# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.4 Practical Application Examples
This analysis quantifies the engineering principle that high-strength cement-treated soils derive their performance primarily from cement dosage and adequate curing time. The systematic comparison between these scenarios reveals important practical insights for engineering applications. The high-strength application achieves not only greater absolute strength but also enhanced relative predictability, with uncertainty reducing from \(\pm\)20.2% to \(\pm\)13.5%. This pattern suggests that heavily cemented mixtures become more  
Figure 14: SHAP waterfall analysis demonstrating parameter contributions to high strength predictions. Figure 13: SHAP waterfall analysis demonstrating parameter contributions to moderate strength predictions. predictable and reliable, supporting aggressive optimization strategies for demanding applications while providing greater confidence in meeting stringent performance requirements. The uncertainty estimates prove particularly valuable for construction quality control and monitoring protocols. When field testing yields UCS values that fall near the bounds of predicted confidence intervals, engineers can make informed decisions about whether observed performance represents normal variability or indicates potential construction issues requiring investigation. For instance, if field tests on the 5% cement mixture yield values around 1900 kPa, the uncertainty framework confirms this falls within the expected range (1870-2818 kPa), avoiding unnecessary remedial work while maintaining appropriate quality standards. Furthermore, the uncertainty quantification enables sophisticated risk assessment and project planning capabilities. Engineers can estimate probabilities that specific design requirements will be met, supporting decisions about testing frequency, acceptance criteria, and contingency planning. For the moderate strength scenario, if a project absolutely requires 2200 kPa minimum strength, the system indicates approximately 77% probability of achieving this target based on the predicted distribution. This probabilistic information supports more nuanced and economically efficient project delivery compared to traditional factor-of-safety approaches. The mechanistic interpretability provided by SHAP analysis enhances these practical applications by enabling targeted optimization strategies. If field performance consistently falls toward the lower bounds of predicted intervals, engineers can identify which parameters offer the most effective pathways for improvement. The waterfall analyses demonstrate that for moderate strength applications, extending curing time may provide more immediate benefits than increasing cement content, while for high-strength applications, cement dosage optimization represents the most direct path to enhanced performance. This integrated approach of precise uncertainty quantification combined with mechanistic interpretability represents a significant advancement in geotechnical engineering practice, enabling engineers to make informed decisions based on quantified confidence levels rather than relying solely on conservative safety factors and empirical experience.