Uncertainty Assessment of Large Finite Element Systems
Uncertainty Assessment of Large Finite Element Systems
Engineering computation under uncertainty - Capabilities of non-traditional models
Computers and Structures
Classical and imprecise probability methods for sensitivity analysis in engineering: A case study
International Journal of Approximate Reasoning
On solutions of stochastic differential equations with parameters modeled by random sets
International Journal of Approximate Reasoning
Probability boxes on totally preordered spaces for multivariate modelling
International Journal of Approximate Reasoning
On the connection between probability boxes and possibility measures
Information Sciences: an International Journal
Evidence-theory-based structural static and dynamic response analysis under epistemic uncertainties
Finite Elements in Analysis and Design
Stochastic dominance with imprecise information
Computational Statistics & Data Analysis
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Random sets form a well-established, general tool for modelling epistemic uncertainty in engineering. They can be seen as encompassing probability theory, fuzzy sets and interval analysis. Random set models for data uncertainty are typically used to obtain robust upper and lower bounds for the reliability of structures in engineering models. The goal of this paper is to show how random set models can be constructed from measurement data by non-parametric methods using inequalities of Tchebycheff type. Relations with sensitivity analysis will also be high-lighted. We demonstrate the application of the methods in an FE-model for the excavation of a cantilever sheet pile wall.