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 time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.
Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 21, 2014; final manuscript received December 14, 2014; published online February 16, 2015. Assoc. Editor: Gary Wang.
- Views Icon Views
- Share Icon Share
- Cite Icon Cite
- Search Site
Hu, Z., and Du, X. (May 1, 2015). "Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis." ASME. J. Mech. Des. May 2015; 137(5): 051401. https://doi.org/10.1115/1.4029520
Download citation file:
- Ris (Zotero)
- Reference Manager