Comparison between Probabilistic and Possibilistic Approaches for Structural Uncertainty Analysis

Abstract

Analysis of a structure is a crucial procedure to ensure its reliable design and performance. These analytical procedures are generally performed deterministically. However, the input parameters defining the material and geometric properties possess uncertainties. These uncertainties can arise from various sources including modeling, manufacturing, and construction. The quantification of uncertainties can be based on either probability theories (using random variables) or possibility theories (using interval and fuzzy variables). In this work, several finite-element-based probabilistic and possibilistic methods are discussed and compared. Case studies of structures analyzed using static and dynamic uncertainty using the aforementioned approaches are presented. Moreover, the analysis methods are compared for both sharpness and computational efficiency. The results of those analyses suggest that the incorporation of uncertainty in the analysis procedure provides a higher level of confidence in the analysis results. It is also observed that the choice of the analytical procedure must be based on both the problem complexity as well as the level of available information.