Probability and convex set hybrid reliability analysis based on active learning Kriging model

Probability and convex set hybrid reliability analysis (HRA) is investigated in this paper. It is figured out that a surrogate model only rightly predicting the sign of the performance function can meet the demand of HRA in accuracy. According to this idea, a methodology based on active learning Kriging model called ALK-HRA is proposed. When constructing the Kriging model, the proposed method only approximates the performance function in some region of interest, i.e., the region where the sign of response tends to be wrongly predicted. Then Monte Carlo Simulation (MCS) is performed based on the Kriging model. ALK-HRA is very accurate for HRA with calling the performance function as few times as possible. Three numerical examples are investigated to demonstrate the efficiency and accuracy of the presented method, which include two simple problems and one complicated engineering application.