Predicting bentonite swelling pressure: optimized XGBoost versus neural networks

Abstract

The swelling pressure of bentonite and bentonite mixtures is critical in designing barrier systems for deep geological radioactive waste repositories. Accurately predicting the maximum swelling pressure is essential for ensuring these systems' long-term stability and sealing characteristics. In this study, we developed a constrained machine learning model based on the extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. A dataset containing 305 experimental data points was compiled, including relevant soil properties such as montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density. The GWO-XGBoost model, incorporating a penalty term in the loss function, achieved an R2 value of 0.9832 and an RMSE of 0.5248 MPa in the testing phase, outperforming feed-forward and cascade-forward neural network models. The feature importance analysis revealed that dry density and montmorillonite content were the most influential factors in predicting maximum swelling pressure. While the developed model demonstrates high accuracy and reliability, it may have limitations in capturing extreme values due to the complex nature of bentonite swelling behavior. The proposed approach provides a valuable tool for predicting the maximum swelling pressure of bentonite-based materials under various conditions, supporting the design and analysis of effective barrier systems in geotechnical engineering applications.