-15T04:14:28Z
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score 11.013731

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