Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference

Abstract

The unintentional release of urban buried gas pipeline may cause crucial consequences to the economy, society and environment. Corrosion and external interference are primary causes of pipeline failure incidents. Due to the complexity and unpredictability of outside influence on the buried gas pipeline, this paper presents an approach to analyze pipeline failure frequency and leakage size caused by corrosion and external interference based on pipeline characteristics. Bayesian network method is used to construct a knowledge model. Pipeline characteristics statistics and failure data are collected to build the relationships among variables in the model and verify the applicability of the model. Results show that the proposed model can estimate buried gas pipeline failure frequency and leakage size caused by corrosion and external interference. It is also capable of highlighting the critical parameters to pipeline failure. Practical application of the model is demonstrated on the underground gas pipeline in the City of H, China. Results indicate that proposed model can explicitly quantify uncertainties and then put forward practical measures for buried gas pipeline parameter design, laying plan and operating maintenance.