Geocell reinforcement has been increasingly applied in transportation infrastructure to improve the stability and serviceability of road embankments constructed on weak subgrades. The load transfer mechanism in geocell–soil systems involves complex interactions between soil confinement, lateral restraint, and membrane effects, which makes the design process challenging using conventional empirical approaches. This study presents a numerical and data-driven framework to investigate the performance of geocell-reinforced embankments subjected to static loading. Finite element simulations were conducted, considering variations in geocell location, geocell height, and distributed load. The simulations are automated through the PLAXIS Python API to generate a comprehensive dataset of embankment responses. A predictive model is then developed using Gene Expression Programming to estimate the settlement of reinforced embankments. A parametric study is subsequently performed to determine effective design configurations. The proposed framework provides a practical tool for improving the reliability and efficiency of geocell-reinforced embankment design.
H. Bui, V. Phan, Thanh-Thien La et al.· Journal of Science & Technol...· 0 citations
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.
Van-Ngoc Pham, H. Do, Thi Phuong Khue Nguyen et al.· Journal of Science & Technol...· 0 citations
