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IRSN, Institut de radioprotection et de sûreté nucléaire

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Enhancing Nuclear Safety


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Development of a diagnostic support tools in uncertain context

Ahmed Mabrouk has defended his thesis on 13th September 2016 at UPMC in Paris.​

Document type > *Mémoire/HDR/Thesis

Keywords >

Research Unit > IRSN/PSN-RES/SAG/LETR

Authors > MABROUK Ahmed

Publication Date > 13/09/2016

Summary

The diagnosis of severe nuclear accident scenarios represents a major challenge for nuclear safety and crisis management. The problem is complex and remains until now one of the main research topics due to the complexity of the physical and chemical phenomena underlying severe accidents, the difficulty in understanding the different correlations between them, and in addition the unavailability of efficient public datasets. Thus, the purpose of this thesis is to propose a dedicated tool for modeling and diagnosis of accident scenarios based on Bayesian networks. The learning process of the Bayesian networks is based on the use of databases created with the ASTEC severe accident software. It should be emphasized that the use of Bayesian networks in this context has faced many challenges, notably the learning process from the accidental data which, after numerous studies, has been doomed to be ineffective to address efficiently this task. These difficulties arise mainly because the used data contains on the one hand, many continuous variables and on the other hand a set of both deterministic and probabilistic relationships between variables. These two constraints present a serious problem for the learning algorithms of Bayesian networks because these latter assume that all relationships between variables are probabilistic and all the used variables in the datasets are factorial (or discrete). Concerning the first point, we proposed of a new structure learning algorithm based on the use of a set of new rules (whose effectiveness has been proven theoretically and experimentally). Regarding discretization step, we proposed a multivariate approach which, according to a detailed experimental study, has enabled us to overcome the drawbacks of these latter while minimizing the information loss during the data transformation.

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