ACTIVE-LEARNING METHOD BASED ON ALK MODEL AND SUBSET SIMULATION - LIU ZeQing; CHENG Xin; YANG XuFeng (Jixie qiangdu)
When estimating very small failure probability, the methods based on active learning Kriging (ALK) model usually need too many candidate points and time-consuming calculation. To address this problem, this paper proposes a two-stage surrogate model method, ALK-SS2, which combines the ALK model and subset simulation(SS). Firstly, based on the constructed surrogate model in the first stage, the method uses a small number of SS last layer samples as candidate samples to complete the rough approximation of the limit state surface, and then in the second stage, it selects the SS last layer samples witha larger sample size to refine the surrogate model in the first stage, so as to obtain higher accuracy. In addition, considering the conventional stopping criteria are too conservative. based on the failure probabilitv evaluated by ALK-SS2, a new stopping criterion based on failure probability error is proposed, which further improves the efficiency of the method. From the investigation of four examples and the comparison with relative methods, it is proved that the proposed method has high calculation accuracy and efficiency, and is suitable for dealing with small failure probability problems and time-consuming implicit function problems.