Prediction of the Vertical Bearing Capacity of Piles in Cold Saline Environments by a Multi-Dimensional Machine Learning Approach

This study proposes an innovative methodology that integrates finite element simulation, machine learning, and interpretable model analysis to predict the vertical bearing capacity of piles in cold saline environments. Initially, Python scripts are developed to drive the ABAQUS platform, and LHS (Latin Hypercube Sampling) is employed to generate random parameter combinations to construct a multi-dimensional ML (machine learning) database. Six ML models, including XGBoost and LightGBM, are developed with hyperparameters optimized by cross-validation and grid search. Model performance is evaluated by five metrics (R2, MSE, RMSE, MAE, and MAPE). Finally, parametric sensitivity is analyzed by the SHAP (SHapley Additive exPlanations) method. The study demonstrates that: (1) the XGBoost and LightGBM models achieve optimal performance on the test set, and the generalization ability significantly exceeds other models; (2) pile diameter is the primary factor influencing vertical bearing capacity, and corrosion depth exhibits higher sensitivity than corrosion thickness; and (3) the bearing capacity of the pile is predicted by using the automated parametric modeling method based on Python (3.8)-ABAQUS (2022). The automated modeling and prediction framework may serve as a reference for pile design in similarly complex environments.