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Earthquake-induced fault ruptures present a considerable risk to structures, especially underground systems like pile foundations. Batter pile foundations, among the various foundation types, are commonly employed for their effectiveness in withstanding inclined forces. Therefore, it is crucial to comprehensively understand how batter pile groups respond to fault ruptures under diverse geotechnical conditions to enhance geoengineering practices. In this study, 3D numerical modeling was used to investigate the internal force and damage distribution mechanisms of different batter pile groups subjected to various normal fault ruptures. Additionally, five novel machine learning regression models (i.e. Light Gradient Boosting Machine (LightGBM), CatBoost, Extreme Gradient Boosting (XGBoost), ExtraTrees, and Random Forest (RF)) were developed to learn and predict the impact of four input parameters related to batter piles and normal fault ruptures. A database comprising 375 datasets was extracted from numerical modeling results to build the learning and testing framework. The comprehensive results indicate that LightGBM has the highest potential for estimating the internal force and concrete damage distribution along batter pile foundations due to normal faults. The coefficient of determination (R2) exceeded 0.90 across all models, with reasonable values for mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). This study provides an effective method for estimating the response of batter pile foundations to normal fault ruptures. The findings can assist engineers in designing batter pile foundations and evaluating the damage conditions of structures subjected to fault ruptures prior to detailed inspections.

来源平台：GEOMECHANICS AND ENGINEERING