Machine learning-based prediction of the seismic response of fault-crossing natural gas pipelines

Herein, we utilized machine-learning (ML) and data-driven (regression) techniques to tackle a critical infrastructure engineering problem-namely, predicting the seismic response of natural gas pipelines crossing earthquake faults. Such a 3D nonlinear problem can take up to 10 h to solve by performing finite element analysis (FEA), considering the length of the pipeline and a large number of pipe and soil elements. However, the ML and data-driven techniques can learn the projection rule of input-output and predict the pipeline response instantaneously given a set of input features. In addition, the well-trained ML model can be implemented for regional-scale risk and rapid post-event damage assessments. In this study, the input for ML comprised approximately 217K nonlinear FEAs, which covered a wide range of combinations of soil, structural and fault properties and yielded critical pipe strain responses under fault-rupture displacements. We adopted various regression models and physics-constrained neural networks, which can accurately and rapidly predict the tensile and compressive strains for a broad range of probable fault-rupture displacements. Performances of various ML and conventional statistical models were systematically examined. Not surprisingly, neural networks exhibited the best performance for this multi-output regression problem, in which R-2 > 0.95 was achieved for a wide range of fault displacement (FD) levels. Further, we used the trained neural network with 14.5 million Monte-Carlo-generated input samples to predict the maximum tensile and compressive strain curves of pipelines. This new dataset aimed at filling the missing input-output points from the 217K FEAs, and improved the accuracy of the prediction of probability of failure for natural gas pipelines under FD hazards.