Reliability Analysis using Evolutionary Algorithm and Importance Sampling

Abstract eng:
The purpose of this research is the development of the method for reliability analysis to largescale structures. In large-scale structures, the reliability analysis is required to consider various failure samples in order to improve the calculation accuracy of failure probability. However, it is difficult due to the trade-off between the calculation accuracy and the calculation time. In this study, an attempt is made to develop a new simulation method for calculating low failure probabilities by using both evolutionary algorithm and importance sampling. Evolutionary algorithm can obtain various solutions in the search space, while importance sampling is a variance reduction technique, the behind idea of which is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others. At first, the proposed method adopts the evolutionary algorithm in order to obtain various failure samples included in different failure modes. Then, the failure probability is calculated by using importance sampling to failure samples obtained. Several numerical examples were presented to demonstrate that the evolutionary algorithm could obtain various failure samples in the proposed method. On the other hand, the calculation accuracy of failure probability was not improved by applying obtained failure samples to Importance Sampling. The safety of structure cannot be improved by only the calculation of failure probability which has the high accuracy. Therefore, it is necessary to develop the reliability analysis using various failure samples obtained by the proposed method.

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