# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 4 Conclusions  
This study successfully combined comprehensive experimental testing with advanced machine learning techniques to investigate and predict the unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iasi, Romania. Based on the analysis of 185 UCS measurements and the development of a sophisticated uncertainty quantification system, the following key conclusions emerge:  
* The experimental program systematically revealed the hierarchical importance of factors governing strength development in cement-stabilized soils. Cement content emerged as the dominant determinant of UCS development, exhibiting a strong positive correlation (0.87) that follows a non-linear pattern with accelerating strength gains at higher dosages. This relationship reflects the transition from localized cementation at low cement contents (2.5%) to the formation of continuous cementitious matrices at higher dosages (7.5-10%), fundamentally altering the soil's load-bearing mechanisms. * Curing period demonstrated a moderate but significant correlation with UCS (0.50), confirming the time-dependent nature of cement hydration and pozzolanic reactions. While substantial strength development occurred within the first seven days, continued strength enhancement persisted through twenty-eight days, particularly pronounced at higher cement contents where extended hydration opportunities exist. * Within the investigated range (0.75-1.25 mm/min), compaction rate exhibited minimal influence on UCS development (R\({}^{2}\approx 0.04\)) when specimens achieved identical target densities. This finding has profound practical implications, suggesting that field construction protocols can accommodate reasonable variations in compaction velocity without compromising engineering performance, provided density targets are met. This result clarifies the distinction between compaction energy effects (which influence density) and compaction rate effects (which have minimal impact when density is controlled). * The systematic evaluation of fourteen regression algorithms revealed that tree-based ensemble methods significantly outperformed linear approaches, with performance gaps exceeding 10% in R\({}^{2}\) scores. This substantial difference confirms that UCS development in cement-treated soils involves complex, non-linear interactions that cannot be adequately captured through traditional empirical relationships. * Random Forest emerged as the optimal algorithm through rigorous nested cross-validation with Bayesian hyperparameter optimization, achieving exceptional performance (R\({}^{2}\) = 0.9471 \(\pm\) 0.0120, RMSE = 291.23 \(\pm\) 39.00 kPa). The algorithm's balanced performance characteristics--combining high accuracy with exceptional stability across validation folds--proved crucial for supporting the subsequent uncertainty quantification framework. * This research advances beyond traditional point prediction models by implementing a sophisticated two-stage uncertainty quantification system that provides both central UCS estimates and calibrated confidence intervals. The uncertainty model achieved R\({}^{2}\) = 0.5632 in predicting error magnitudes--a substantial accomplishment considering the inherent difficulty of forecasting prediction uncertainty rather than primary target values. The system demonstrated exceptional calibration quality across multiple confidence levels, achieving 67.6% empirical coverage for 68% confidence intervals (calibration score: 99.6%) and 93.5% coverage for 95% intervals (calibration score: 98.5%). This calibration consistency across different confidence thresholds and validation folds establishes the system's reliability for practical engineering applications where uncertainty estimates directly impact safety-critical decisions. The SHAP interpretability analysis provides mechanistic understanding of parameter contributions, enabling targeted optimization strategies.