A new method for global sensitivity analysis of fuzzy distribution parameters

To measure the contribution of the fuzzy distribution parameters of random model inputs to the statistical characters of model output reasonably, the global sensitivity effect index of the fuzzy distribution parameters is proposed, and the efficient solution is studied. Firstly, according to the propagation of uncertainties from the fuzzy distribution parameters to the statistical characters of output, the mean of model output for instance is taken, and the contribution of the fuzzy distribution parameters is measured by using the average difference between the unconditional membership function of the mean of output and the membership function under the condition that one distribution parameter is fixed, and then the definition of the global sensitivity effect index is given. Secondly, to reduce the computational cost of the proposed index and improving the computation efficiency, the extended Monte Carlo simulation (EMCS) is applied for estimating the function relationship between the distribution parameters of model inputs and the statistical characters of model output. Finally, two analytical examples and the cylindrical pressure vessel engineering example are used to verify the accuracy and efficiency of the proposed method.