一种基于AKＧMCSＧK的失效概率函数估计方法

An efficient method for solving the failure probability function was proposed to address the difficulties of solving the failure probability function in reliability optimization design, such as complexity and large amount of computation. The basic idea of the proposed method was to utilize the adaptive Kriging method to construct a local surrogate model of the full space of input variables at the failure boundary. The local surrogate model was then combined with the Monte Carlo simulation method to calculate the failure probability of the structures under the specified distribution parameter samples. The functional relationship between the sample points of the distribution parameters and the structural failure probability was then fitted by the Kriging method. Finalization of the implicit function of the failure probability function expressed in terms of the Kriging model. In order to test the accuracy and efficiency of the proposed method, two examples were given to compare the computational results of the proposed method with those of the existing methods for solving failure probability functions. The results of examples show that the proposed method is suitable for solving complicated functional function problems and significantly reduces the amount of computation while satisfying the accuracy requirements.