A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment

Nicola Pedroni (), Enrico Zio (), Alberto Pasanisi () and Mathieu Couplet ()
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Nicola Pedroni: LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec
Enrico Zio: LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec, SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec - Ecole Centrale Paris - Ecole Supérieure d'Electricité - SUPELEC (FRANCE) - CentraleSupélec - EDF R&D - EDF R&D - EDF - EDF
Alberto Pasanisi: EIFER - European Institute For Energy Research - TH - Universität Karlsruhe - EDF R&D - EDF R&D - EDF - EDF
Mathieu Couplet: EDF R&D STEP - Simulation et Traitement de l'information pour l'Exploitation des systèmes de Production - EDF R&D - EDF R&D - EDF - EDF

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Abstract: Models for the assessment of the risk of complex engineering systems are affected by uncertainties, due to the randomness of several phenomena involved and the incomplete knowledge about some of the characteristics of the system. The objective of the present paper is to provide operative guidelines to handle some conceptual and technical issues related to the treatment of uncertainty in risk assessment for engineering practice. In particular, the following issues are addressed: (1) quantitative modeling and representation of uncertainty coherently with the information available on the system of interest; (2) propagation of the uncertainty from the input(s) to the output(s) of the system model; (3) (Bayesian) updating as new information on the system becomes available; (4) modeling and representation of dependences among the input variables and parameters of the system model. Different approaches and methods are recommended for efficiently tackling each of the issues (1)‒(4) above; the tools considered are derived from both classical probability theory as well as alternative, non-fully probabilistic uncertainty representation frameworks (e.g., possibility theory). The recommendations drawn are supported by the results obtained in illustrative applications of literature.

Keywords: uncertainty representation and propagation; Bayesian update; dependences (search for similar items in EconPapers)
Date: 2017-07
Note: View the original document on HAL open archive server: https://hal.science/hal-01652230
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Citations: View citations in EconPapers (3)

Published in Risk Analysis, 2017, 37 (7), pp.1315 - 1340. ⟨10.1111/risa.12705⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01652230

DOI: 10.1111/risa.12705

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