Reliability Assessment with Fuzzy Random Variables Using Interval Monte Carlo Simulation

This article provides a structural reliability assessment for structures with uncertain loads and material properties. This article models uncertain variables as fuzzy random variables. The Interval Monte Carlo Simulation and the interval finite element method is used to evaluate failure probability. Interval Monte Carlo is compared with existing search algorithms that are used in the reliability assessment of fuzzy random structural systems for both accuracy and efficiency. The genetic algorithm, as one of the well-developed approaches is selected for comparison. Fuzzy randomness is used as a model for handling both aleatory and epistemic uncertainties and fuzzy quantities are calculated using the a-cut approach. In the case of Interval Monte Carlo, bounds on response quantities are obtained for each a-cut using only one run of the interval finite element method. The genetic approach requires performing Monte Carlo Simulation for each of the considered different possible combinations within the search domain (a-cut) and running finite element for each of the Monte Carlo realizations. Both load and material uncertainties are considered in this article. In addition, results show how the Interval Monte Carlo approach provides a guaranteed and sharp enclosure to the system solution. The numerical results presented in this article show that the computational efficiency of the Interval Monte Carlo approach and its superiority to the alternative search approaches such as optimization and genetic algorithms.

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  • English

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  • Accession Number: 01519252
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 25 2014 2:29PM