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A novel active learning method for profust reliability analysis based on the Kriging model
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DOI：10.1007/s00366-021-01447-y.png)
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Profust reliability analysis, in which the failure state of a load-bearing structure is assumed to be fuzzy, is investigated in this paper. A novel active learning method based on the Kriging model is proposed to minimize the number of function evaluations. The new method is termed ALK-Pfst. The sign of performance function at a given random threshold determines the profust failure probability. Therefore, the expected risk function at an arbitrary threshold is derived as the learning function of ALK-Pfst. By making full use of the prediction information of Kriging model, the prediction error of profust failure probability is carefully derived into a closed-form expression. Aided by the prediction error, the accuracy of Kriging model during the learning process can be monitored in real time. As a result, the learning process can be timely terminated with little loss of accuracy. Four examples are provided to demonstrate the advantages of the proposed method.
Keyword:
Active learning
Kriging model
Fuzzy failure state
Error estimation
Profust reliability analysis
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A novel dual-stage adaptive Kriging method for profust reliability analysis一种用于profust可靠性分析的新型双阶段自适应Kriging方法
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