Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis


Time-dependent 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 (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO 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 m-EGO method are demonstrated by three examples.


Mechanical and Aerospace Engineering

Keywords and Phrases

Efficiency; Global optimization; Intelligent systems; Interpolation; Monte Carlo methods; Reliability; Structural analysis; Efficient global optimization; First order reliability methods; Kriging; Mixed ego; Reliability methods; Time dependent; Time dependent reliability; Time dependent reliability analysis; Reliability analysis

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Article - Journal

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© 2015 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 May 2015