A Probabilistic Method for Assessing Pipeline Strain Demand Using InSAR Ground Movement Data

Abstract Ground movement hazards, such as landslides, ground settlement, and heave, pose a major threat to buried pipelines and resulted in $391M USD in property damages in the United States from 2002 to 2021. The dynamic nature of ground movement makes it necessary to actively model and predict pipeline integrity in order to maintain a reliable pipeline network. Strain-based design and assessment (SBDA) methods excel at predicting pipeline failure in the presence of large longitudinal strains that commonly result from ground movement hazards. Applying SBDA methods to operational pipelines requires the estimation of strain demand, the strain induced on the pipeline. Synthetic Aperture Radar (SAR) data, acquired by satellite, can be used to compute ground movement using SAR interferometry (InSAR). When combined with pipe-soil interaction (PSI) modeling, InSAR offers an attractive method for estimating strain demand; InSAR can cover large areas of interest and provide precise ground displacement measurements at a high spatial and temporal resolution. This paper presents a probabilistic method for predicting pipeline strain demand using ground movement data computed with InSAR. Pipe-soil interaction models from prior research were integrated into a Bayesian network (BN) which accounts for the environmental effects on pipeline displacement. Model performance was tested using a landslide case study in which the predicted axial strain was within reason. However, the model needs further work to accurately predict bending strain. The high resolution of InSAR data and the use of BN models enable the probabilistic evaluation of strain demand without the need for finite element method (FEM) models. The proposed method can empower pipeline companies to perform pipeline integrity assessments with greater ease, promoting a fast and data-informed response to ground movement hazards.