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Efficient structural reliability analysis based on adaptive Bayesian support vector regression
Computer Methods in Applied Mechanics and Engineering, Volume: 387, Start page: 114172
Swansea University Author:
Chenfeng Li
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©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)
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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...
Published in: | Computer Methods in Applied Mechanics and Engineering |
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ISSN: | 0045-7825 |
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Elsevier BV
2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa58159 |
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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 |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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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 |
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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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1763753976713445376 |
score |
11.021648 |