Risk Informed Decision Making (RIDM) is based on risk metrics obtained from a Probabilistic Risk Assessment (PRA). For plants exposed to multiple hazards, Multi-Hazards Risk Aggregation (MHRA) is necessary to inform decisions. In practice, this is often done by a simple arithmetic summation over the different risk contributors, without taking into account that the state of knowledge of the risk models of the different hazards can be quite different. In this paper, we provide a hierarchical framework to assess the strength of knowledge that PRA models are based upon. The framework is organized in three attributes characterizing the knowledge which a PRA model is based upon (assumptions, data, phenomenological understanding). These attributes are further broken down into sub-attributes and, finally, attributes that can be evaluated. The PRA models of two hazards groups for Nuclear Power Plants (NPPs) are considered and the strength of knowledge behind each model is assessed using the developed framework.
In this paper, we develop a new method for Multi-Hazards Risk Aggregation (MHRA). A hierarchical framework is first developed for evaluating the trustworthiness of the risk assessment. The evaluation is based on two main attributes (criteria), i.e., the strength of knowledge supporting the assessment and the fidelity of the risk assessment model. These two attributes are further broken down into sub-attributes and, finally, leaf attributes. The trustworthiness is calculated using a weighted average of the leaf attributes, in which the weights are calculated using the Dempster Shafer Theory-Analytical Hierarchy Process (DST-AHP). Risk aggregation is, then, performed by a "weighted posterior" method, considering the level of trustworthiness. An application to the risk aggregation of two hazard groups in Nuclear Power Plants (NPP) is illustrated.
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