Author: Colin Schell
Date/Time: October 31st, 2024 at 2pm EST
Location: EGR-0151, Glenn L. Martin Hall | Zoom
Committee members
Dr. Katrina Groth, Chair
Dr. Abhijit Dasgupta
Dr. Mohammad Modarres
Dr. Yunfei Zhao
Dr. Michelle Bensi, Dean’s Representative
Title of dissertation: A Causal Information Fusion Model for Assessing Pipeline Integrity in the Presence of Ground Movement
Abstract:
Pipelines are the primary transportation method for natural gas and oil in the United States making them critical infrastructure to maintain. However, ground movement hazards, such as landslides and ground subsidence, can deform pipelines and potentially lead to the release of hazardous materials. According to the Pipeline and Hazardous Materials Safety Administration (PHMSA), from 2004 to 2023, ground movement related pipeline failures resulted in $413M USD in damages. The dynamic nature of ground movement makes it necessary to collect pipeline and ground monitoring data and to actively model and predict pipeline integrity. Conventional stress-based methods struggle to predict pipeline failure in the presence of large longitudinal strains that result from ground movement. This has prompted many industry analysts to use strain-based design and assessment (SBDA) methods to manage pipeline integrity in the presence of ground movement. However, due to the complexity of ground movement hazards and their variable effects on pipeline deformation, current strain-based pipeline integrity models are only applicable in specific ground movement scenarios and cannot synthesize complementary data sources. This makes it costly and time-consuming for pipeline companies to protect their pipeline network from ground movement hazards.
To close these gaps, this research developed a causal framework and information fusion model for assessing pipeline integrity in a variety of ground movement scenarios. The causal framework categorizes and describes how different risk-influencing factors (RIFs) affect pipeline reliability and was created based on academic literature, joint industry projects, PHMSA projects, pipeline data, and input from engineering experts. The framework was the foundation of the information fusion model which leverages SBDA methods, Bayesian network (BN) models, pipeline monitoring data, and ground monitoring data to calculate the probability of failure and the additional longitudinal strain needed to fail the pipeline. The information fusion model was then applied to several case studies with different contexts and data to compare model-based recommendations to the actions taken by decision makers. In these case studies, the proposed model leveraged the full extent of data available at each site and produced similar conclusions to those made by decision makers. These results demonstrate that the model can be used in a variety of ground movement scenarios and exemplified the comprehensive insights that come from using an information fusion approach for assessing pipeline integrity. The proposed model lays the foundation for the development of advanced decision making tools that can enable operators to identify at-risk pipeline segments that require site specific integrity assessments and efficiently manage the reliability of their pipelines in the presence of ground movement.