Physics-informed neural network-based reliability analysis of buried pipelines subjected to permanent ground displacement

• PINN-RA integrates physics-informed neural networks with Monte Carlo simulation • Extended PINN-based surrogate model solves parametric differential equations of buried pipelines • Uncertainties in soil properties and ground movement magnitude incorporated directly into the model • Ground movement magnitude identified as the dominant factor in reliability assessment • PINN-RA delivers substantial computational efficiency over FEM and FDM methods . Buried pipelines transporting oil and gas across geohazard-prone regions are exposed to potential ground movement, leading to the risk of significant strain demand and structural failure. Reliability analysis, which determines the probability of failure after accounting for pertinent uncertainties, is essential for ensuring the safety of pipeline systems. However, traditional reliability analysis methods involving computationally intensive numerical models, such as finite element simulations of pipelines subjected to ground movement, have limited applications; this is partly because stochastic sampling approaches require repeated simulations over a large number of samples when estimating low probabilities. This study introduces Physics-Informed Neural Network for Reliability Analysis (PINN-RA) for buried pipelines subjected to horizontal permanent ground movement, integrating a PINN-based surrogate model with Monte Carlo Simulation (MCS) to achieve efficient reliability assessment. To enable its application under uncertain variables associated with soil properties and ground movement, the PINN-based surrogate model is extended to solve a parametric differential equation system, namely the governing equation of pipelines embedded in soil with different properties. The findings demonstrate that PINN-RA significantly reduces the computational effort required and thus accelerates reliability analysis for pipelines subjected to ground movement, enabling rapid decision-making in geohazard-prone regions.