Journal
COMPUTERS & MATHEMATICS WITH APPLICATIONS
Volume 70, Issue 5, Pages 1182-1197Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2015.07.004
Keywords
Reliability; Kriging; Learning function; Information entropy; Line Sampling
Categories
Funding
- National Natural Science Foundation of China [NSFC 51175425]
- Aviation Foundation [2011ZA53015]
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In structural reliability, an important challenge is to reduce the number of calling the performance function, especially a finite element model in engineering problem which usually involves complex computer codes and requires time-consuming computations. To solve this problem, one of the metamodels, Kriging is then introduced as a surrogate for the original model. Kriging presents interesting characteristics such as exact interpolation and a local index of uncertainty on the prediction which can be used as an active learning method. In this paper, a new learning function based on information entropy is proposed. The new learning criterion can help select the next point effectively and add it to the design of experiments to update the metamodel. Then it is applied in a new method constructed in this paper which combines Kriging and Line Sampling to estimate the reliability of structures in a more efficient way. In the end, several examples including non-linearity, high dimensionality and engineering problems are performed to demonstrate the efficiency of the methods with the proposed learning function. (C) 2015 Elsevier Ltd. All rights reserved.
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