Work

Multi-hazard probabilistic safety assessments using Bayesian networks – A framework and demonstration for integrating technical and human risk

Year: 2025

Type: article

Abstract: Despite the advantages of using Bayesian networks for probabilistic risk assessment, adoption in practice has been limited due to the lack of realistic, facility-scale studies. Scaling up from systems to facility-level safety assessments poses challenges in (i) integrating external hazards and their cascading effects, and (ii) resolving non-homogeneity of various technical and human reliability models. This study introduces a Bayesian network-based multi-hazard risk integration framework for facility-scale applications. The framework is demonstrated for a nuclear power plant subject to flooding and earthquake hazards, capturing dependencies among the hazards and consequences. Individual reliability models – conventionally extraneous to facility-wide risk models – are included as subnetworks using Bayesian network-based surrogate models for technical systems and a Bayesian networks approach for human reliability modelling. Two approaches are used for subnetwork integration – object-oriented and unified Bayesian networks. The unified approach allows for prediction, diagnostics and inter-causal reasoning since Bayesian inference is bi-directional. Conversely, in the object-oriented approach, diagnostics are limited to within individual subnetworks and as a consequence the model can potentially neglect dependencies between objects. However, the object-oriented model requires only 50% of the computational memory and consumes less than 25% of the runtime as the unified network, while improving visual clarity of the risk model. The framework reveals key insights – for example, variations in operator stress or available response time during a hazard event can result in up to a 77% change in top event probability – demonstrating its effectiveness in capturing critical relationships in complex, facility-scale risk scenarios.

Source: Nuclear Engineering and Design

DOI: 10.1016/j.nucengdes.2025.114558

Authors: Varenya Kumar D. Mohan, Pieter H.A.J.M. van Gelder, Pierre Gehl, Michael A. Hicks, Philip J. Vardon

Institutions: Delft University of Technology, Bureau de Recherches Géologiques et Minières

Language: English

Topic: Risk and Safety Analysis

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,710

Funders: European Commission, Horizon 2020 Framework Programme, H2020 Euratom