Classical and imprecise probability methods for sensitivity analysis in engineering: A case study

AbstractThis article addresses questions of sensitivity of output values in engineering models with respect to variations in the input parameters. Such an analysis is an important ingredient in the assessment of the safety and reliability of structures. A major challenge in engineering applications lies in the fact that high computational costs have to be faced. Methods have to be developed that admit assertions about the sensitivity of the output with as few computations as possible. This article serves to explore various techniques from precise and imprecise probability theory that may contribute to achieving this goal. It is a case study using an aerospace engineering example and compares sensitivity analysis methods based on random sets, fuzzy sets, interval spreads simulated with the aid of the Cauchy distribution, and sensitivity indices calculated by direct Monte Carlo simulation. Computational cost, accuracy, interpretability, ability to incorporate correlated input and applicability to large scale problems will be discussed