Fuzzy Bayesian network risk analysis of gas pipeline based on ranked nodes method and analytical hierarchy process

The failure of natural gas pipelines may lead to serious casualties and huge property losses, and it also poses a great threat to the surrounding ecological environment and social public safety. Risk analysis is an important technical means to predict potential safety hazards existing in pipelines. By carrying out systematic risk identification, quantitative evaluation and targeted preventive control, relevant departments can accurately grasp the risk level of pipeline operation and take corresponding measures to reduce the occurrence probability of pipeline failure effectively. However, in the actual engineering application of natural gas pipelines, the available historical accident data are often insufficient, and the relationship among various risk factors is complex and uncertain, which brings great difficulties to the accurate determination of pipeline failure probability and the rationality of risk assessment results. To solve this problem, this paper proposes a fuzzy Bayesian network risk analysis method for gas transmission pipelines based on the Ranking Nodes Method (RNM) and the Analytic Hierarchy Process (AHP). Firstly, Fuzzy Comprehensive Evaluation (FCE) is used to realize the quantification and systematic analysis of expert opinions, so as to reduce the subjectivity and fuzziness of expert judgment and calculate the prior probability of each key risk factor of pipelines. Secondly, the weight of each basic risk factor is calculated by using the Analytic Hierarchy Process, so as to reflect the relative importance of different risk factors in the process of pipeline failure. Thirdly, the Conditional Probability Table (CPT) between nodes is calculated according to the weight by using the Ranking Nodes Method, which provides a reliable data basis for the construction of Bayesian network model. Finally, the calculated prior probability and the conditional probability table between nodes are applied to the Bayesian Network (BN), so as to realize the accurate calculation of pipeline failure probability under the conditions of insufficient pipeline accident data and complex relationship of influencing factors. At the same time, through probability updating and reverse reasoning, the critical events that have a significant impact on pipeline failure can be efficiently identified. In order to verify the effectiveness of the method, it is applied to an actual gas transmission pipeline. According to the existing engineering database and on-site expert opinions, various potential risks in the operation process are identified comprehensively. These key risk factors are taken as basic nodes, and the logical relationship and influence mechanism between nodes are comprehensively analyzed to establish a complete Bayesian network model. Then, the prior probability and conditional probability are calculated by using the risk analysis method proposed in this study, and the calculation results are substituted into the Bayesian model to obtain the final pipeline failure probability and determine the critical events leading to pipeline failure. The calculated failure probability is close to the failure probability recorded in the existing database, which fully verifies the feasibility, rationality and accuracy of the method. The results show that this method can overcome the limitation of lack of accident data, realize the scientific and quantitative evaluation of pipeline operation risk, and provide important theoretical support and scientific guidance for pipeline technicians in daily safety management, risk early warning and maintenance decision-making.