An efficient Kriging method for global sensitivity of structural reliability analysis with non-probabilistic convex model

The convex model which only needs to know the variation bound of the uncertainty domain is competent to deal with the reliability analysis for the engineering problems lacking sufficient information. However, compared with the researches in solving non-probabilistic reliability index with convex model, the non-probabilistic reliability sensitivity analysis is less available. In this article, the moment-independent global sensitivity analysis of the basic variable based on convex model is performed for investigating the effect of non-probabilistic variable of the structure or system on the dangerous degree in reliability engineering. The proposed sensitivity index inherits the advantages of the traditional moment-independent global sensitivity index. For the problems of which the computational cost of the Monte Carlo simulation is too high, an active learning Kriging solution is established to solve the global sensitivity index. Several examples are adopted to illustrate the correctness of this global reliability sensitivity describing the effect on the reliability of the structure system of the convex model variable and the applicability and feasibility of the active learning Kriging–based solution.