Prediction of bearing capacity of geogrid-reinforced sand over stone columns in soft clay

Abstract

It is a challenge to estimate the ultimate bearing capacity (qrs) of a geogrid-reinforced sandy bed on vertical stone columns in soft clay due to their complex geometry and uncertain parameters. Predicting qrs can be costly and challenging, so creating an accurate prediction model is important for real-world applications. The research aims to use ensemble techniques like K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to develop a model for estimating the bearing capacity of geogrid-reinforced stone columns (GRBS) on a sandy bed with vertical stone columns in soft clay. A dataset of 245 experimental observations is used to train the models. The present study highlights the potential use of the XGBoost model as a useful tool that can assist in predicting the bearing capacity. The results of the study reveal that this model has performed well in predicting the bearing capacity with high correlation coefficients of 0.9947. Furthermore; a SHAP dependency analysis was conducted to ascertain the significance of each parameter.