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This study evaluates the performance of three machine learning algorithms—Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—in predicting the settlement of bored piles in clayey sand using data from 1,200 finite element method (FEM) simulations. The ANN model outperformed the others, achieving a coefficient of determination (R²) of 0.9946 and a root mean squared error (RMSE) of 15.14 mm, demonstrating its effectiveness in geotechnical applications. The findings sug

Author

nzeynepozak

Language

EN