Global sensitivity analysis based on random variables with interval parameters by metamodel-based optimisation

Traditionally, only the uncertainty of input variables is considered in sensitivity analysis. In this paper, a new sensitivity analysis technique based on the variance-based sensitivity method is proposed for input variables through considering the uncertainty in input variables and uncertainty in distribution parameters of input variables simultaneously. The uncertainty of input variables is represented by probability density function and the uncertainty in distribution parameters is described by interval bounds. The objective of this work is to compare the relative importance of the input variables considering the uncertainty in distribution parameters simultaneously, and principles are proposed to rank the input variables. Since the distribution parameters are represented by interval variables, the sensitivity indices also become interval variables. To compute the interval bounds of the total effect indices effectively, metamodel-based optimisation is used to surrogate the Monte Carlo simulation based optimisation. Numerical and engineering examples show that the proposed principle can rank the input variables reasonably and the Kriging method can effectively calculate the interval bounds of total effect indices.