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Abstract

The structural reliability analysis in presence of mixed uncertain variables demands more computation as the entire configuration of fuzzy variables needs to be explored. Moreover, the existence of multiple design points plays an important role in the accuracy of results as the optimization algorithms may converge to a local design point by neglecting the main contribution from the global design point. Therefore, in this chapter, a novel uncertain analysis method for estimating the failure probability bounds of structural systems involving multiple design points in presence of mixed uncertain variables is presented. The proposed method involves weight function to identify multiple design points, multicut-high dimensional model representation technique for the limit state function approximation, transformation technique to obtain the contribution of the fuzzy variables to the convolution integral, and fast Fourier transform for solving the convolution integral. The proposed technique estimates the failure probability accurately with significantly less computational effort compared to the direct Monte Carlo simulation. The methodology developed is applicable for structural reliability analysis involving any number of fuzzy and random variables with any kind of distribution. The numerical examples presented demonstrate the accuracy and efficiency of the proposed method.

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Correspondence to A. S. Balu .

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Balu, A.S., Rao, B.N. (2013). Failure Probability Bounds Using Multicut-High-Dimensional Model Representation. In: Chakraborty, S., Bhattacharya, G. (eds) Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM - 2012). Springer, India. https://doi.org/10.1007/978-81-322-0757-3_18

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  • DOI: https://doi.org/10.1007/978-81-322-0757-3_18

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-0756-6

  • Online ISBN: 978-81-322-0757-3

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