Possibilistic uncertainty quantification in 1-D consolidation problems

Djamalddine Boumezerane*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

In this study, we use possibility distribution as a basis for parameter uncertainty quantification in one-dimensional consolidation problems. A Possibility distribution is the one-point coverage function of a random set and 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 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 problems.
Original languageEnglish
Article number020905
Number of pages11
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:
Volume7
Issue number2
Early online date15 Feb 2021
DOIs
Publication statusPublished - 30 Jun 2021

Keywords

  • probability-possibility transformation
  • parameter uncertainty
  • coefficient of consolidation
  • intervals
  • Hartley-like measure

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