Strength of Knowledge Assessment for Risk Informed Decision Making

Tasneem Bani-Mustafa (1) , Zhiguo Zeng (2, 1) , Enrico Zio (2, 1) , Dominique Vasseur (3)

1 LGI - Laboratoire Génie Industriel - EA 2606
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
3 EDF R&D MRI - Management des Risques Industriels

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, “leaf” 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.

Mots clés: Strength of knowledge; Probabilistic Risk Assessment (PRA); Risk Informed Decision Making (RIDM); Multi- Hazards Risk Aggregation (MHRA)

Domaines: Statistiques [stat]; Applications [stat.AP]

Tasneem Bani-Mustafa, Zhiguo Zeng, Enrico Zio, Dominique Vasseur. Strength of Knowledge Assessment for Risk Informed Decision Making. ESREL 2018, Jun 2018, Trondheim, Norway. ⟨hal-01989073⟩