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.

This study explores machine learning approaches for predicting unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iași, Romania. Laboratory testing generated 185 UCS measurements examining the influence of cement content, curing period, and compaction rate. Exploratory data analysis revealed strong correlations between UCS and cement content and moderate correlations with curing period, while compaction rate showed minimal impact. Multiple regression algorithms were evaluated using a nested cross-validation framework with Bayesian hyperparameter optimization. Random Forest demonstrated superior performance when trained on the complete dataset. Feature importance analysis confirmed cement content and curing period as the dominant factors influencing strength development. The model enables geotechnical engineers to predict stabilized soil strength without extensive laboratory testing, supporting efficient design of ground improvement works. The methodology demonstrates the potential of data-driven approaches in geotechnical engineering applications, with both original data and the trained model made publicly available to support further research and practical implementation.

This study presents an in-depth evaluation of the double-action oedometer (DAO) as an advanced testing apparatus for simulating soil compressibility under near-field conditions. Unlike the classic oedometer, which enforces full lateral confinement and single-direction loading, the DAO introduces a dual-loading mechanism. A large platen simulates preconsolidation pressure σ’p, while a concentric piston does the incremental vertical loads, which in effect allows partial lateral deformation. The objective is to reflect the in-situ anisotropic stress paths so that post-test theoretical corrections could be minimised. Silty clay samples were tested using both the DAO and the classic oedometer. Compressibility parameters Eoed, Cc, mv and av were compared. The DAO results recorded higher stiffness Eoed = 10,867 kPa, reduced strain, and more realistic ei trends. The DAO Eoed in comparison with the classic gives M0 = 1.04, which is lower than the theoretical M0 from the correction coefficient in the NP 112/2014. These outcomes could indicate the overestimation of the M0 in theoretical standards. Additionally, the lower strain and settlement yields from the DAO testing under identical stress levels indicate reduced influence of sample disturbance. The apparatus effectively simulates the natural soil stress history and void ratio evolution. This leads to improved prediction of settlement and more accurate derivation of mechanical parameters used in design. The DAO demonstrates clear benefits for geotechnical modelling, offering a cost-effective alternative to classic and modified oedometers. Its potential for standardization and integration into geotechnical codes is significant