|
| A combined active learning Kriging model and sequential importance sampling for hybrid reliability analysis with random and interval variables |
| Received:June 02, 2021 Revised:June 16, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20210602415 |
| KeyWord:random variables interval variables reliability analysis sequential importance sampling Kriging model |
| Author | Institution |
| 李刚 |
大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连 |
| 姜龙 |
大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连 |
| 赵刚 |
大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连 |
|
| Hits: 631 |
| Download times: 291 |
| Abstract: |
| Aiming at the complicated performance functions with high nonlinearity or multiple design points in random-interval hybrid reliability analysis, a hybrid reliability analysis method combining active learning Kriging model and sequence importance sampling method is proposed in this paper. The sequential importance sampling method is employed to generate the approximate samples of the optimal importance sampling function gradually by using Gaussian mixture distribution as the proposed distribution. Then, combined with the sequential importance sampling method, a two-step active learning scheme of Kriging model is presented, which can improve the efficiency of the proposed method significantly while ensuring the accuracy. Finally, the proposed method is compared with some existing hybrid reliability analysis methods through several numerical examples, to verify the accuracy and efficiency of the proposed method. |