A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes

Recently, the active learning Kriging (ALK) metamodel has proved to be an efficient method for structural system reliability analysis with multiple failure modes. A key step for enhancing the accuracy and efficiency of an ALK method is to select training samples around the limit state of the performance function as fast as possible. This study develops a new ALK metamodel based on the concept of significant domain where samples contribute the most to the failure of the system, which is termed as the ALK-SD, to achieve the above purpose. First, multiple Kriging models are established based on several initial training samples for each component using a modified DACE toolbox. Then, the significant domain of an engineering system is identified according to the type of the system. Thereafter, a new learning function H is defined to sequentially update the initial Kriging models by selecting the most probable points in the significant domain. Finally, four numerical examples and one engi-neering example are studied to illustrate the effectiveness of the proposed method, and the corresponding results are subsequently compared with those obtained via similar methods in literature in terms of the computational efficiency and the locations of newly added training samples. The results show that the proposed method can effectively and accurately evaluate the structural system reliability by outperforming its counterparts.