A Lightweight Bayesian Network Framework for Risk Analysis of Hydrogen-Blended Pipelines with Large Language Model Integration

Abstract Probabilistic risk analysis is a fundamental approach for safety assessment in engineering systems. However, its application to emerging domains such as hydrogen-blended pipeline systems faces new challenges due to limited fault data and scarce expert knowledge. Bayesian networks (BNs) are widely adopted for risk analysis because they illustrate the relationships between risk factors and facilitate probabilistic reasoning. Under these conditions, two major challenges arise. First, the network structure, including the relevant risk factors and their dependencies, is typically unavailable. Second, when risk factors have multiple causes, the parameter space of conditional probabilities grows exponentially, which requires extensive expert input for parameter specification. To address these problems, this study presents a lightweight framework for risk analysis that minimizes the need for expert judgment and does not rely on data. The framework centers on a BN for probabilistic computation. A large language model identifies risk factors and their relationships from technical documentation. Additionally, a parameter reduction method named pre-CPT is developed to compress the dimensionality of conditional probability tables from an exponential to a polynomial scale through weighted combinations. This reduces the need for expert elicitation. The framework is validated through a case study on hydrogen-blended natural gas pipeline risk assessment and a benchmark and numerical comparison. The results show less than a 5% deviation in posterior probabilities and over a 90% consistency in BN structures compared with benchmark models. The workload for parameter specification is reduced to about 15% of that required in conventional BN modeling.