Journal of the Society of Materials Science, Japan
Online ISSN : 1880-7488
Print ISSN : 0514-5163
ISSN-L : 0514-5163
Original Papers
Application of Metaheulistic Method to Reliability Analysis for Lifeline Network Involves Multiple Failure Modes
Ken ISHIBASHIHitoshi FURUTAYasutoshi NOMURAKoichiro NAKATSUKyosuke TAKAHASHI
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2014 Volume 63 Issue 2 Pages 143-148

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Abstract
The reliability analysis is necessary to maintain the safety of structure. In order to evaluate the soundness of structure quantitatively, the calculation of failure probability is one of useful measures. In the reliability analysis for large-scale structures, the enhancement of calculation accuracy and the estimation of factors for the failure require the failure probability calculated by considering the various failure modes. However, the sampling method like Monte Carlo simulation is difficult to calculate the failure probability and estimate multiple failure modes efficiently. This is because the search of various failure modes increases the calculation cost significantly. Therefore, in order to overcome the trade-off, this paper attempts to propose an efficient method by applying Metaheuristic methods. Metaheuristic methods are characterized by the high search ability with keeping the diversity among solution candidates. Firstly, the search of failure modes is formulated as the combinatorial optimization. In this optimization, the metaheuristic method searches failure modes in consideration of the occurrence probability of minimal cut set. In this way, various failure modes that have high occurrence probability are obtained efficiently. Secondly, Probabilistic Network Evaluation Techniques (PNET) is applied to calculate the failure probability of network in consideration of the correlation among obtained failure modes. Through these processes, the proposed method can calculate the accurate failure probability efficiently. Several numerical experiments are presented to demonstrate the applicability of the proposed method for the reliability analysis of large-scale networks.
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© 2014 by The Society of Materials Science, Japan
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