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

William J. Zywiec, Thomas A. Mazzuchi and Shahram Sarkani

Reliability Engineering and System Safety, 2021, vol. 207, issue C

Abstract: 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.

Keywords: Bayesian network; Metamodel; Neural network; Risk analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308152

DOI: 10.1016/j.ress.2020.107322

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