Novel methods of scenario analysis for the probabilistic risk assessment of nuclear waste storage and disposal facilities

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School of Science | Doctoral thesis (article-based) | Defence date: 2021-09-14
Degree programme
56 + app. 152
Aalto University publication series DOCTORAL DISSERTATIONS, 104/2021
The safety of nuclear waste management facilities is typically assessed by considering accident scenarios in which the containment function may be compromised. In some scenario analysis approaches, relatively few scenarios are built based on the available knowledge. Then, the containment performance of the facility and the resulting radiological impact is analyzed separately for each scenario. Alternatively, other approaches consider scenarios within a structured probabilistic safety assessment.   Probabilistic safety assessment systematically accounts for the aleatory uncertainty about the evolution of the nuclear waste management facility under a set of accident scenarios. Thus, the probabilities and impacts of these scenarios are aggregated into an estimate of the overall risk. The scarcity and imprecision of the data utilized in the safety assessment also involves epistemic uncertainty, as it is hard to assign precise values to event probabilities and other model parameters.   This dissertation addresses the modeling of uncertainties in estimating the risk of nuclear waste management facilities, and what this implies for the comprehensiveness of scenario analysis as a support to risk-informed decision making. Specifically, it is suggested that comprehensiveness is achieved when the uncertainty about risk is sufficiently small to assess conclusively whether the facility is safe or not.   The main challenges in the attainment of comprehensiveness are also identified, and novel methodologies for probabilistic scenario analysis are presented. In particular, Bayesian networks and probabilistic cross-impact analysis are developed to describe systemic dependencies. Epistemic uncertainties are characterized through probability distributions or regions of feasible values. The uncertainties are propagated to the risk estimate by using Monte Carlo simulation or solving optimization problems. Risk importance measures are introduced and calculated to identify which scenarios contribute most to the overall risk level. This offers relevant information for risk management decisions.
Defense is held on 14.9.2021 at 12:00 Zoom link
Supervising professor
Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Zio, Enrico, Prof., Politecnico di Milano, Italy
risk assessment, scenario analysis, uncertainty, bayesian networks
Other note
  • [Publication 1]: Tosoni, Edoardo; Salo, Ahti; Zio, Enrico. 2018. Scenario analysis for the safety assessment of nuclear waste repositories: a critical review. Risk Analysis, 38(4), 755-776.
    DOI: 10.1111/risa.12889 View at publisher
  • [Publication 2]: Cadini, Francesco; Tosoni, Edoardo; Zio, Enrico. 2016. Modeling the release and transport of 90Sr radionuclides from a superficial nuclear storage facility. Stochastic Environmental Research and Risk Assessment, 30, 693-712.
    DOI: 10.1007/s00477-015-1112-7 View at publisher
  • [Publication 3]: Tosoni, Edoardo; Salo, Ahti; Govaerts, Joan; Zio, Enrico. 2019. Comprehensiveness of scenarios in the safety assessment of nuclear waste epositories. Reliability Engineering and System Safety, 188, 561-573.
    DOI: 10.1016/j.ress.2019.04.012 View at publisher
  • [Publication 4]: Salo, Ahti; Tosoni, Edoardo; Roponen, Juho; Bunn, Derek. Using cross-impact analysis for probabilistic risk assessment. Manuscript, 25 pages. Submitted in April 2021.
  • [Publication 5]: Salo, Ahti; Tosoni, Edoardo; Zio, Enrico. Risk importance measures for scenarios in probabilistic risk assessment with Bayesian networks. Manuscript, 31 pages. Submitted in October 2019.
  • [Publication 3 bis.]: Tosoni, Edoardo; Salo, Ahti; Govaerts, Joan; Zio, Enrico. 2020. Definition of the data for comprehensiveness in scenario analysis of near-surface nuclear waste repositories. Data in Brief, 31, 105780.
    Full text in Acris/Aaltodoc:
    DOI: 10.1016/j.dib.2020.105780 View at publisher