Improved AK-MCS method based on new learning function and convergence criterion

被引：0
|
作者：
Fan W. [1 ,2 ]
Yu S. [1 ]
Li Z. [1 ,2 ]
机构：
[1] School of Civil Engineering, Chongqing University, Chongqing
[2] Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2024年 / 52卷 / 01期
关键词：
active learning; convergence criterion; error estimation; Kriging model; structural reliability;
D O I：
10.13245/j.hust.240298
中图分类号：
学科分类号：
摘要：
On the basis of error analysis of the AK-MCS method and considering the impact of Kriging model updates on errors,a new learning function and convergence criterion were proposed．Firstly,according to statistical properties of Kriging model,the error analysis of the AK-MCS method for predicting failure probability was performed． Secondly,due to influence of adding training sample on the error contribution,a learning function considering regional influence was proposed,and then a two-step method for selecting training sample was presented,in which the differences of both deterministic error and statistical one for different training samples were involved． Thirdly,considering accuracy of reliability and stability of model convergence,a convergence criterion was proposed．Combining the new strategy for selecting training sample and the new convergence criterion, an improved AK-MCS for reliability analysis was presented．Finally,several examples were employed to verify the applicability of the proposed method for problems concerning high nonlinearity,multiple failure domains higher dimensionality and finite element engineering problems,what is more,the results showed that the method is of high accuracy,efficiency and stability. © 2024 Huazhong University of Science and Technology. All rights reserved.
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页码：99 / 105
页数：6
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