Possibilistic Uncertainty Quantification in One-Dimensional Consolidation Problems

Abstract

In this study, we use possibility distribution as a basis for parameter uncertainty quantification in one-dimensional (1D) consolidation problems. A possibility distribution is the one-point coverage function of a random set and is viewed as containing both partial ignorance and uncertainty. Vagueness and scarcity of information needed for characterizing the coefficient of consolidation in clay can be handled using possibility distributions. Possibility distributions can be constructed from existing data, or based on the transformation of probability distributions. An attempt is made to set a systematic approach for estimating uncertainty propagation during the consolidation process. The measure of uncertainty is based on Klir's definition (1995). We make comparisons with results obtained from other approaches (probabilistic…) and discuss the importance of using possibility distributions in this type of problem.