Analysis of process criticality accident risk using a metamodel-driven Bayesian network

In recent years, neural network metamodels have become increasingly popular for reducing the computational burden of performing direct, simulation-based analysis of physical systems. This paper proposes a new methodology for training a neural network metamodel and incorporating it into a Bayesian network-based probabilistic risk assessment. This methodology can be applied to a wide variety of industrial accidents, where there is at least one latent variable that is normally calculated using a physics code. The main benefit of this methodology is that it combines the interpretability and sampling algorithm of a Bayesian network with the high-dimensional, latent variable modeling capability of a neural network metamodel.