The objective of this study is to present a novel method of level-2 uncertainty analysis in risk assessment by means of uncertainty theory. In the proposed method, aleatory uncertainty is characterized by probability distributions, whose parameters are affected by epistemic uncertainty. These parameters are described as uncertain variables. For monotone risk models, such as fault trees or event trees, the uncertainty is propagated analytically based on the operational rules of uncertain variables. For non-monotone risk models, we propose a simulation-based method for uncertainty propagation. Three indexes, i.e., average risk, value-at-risk and bounded value-at-risk, are defined for risk-informed decision making in the level-2 uncertainty setting. Two numerical studies and an application on a real example from literature are worked out to illustrate the developed method. A comparison is made to some commonly used uncertainty analysis methods, e.g., the ones based on probability theory and evidence theory.
Uncertainty theory Uncertainty analysis Epistemic uncertainty
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This work was supported by the National Natural Science Foundation of China (Nos. 61573043, 71671009).
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Conflicts of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Human and animal rights
This article does not contain any studies with human participants performed by any of the authors.
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