A Practical Method of Nonprobabilistic Reliability and Parameter Sensitivity Analysis Based on Space-Filling Design

Using the convex model approach, the bounds of uncertain variables are only required rather than the precise probability distributions, based on which it can be made possible to conduct the reliability analysis for many complex engineering problems with limited information. In this paper, three types of convex model including interval, ellipsoid, and multiellipsoid convex uncertainty model are investigated, and a uniform model of nonprobabilistic reliability analysis is built. In the reliability analysis process, an effective space-filling design is introduced to generate representative samples of uncertainty space so as to reduce the computational cost and provide an accurate depiction of possible model outcome. Finally, Spearman's rank correlation coefficient is used to perform parameters global sensitivity analysis. Three numerical examples are investigated to demonstrate the feasibility and accuracy of the presented method.