ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION
When estimating very small failure probability, the methods based on active learning Kriging (ALK) model usually need too many candidate points and time-consuming calculation. To address this problem, this paper proposes a two-stage surrogate model method, ALK-SS<sup>2</sup>, which combi...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-02-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.01.013 |
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| author | LIU ZeQing CHENG Xin YANG XuFeng |
| author_facet | LIU ZeQing CHENG Xin YANG XuFeng |
| author_sort | LIU ZeQing |
| collection | DOAJ |
| description | When estimating very small failure probability, the methods based on active learning Kriging (ALK) model usually need too many candidate points and time-consuming calculation. To address this problem, this paper proposes a two-stage surrogate model method, ALK-SS<sup>2</sup>, which combines the ALK model and subset simulation(SS). Firstly, based on the constructed surrogate model in the first stage, the method uses a small number of SS last layer samples as candidate samples to complete the rough approximation of the limit state surface, and then in the second stage, it selects the SS last layer samples witha larger sample size to refine the surrogate model in the first stage, so as to obtain higher accuracy. In addition, considering the conventional stopping criteria are too conservative. based on the failure probabilitv evaluated by ALK-SS<sup>2</sup>, a new stopping criterion based on failure probability error is proposed, which further improves the efficiency of the method. From the investigation of four examples and the comparison with relative methods, it is proved that the proposed method has high calculation accuracy and efficiency, and is suitable for dealing with small failure probability problems and time-consuming implicit function problems. |
| format | Article |
| id | doaj-art-5f19014446c54215ba76fea80c6b96b7 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-5f19014446c54215ba76fea80c6b96b72025-01-15T02:44:44ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-02-01469610655273240ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATIONLIU ZeQingCHENG XinYANG XuFengWhen estimating very small failure probability, the methods based on active learning Kriging (ALK) model usually need too many candidate points and time-consuming calculation. To address this problem, this paper proposes a two-stage surrogate model method, ALK-SS<sup>2</sup>, which combines the ALK model and subset simulation(SS). Firstly, based on the constructed surrogate model in the first stage, the method uses a small number of SS last layer samples as candidate samples to complete the rough approximation of the limit state surface, and then in the second stage, it selects the SS last layer samples witha larger sample size to refine the surrogate model in the first stage, so as to obtain higher accuracy. In addition, considering the conventional stopping criteria are too conservative. based on the failure probabilitv evaluated by ALK-SS<sup>2</sup>, a new stopping criterion based on failure probability error is proposed, which further improves the efficiency of the method. From the investigation of four examples and the comparison with relative methods, it is proved that the proposed method has high calculation accuracy and efficiency, and is suitable for dealing with small failure probability problems and time-consuming implicit function problems.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.01.013Reliability analysisActive learningKriging modelSubset simulation |
| spellingShingle | LIU ZeQing CHENG Xin YANG XuFeng ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION Jixie qiangdu Reliability analysis Active learning Kriging model Subset simulation |
| title | ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION |
| title_full | ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION |
| title_fullStr | ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION |
| title_full_unstemmed | ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION |
| title_short | ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION |
| title_sort | active learning method based on alk model and subset simulation |
| topic | Reliability analysis Active learning Kriging model Subset simulation |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.01.013 |
| work_keys_str_mv | AT liuzeqing activelearningmethodbasedonalkmodelandsubsetsimulation AT chengxin activelearningmethodbasedonalkmodelandsubsetsimulation AT yangxufeng activelearningmethodbasedonalkmodelandsubsetsimulation |