Advanced machine learning models for estimating seismic bearing capacity of shallow strip footings considering P- and S-wave effects

Seismic bearing capacity is a critical factor in designing shallow foundations, particularly in earthquake-prone regions, where traditional analytical methods often fall short. This study explores machine learning (ML) techniques to predict the seismic bearing capacity factor of shallow strip footings, explicitly considering the influence of both primary (P) and secondary (S) seismic waves. Multiple evolutionary ML models - Decision Trees (DT), Random Forest (RF), Adaptive Boosting (AdaB), and CatBoost (CB) - were developed and evaluated using robust statistical metrics. Among these, the CatBoost model emerged as the most accurate and reliable, delivering strong performance with R 2 (coefficient of determination) values of 0.957 in training and 0.936 in testing, alongside low mean absolute error (MAE) and root mean square error (RMSE). Notably, CatBoost surpassed the standard analytical formulas, underscoring its superior predictive power. A comprehensive parametric analysis further confirmed the model's reliability, showing strong agreement with trends reported in established literature. These findings demonstrate CatBoost's effectiveness as both a research tool for soil-structure interaction analysis under seismic conditions and a practical solution for geotechnical engineers requiring reliable bearing capacity assessments. • ML captures soil's nonlinear seismic response, replacing conventional analyses • Gradient boosting outperforms classical models in predicting seismic bearing capacity • CatBoost shows high robustness and accuracy in seismic bearing capacity prediction • CatBoost shows stable, generalizable performance for static and seismic loading cases