Assessment and design of an engineering structure with polymorphic uncertainty quantification

Engineers are faced with the challenge of supporting decision making under uncertainty. Engineering decisions often depend on model-based predictions of the performance of the engineering system of interest. Input uncertainties of models can be categorized into two distinct types: aleatory (random/irreducible) or epistemic (reducible). Polymorphic uncertainty quantification (UQ) can be used to treat aleatory and epistemic uncertainties in a unified framework. The polymorphic UQ framework employs probability theory to model aleatory variables and alternative approaches (interval, fuzzy, Bayesian probabilistic, and combinations thereof) to model epistemic variables. This paper compares different polymorphic UQ approaches with respect to their ability to support a simple engineering decision. The comparison is based on a test-bed example, whereby aleatory variables are defined in terms of probability distributions and epistemic variables are described based on limited information (sparse data or intervals). Two challenges related to common engineering decisions (safety assessment and reliability-based design) serve as a basis for the comparison. Five independent research groups applied different models to describe the epistemic parameters based on a subjective interpretation of the given information. The comparison of the results reveals a strong influence of both the subjective choices on the models of the epistemic variables and the chosen basis for assessing the performance of the structure on the obtained decision outcomes.