Advances in probabilistic risk assessment (PRA) for decision-making under uncertainty

A core problem in the nuclear industry arises from the need to make high-stakes decisions under uncertainty. Timely examples in the UK include decisions over the extension of life for components in current nuclear installations, with multiple Advanced Gas Reactor (AGR) plants due to reach or exceed their planned life over the next 10 years. The traditional approach to the risk assessment task, developed over more than 30 years, has been to apply methods based on probabilistic risk analysis, with approaches to characterise failure scenarios having typically included fault trees, event trees, Markov models and, more recently, Bayesian belief networks. The data captured within these formalisms has typically been evaluated using purely probabilistic methods. However, traditional PRA has issues in characterising certain forms of uncertainty, for example where knowledge of the underlying physical phenomena is limited. In addition, a key limitation in model-based PRA is in performing probabilistic uncertainty quantification (UQ) across model levels, and thus integrating the predictions of a substructure/component model into a global model to make system-level decisions under uncertainty.
The aim of this project is to develop methods for combining uncertainties arising from multiple sources (data and physics-based models) within a Bayesian probabilistic risk assessment framework using graphical models as a combining framework, with the specific application being decision making under uncertainty for safety-critical and/or high-value structures. This will involve the student learning state-of-the-art techniques for quantifying uncertainty from both models and data; and for combining methods from machine learning and numerical model validation within the proposed framework. The project will build from small scale testing conducted within the LVV to application to real world case studies. The work constitutes a new research theme that builds on the current interests of the supervisory team in uncertainty quantification, model validation and machine learning.