Machine learning for predicting maximum displacement in soil-pile-superstructure systems in laterally spreading ground

Abstract

Extensive damage to pile-supported structures, often caused by earthquake-induced lateral spreading, has been reported frequently in numerous major earthquakes. To mitigate such damage, accurate prediction of the seismic behavior of the soil-pile-superstructure system (SPSS) has been extensively studied through experimental and numerical simulations. However, these methods typically require substantial time and high cost, making them challenging to adapt in practical engineering scenarios. This study successfully applied machine learning (ML) techniques to predict the maximum seismic response of the SPSS, offering a more efficient and flexible solution for engineers. Six ML algorithms were used: decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGB), random forest (RF), artificial neural network (ANN), and Gaussian process regression (GPR). A detailed evaluation of these algorithms has shown that ML models can effectively predict the maximum displacement of both pile and soil. Notably, XGB outperformed other methods in terms of accuracy, stability, and efficiency. Furthermore, the study indicates that the velocity-dependent ground motion parameter, root mean square velocity (v_RMS), effectively represents the ground motion parameters for accurately predicting maximum pile-soil displacement. This study demonstrates the potential of ML in geotechnical earthquake engineering, establishing a basis for further applications and contributing to enhanced seismic design of pile-supported structures in liquefiable soils.