This study develops a data-driven framework using Extreme Gradient Boosting (XGBoost) to predict the Indirect Tensile Strength (ITS) of cement-treated clayey soils. Using 180 specimens with five input variables - cement content, curing time, curing temperature, plasticity index, and compaction energy - the model was trained (80%) and tested (20%), achieving strong accuracy (training R²=0.932, RMSE=55.41 kPa; testing R²=0.922, RMSE=81.98 kPa). SHapley Additive exPlanations (SHAP) analysis was applied for interpretability, showing cement content (39.3%) and curing time (30.1%) as the dominant predictors, followed by plasticity index (12.9%), while compaction energy (9.1%) and curing temperature (8.6%) had minor influence. K-means clustering combined with SHAP waterfall plots identified five distinct strength-development behavior groups, offering mechanistic insight into ITS variability. Overall, the XGBoost-SHAP framework proves to be a robust, interpretable tool for performance-based design and mixture optimization of cement-treated soils in infrastructure applications.
This study presents a statistical-experimental approach to optimise the proportions of Portland cement and ground granulated blast furnace slag (GGBS) for improving the strength characteristics of soft montmorillonitic clay (SMC). Response Surface Methodology (RSM) was employed to investigate the effects of three varia...
Sourabh Choudhary, Ankit Kumawat, L. Borana· Proceedings of the Instituti...· 0 citations
Alkali-activated recycled aggregate concrete (AARAC) offers a sustainable alternative to traditional concrete but suffers from complex, non-linear mechanical behavior that challenges conventional prediction methods. This study develops and compares five machine learning models, linear regression (LR), M5P, Random Fores...
Ahmed D. Almutairi, A. A. Al Sayed· Buildings· 0 citations
The use of supplementary cementitious materials such as fly ash can reduce environmental impacts and improve the sustainability of concrete construction. However, the nonlinear interactions among mixture design parameters make accurate prediction of concrete compressive strength challenging. In this study, TabPFN, a pr...
Zhihao Zhao, Jinjin Wang, Guohui Ma et al.· Buildings· 0 citations
: Microbially Induced Carbonate Precipitation (MICP) is an environmentally friendly technique for sandy soil stabilization. However, the cementation performance is governed by multiple coupled factors and complex experimental procedures, making accurate prediction challenging. In this study, a dual-objective XGBoost pr...
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
Yuchen Lin· International Conference on...· 0 citations
