Construction of a Strain-Based Bayesian Network for Assessing Pipeline Risk due to Ground Movement

Abstract Ground movement events pose a significant threat to buried natural gas and oil pipelines which resulted in an estimated $388 million in damages from 2002 to 2022. Strain-based design and assessment (SBDA) methods are commonly used to manage pipeline integrity in ground movement scenarios but utility companies still face many challenges in applying SBDA to pipeline integrity management. This paper presents the development process of a Bayesian network structure using SBDA methods for managing pipeline integrity in the event of ground movement hazards such as landslides and ground subsidence. The Bayesian network model proposed in this work presents a first-of-its-kind approach to: (1) integrate the multiple risk factors and data sources required to model SBDA and assess pipeline risk; (2) identify the sources of uncertainty that affect pipeline risk estimates; and (3) provide a holistic tool for addressing pipeline risks stemming from ground movement events. Quantification and performance validation of the Bayesian network is an ongoing process, but the model is expected to utilize satellite-based ground movement data, infield strain measurements, in-line inspection defect data, strain accumulation models, metallurgical data, and knowledge of the past loading envelope of the pipeline to perform a quantitative risk assessment for a network of pipelines. In this paper, a novel SBDA taxonomy is presented, analytical strain capacity equations are selected for future use, and the overall model architecture is developed to support the creation of a comprehensive and robust model.