A Comparative Study of Different Multi-Fidelity Kriging Models for Reliability Analysis

The integration of active learning methods and multi-fidelity (MF) Kriging models has been demonstrated to effectively reduce the number of high-fidelity (HF) samples required for reliability analysis. However, there is a lack of comparative research on different MF Kriging models in this context. In this work, the performances of the hierarchical Kriging model, dis-Kriging model, and Co-Kriging model are compared within one-stage and two-stage active learning frameworks through five numerical examples. The total cost, the number of HF samples required, and the stability of estimated failure probability are utilized to facilitate a comparative analysis of different MF Kriging models. The results from the five examples indicate the following: the hierarchical Kriging model demonstrates a satisfactory performance in both one-stage and two-stage frameworks; the dis-Kriging model is not adequately equipped to handle complex, highly nonlinear problems; and the Co-Kriging model exhibits a lack of robustness, though it performs well in certain cases.