Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach

This study integrates systematic laboratory testing with advanced machine learning techniques to predict the unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iași, Romania. Laboratory experiments generated 185 UCS measurements, examining the effects of cement content, curing period, and compaction velocity on strength development. Fourteen regression algorithms were initially screened, with the top three performers subsequently evaluated using nested cross-validation and Bayesian hyperparameter optimization via the Optuna framework. Correlation analysis identified cement content as the primary factor, with curing period as moderately influential and compaction rate having minimal impact when target density was achieved. Random Forest emerged as the optimal algorithm, providing robust and accurate UCS predictions. Beyond standard predictions, a two-stage uncertainty quantification system was implemented, allowing for both central estimates and reliable confidence intervals. SHAP analysis confirmed the dominant roles of cement content and curing period and enabled mechanistic interpretation of parameter contributions. The complete predictive system is available as a public web application, enabling geotechnical engineers to obtain rapid UCS predictions with quantified uncertainty, supporting efficient ground improvement design and risk assessment.