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| Adaptive algorithm based on Bayesian support vector regression for structural reliability analysis |
| Received:January 11, 2021 Revised:April 21, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20210111001 |
| KeyWord:structural reliability Bayesian support vector regression adaptive algorithm learning function sampling region scheme |
| Author | Institution |
| 汪金胜 |
西南交通大学 土木工程学院, 成都 |
| 李永乐 |
西南交通大学 土木工程学院, 成都 |
| 杨剑 |
中南大学 土木工程学院, 长沙 |
| 徐国际 |
西南交通大学 土木工程学院, 成都 |
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| Abstract: |
| The reliability analysis of complex structures usually involves an implicit performance function and time-demanding simulation model,hence the reduction of the number of functional calls is of critical importance to improve computational efficiency.In this regard,an adaptive algorithm based on Bayesian support vector regression (ABSVR) is proposed for efficient reliability analysis.To improve the overall performance of ABSVR,a new learning function is devised using the probabilistic information provided by the Bayesian SVR model.Besides,a distance constraint term is added into the learning function to avoid the clustering of samples,so that the selection of informative sample points can be achieved more efficiently.Moreover,an effective sampling region scheme is introduced in the learning process to filter out samples with weak probability density,through which only samples with large contributions to the failure probability are retained.Several numerical examples are employed to illustrate the accuracy and efficiency of the proposed method. |
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