An elicitation process to quantify bayesian networks for dam failure analysis

Bayesian networks support the probabilistic failure analysis of complex systems, e.g., dams and bridges, needed for a better understanding of the system reliability and for taking mitigation actions. In particular, they are useful in representing graphically the interactions among system components, while the quantitative strength of the interrelationships between the variables is measured using conditional probabilities. However, due to a lack of objective data it often becomes necessary to rely on expert judgment to provide subjective probabilities to quantify the model. This paper proposes an elicitation process that can be used to support the collection of valid and reliable data with the specific aim of quantifying a Bayesian network, while minimizing the adverse impact of biases to which judgment is commonly subjected. To illustrate how this framework works, it is applied to a real-life case study regarding the safety of the Mountain Chute Dam and Generating Station, which is located on the Madawaska River in Ontario, Canada. This contribution provides a demonstration of the usefulness of eliciting engineering expertise with regard to system reliability analysis.