Enhanced Bayesian networks approach to risk assessment of spent fuel ponds

Silvia Tolo, Edoardo Patelli, Michael Beer, Matteo Broggi

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Abstract

A model for the risk assessment of spent nuclear fuel ponds subject to the risk of flooding is proposed. The methodology adopted is based on the enhancement of Bayesian Networks approach with Structural Reliability Methods, in order to overcome the limitations of classic Bayesian Networks (such as the use of only discrete variables in case of exact inference calculations). The computational tool developed for the methodology mentioned is briefly described together with the application to a real-case study. The related results are discussed and compared to those previously obtained by traditional Bayesian Network analysis. Finally, a brief discussion about the advantages and drawbacks of the approach adopted is provided.

Original languageEnglish
Title of host publication12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015
Place of PublicationVancouver
PublisherUniversity of British Columbia
Number of pages8
ISBN (Electronic)9780888652454
Publication statusPublished - 31 Jul 2015
Event12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 - Vancouver, Canada
Duration: 12 Jul 201515 Jul 2015

Publication series

Name12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015

Conference

Conference12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012
CountryCanada
CityVancouver
Period12/07/1515/07/15

Keywords

  • enhanced Bayesian networks
  • risk analysis
  • spent fuel ponds

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    Tolo, S., Patelli, E., Beer, M., & Broggi, M. (2015). Enhanced Bayesian networks approach to risk assessment of spent fuel ponds. In 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015 (12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015). University of British Columbia.