ACTA AERONAUTICAET ASTRONAUTICA SINICA >
An active learning reliability method based on Kriging and Monte Carlo
Received date: 2014-10-21
Revised date: 2014-12-28
Online published: 2015-01-07
Supported by
National Science and Technology Major Project (2013ZX04011-011)
In structural reliability analysis, surrogate models are usually used to approximate implicit performance function in order to solve the problem of large computation. However, selection of the design of numerical experiments should consider the accuracy of fitting surrogate model and the precision of calculating reliability simultaneously. Therefore, in order to make full use of the few sample information as little as possible and to maximize the accuracy of reliability calculation, an active learning reliability calculation method is proposed. Firstly, a learning function based on Kriging prediction is proposed similar to the improved function determining the selection of point in the optimization problem. And the best sampling point corresponding to the minimum of learning function is selected from the Monte Carlo population. Secondly, an iterative stopping criterion is proposed to ensure the correctness of Monte Carlo sample points' sign, and the iterations decrease dramatically. Finally, the correctness and efficiency of the proposed method are proved by two academic examples from literature; it is shown that the proposed method requires fewer calls to the performance function than other methods and the failure probability obtained from the proposed method is more accurate.
Key words： reliability; Monte Carlo; Kriging model; active learning; failure probability
TONG Cao , SUN Zhili , YANG Li , SUN Anbang . An active learning reliability method based on Kriging and Monte Carlo[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015 , 36(9) : 2992 -3001 . DOI: 10.7527/S1000-6893.2014.0361
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