Abstract In practical engineering, structural reliability analysis is usually characterized with implicit performance functions and time-consuming structural responses. It is not efficient to utilize Monte Carlo simulation that requires a large number of samples. Aiming at this issue, the original structural performance function is approximated using the Kriging model. However, to obtain an accurate Kriging model with a large number of samples, the computation cost is increased. Therefore, the active learning method is utilized to sequentially select a best next point to update the original Kriging model progressively. An accurate reliability result can be obtained with few sample points in comparison with the direct Monte Carlo simulation, indicating a potential of the proposed method for reliability analysis with implicit performance function. Four different learning functions are introduced in this paper, and the corresponding distribution of the best next points, the approximation of performance functions and the accuracy of reliability results are compared through three numerical examples. And finally, reliability analysis for a cantilever plate with an implicit performance function is carried out. The results demonstrate that the computation efficiency is improved by utilizing the Kriging model combined with active learning methods. The accuracy of reliability result is found to be affected by the choice of active learning functions.
Received: 07 September 2015
Published: 14 April 2016
2016, Vol. 37 
