Esra Tatlıoğlu

Predictive modeling of soaked and unsoaked California bearing ratio for coarse and fine-grained soils: a comparative study using XGBoost and ANN architectures

The California Bearing Ratio is a vital parameter for pavement design, but traditional laboratory testing is expensive and time-consuming. This study addresses the limitations of simplified empirical models by developing advanced data-driven frameworks using Artificial Neural Networks and Extreme Gradient Boosting. Uti...