Parallel efficient global optimization method: A novel approach for time-dependent reliability analysis and applications

Time-dependent failure may result from material properties deterioration, random load, and other uncertain factors, which are widespread among practical engineering applications. However, in previous studies of timedependent reliability analysis (TRA), parallel computing, which can speed up the optimization process significantly, has not been fully considered. Therefore, this study proposed a parallel efficient global optimization (PEGO) integrated with the adaptive Kriging-Monte Carlo simulation (AK-MCS) for the TRA problems. It was shown that the proposed method was superior to the existing TRA methods in computing efficiency and maintained a high accuracy to solve the high-dimension of the TRA problems. Besides, it exhibited an extensive application scope due to the excellent performances (i.e., efficiency, accuracy), which were verified via six representative cases, including four low-dimension and two high-dimension, by comparing with the existing TRA methods.

Automated summary

This study proposed a parallel efficient global optimization method integrated with the adaptive Kriging-Monte Carlo simulation for time-dependent reliability analysis (TRA) problems, which showed superior computing efficiency and high accuracy in solving high-dimensional TRA problems.