Mixed Efficient Global Optimization for Time Dependent Reliability Analysis

Abstract

Timedependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (mEGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the mEGO method draws samples for the two types of samples simultaneously. The mEGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the timedependent reliability based on the surrogate model. Good accuracy and efficiency of the mEGO method are demonstrated by three examples.