The main objective of the present study is to permit the reliability analyst or system manager to analyze the failure behavior of the system in a more consistent and logical manner. As the collected or available data from various resources are uncertain and imprecise due to various practical constraints and hence the performance of the system cannot be made up to desired levels. To cope with such situations and subjective information in a consistent and logical manner, fuzzy methodology is one of the most vital and effective tool. To this effect a structural framework has been developed by the authors for analyzing and predicting the system behavior. The pulping unit of paper industry has been taken as an illustration. The failure rates and repair times for all the constituent components are obtained by solving availability-cost optimization model using particle swarm optimization and genetic algorithm. To increase the performance of the system, various reliability parameters are computed with the obtained results using a confidence interval based fuzzy lambda-tau methodology. Sensitivity as well as performance analysis of the system performance has been done for ranking the critical component of the system as per preferential order. The computed results are compared with existing fuzzy lambda-tau and traditional (crisp) methodology results.
Fire response of Hybrid Fiber Reinforced Concrete to High Temperature
