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An improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis
https://orcid.org/0000-0002-6498-805X
Highlights The formula of traditional importance sampling (IS) method is extended to the form considering the Kriging prediction uncertainty of symbol function. The estimated expectation and variance of failure probability caused by the prediction uncertainty of Kriging model are obtained. A novel learning function considering the characteristic of IS density function and the penalty function is proposed. The reliability global sensitivity index is calculated through the failure probability and Bayes theorem based on Gaussian Mixture Model. The proposed method has high accuracy and efficiency compared with traditional adaptive Kriging-based IS method.
Abstract An improved adaptive Kriging model for importance sampling (IS) reliability and reliability global sensitivity analysis is proposed by introducing the IS density function into learning function. Considering the variance information of Kriging prediction, the formula of traditional IS method is extended to the form considering the uncertainty of symbol function. The estimated variance of failure probability caused by the prediction uncertainty of Kriging model is obtained, and the corresponding coefficient of variation (COV) is defined. Based on the standard deviation information of failure probability, a novel learning function considering the characteristic of IS density function is proposed, which are used to minimize the prediction uncertainty of Kriging. The corresponding stopping criterion is defined based on the COV information. In order to increase the likelihood that the selected sample points fall around the limit state boundary, the penalty function method is introduced to improve the learning function. Once the failure probability is obtained, the variable global sensitivity index is calculated through the failed sample set and Bayes theorem. The results show that: By introducing IS density function and penalty function into learning function, the sample points which contribute more to the failure probability can be obtained more effectively in IS method. The proposed method has high accuracy and efficiency compared with traditional Kriging-based IS method.
An improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis
https://orcid.org/0000-0002-6498-805X
Highlights The formula of traditional importance sampling (IS) method is extended to the form considering the Kriging prediction uncertainty of symbol function. The estimated expectation and variance of failure probability caused by the prediction uncertainty of Kriging model are obtained. A novel learning function considering the characteristic of IS density function and the penalty function is proposed. The reliability global sensitivity index is calculated through the failure probability and Bayes theorem based on Gaussian Mixture Model. The proposed method has high accuracy and efficiency compared with traditional adaptive Kriging-based IS method.
Abstract An improved adaptive Kriging model for importance sampling (IS) reliability and reliability global sensitivity analysis is proposed by introducing the IS density function into learning function. Considering the variance information of Kriging prediction, the formula of traditional IS method is extended to the form considering the uncertainty of symbol function. The estimated variance of failure probability caused by the prediction uncertainty of Kriging model is obtained, and the corresponding coefficient of variation (COV) is defined. Based on the standard deviation information of failure probability, a novel learning function considering the characteristic of IS density function is proposed, which are used to minimize the prediction uncertainty of Kriging. The corresponding stopping criterion is defined based on the COV information. In order to increase the likelihood that the selected sample points fall around the limit state boundary, the penalty function method is introduced to improve the learning function. Once the failure probability is obtained, the variable global sensitivity index is calculated through the failed sample set and Bayes theorem. The results show that: By introducing IS density function and penalty function into learning function, the sample points which contribute more to the failure probability can be obtained more effectively in IS method. The proposed method has high accuracy and efficiency compared with traditional Kriging-based IS method.
An improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis
https://orcid.org/0000-0002-6498-805X
Highlights The formula of traditional importance sampling (IS) method is extended to the form considering the Kriging prediction uncertainty of symbol function. The estimated expectation and variance of failure probability caused by the prediction uncertainty of Kriging model are obtained. A novel learning function considering the characteristic of IS density function and the penalty function is proposed. The reliability global sensitivity index is calculated through the failure probability and Bayes theorem based on Gaussian Mixture Model. The proposed method has high accuracy and efficiency compared with traditional adaptive Kriging-based IS method.
Abstract An improved adaptive Kriging model for importance sampling (IS) reliability and reliability global sensitivity analysis is proposed by introducing the IS density function into learning function. Considering the variance information of Kriging prediction, the formula of traditional IS method is extended to the form considering the uncertainty of symbol function. The estimated variance of failure probability caused by the prediction uncertainty of Kriging model is obtained, and the corresponding coefficient of variation (COV) is defined. Based on the standard deviation information of failure probability, a novel learning function considering the characteristic of IS density function is proposed, which are used to minimize the prediction uncertainty of Kriging. The corresponding stopping criterion is defined based on the COV information. In order to increase the likelihood that the selected sample points fall around the limit state boundary, the penalty function method is introduced to improve the learning function. Once the failure probability is obtained, the variable global sensitivity index is calculated through the failed sample set and Bayes theorem. The results show that: By introducing IS density function and penalty function into learning function, the sample points which contribute more to the failure probability can be obtained more effectively in IS method. The proposed method has high accuracy and efficiency compared with traditional Kriging-based IS method.
An improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis
Jia, Da-Wei (author) / Wu, Zi-Yan (author)
Structural Safety ; 107
2023-12-05
Article (Journal)
Electronic Resource
English
Meta-model-based importance sampling for reliability sensitivity analysis
| British Library Online Contents | 2014
Meta-model-based importance sampling for reliability sensitivity analysis
| Online Contents | 2014