A comparative machine and deep learning approach for predicting ultimate bearing capacity of shallow foundations in...

This study developed a Python-based framework to predict the ultimate bearing capacity of shallow foundations on cohesionless soil, employing machine learning (ML) and deep learning (DL) techniques. Utilizing a comprehensive dataset of 116 footing experiments, Eleven ML models (Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF), Categorical Boosting (CatBoost) etc.) and five DL models (Artificial Neural Network (ANN), Deep Neural Network (DNN), etc.) trained and compared against traditional methods. Input parameters included foundation dimensions and soil properties. Results demonstrated that ML and DL models significantly outperformed traditional equations, achieving higher accuracy. Ensemble methods like GPR, XGBoost, GBM, RF, and CatBoost exhibited superior performance, with a Coefficient of Determination (R²) values above 0.988 and a Mean Absolute Percentage Error (MAPE) below 5.07%. Conversely, traditional methods showed lower accuracy, with R² values ranging from 0.684 to 0.82 and MAPE exceeding 19.63%. Taylor diagram analysis confirmed the improved performance of ML and DL. Additionally, a SHapley Additive exPlanations (SHAP) analysis highlighted foundation depth and soil friction angle as the most influential parameters, consistent with geotechnical principles.