Abstract
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.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61573043, 71671009).
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Zhang, Q., Kang, R. & Wen, M. A new method of level-2 uncertainty analysis in risk assessment based on uncertainty theory. Soft Comput 22, 5867–5877 (2018). https://doi.org/10.1007/s00500-018-3337-0
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DOI: https://doi.org/10.1007/s00500-018-3337-0