ANN-Based Assessment of Soft Surface Soil Layers’ Impact on Fault Rupture Propagation and Kinematic Distress of Gas Pipelines

Abstract

Large-scale lifelines in seismic-prone regions very frequently cross areas that are characterized by active tectonic faulting, as complete avoidance might be techno-economically unfeasible. The resulting Permanent Ground Displacements (PGDs) constitute a major threat to such critical infrastructure. The current study numerically investigates the crucial impact of soil deposits, which usually cover the ruptured bedrock, on the ground displacement profile and the kinematic distress of natural gas pipelines. For this purpose, a decoupled numerical methodology, based on Finite Element Method (FEM), is adopted and a detailed parametric investigation is performed for various fault and soil properties. Moreover, the advanced capabilities of Artificial Neural Networks (ANNs) are utilized, aiming to facilitate the fast and reliable assessment of soil response and pipeline strains due to seismic faulting, replacing time-consuming FEM computations. An extensive sensitivity analysis is performed to select the optimal architecture and training algorithm of the employed ANNs for both the geotechnical and structural parts of the decoupled approach, with suitable input and target values related to bedrock offset, fault and soil properties, surface PGDs, and pipeline strains. The proposed ANN-based approach can be efficiently applied by practice engineers in seismic design and route optimization of natural gas pipelines.