Modified Decomposition Method for Complex Bridge Network

Belief reliability is a new reliability metric that accounts for aleatory uncertainty and epistemic uncertainty. Belief reliability is based on the uncertainty theory it considers epistemic and aleatory uncertainty. Finding the distribution of belief dependability depending on the real conditions is crucial to understanding belief reliability, but doing so can be challenging when there is a lack of knowledge availability of components. The issue of belief reliability of systems has been addressed recently. These methods of Belief reliability are inefficient in the process of deriving system reliability expression as these methods generate both cancelling and non-cancelling terms. Also the number of terms in the expression of Belief Reliability are large enough. To resolve these issues an efficient method is proposed that considers a priori knowledge of minimal paths and cuts of the system. The method generates only success terms (non-cancelling terms) and number of terms in Belief Reliability expression are very less as compared to existing method. The findings are quantified and the results are validated with the help of Benchmark problem i.e. Bridge Network.