# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.3 System Interpretability and Feature Analysis  
Understanding how both components of the uncertainty quantification system make their decisions provides crucial insights for engineering applications. The feature importance analysis reveals distinct patterns between factors driving central UCS predictions versus those influencing prediction uncertainty, offering valuable guidance for practical implementation. Primary Model Feature Importance  
The primary Random Forest model confirms and quantifies established engineering understanding of cement stabilization mechanisms. Cement content emerges as the dominant factor influencing UCS development, accounting for 78.5% of the model's predictive importance (Figure 8d). This overwhelming dominance aligns perfectly with established understanding of cement stabilization mechanisms, where the quantity of cementitious binder directly controls the extent of hydration reactions and the resulting strength of the soil-cement matrix. Curing period demonstrates the second highest importance at 21.1%, reflecting the time-dependent nature of cement hydration and strength development processes. This substantial but secondary importance confirms that while adequate curing time is essential for strength development, its impact is significantly less than cement content. The feature importance analysis reveals that the effects of curing period are not simply linear but involve complex interactions with cement content, where higher cement contents provide more opportunities for continued hydration reactions over extended periods. Compaction rate demonstrates minimal importance at only 0.4%, confirming the experimental observations that variations in compaction velocity within the investigated range have negligible impact on final strength development. This finding has significant practical implications, suggesting that field construction procedures can accommodate reasonable variations in compaction rates without substantially affecting the engineering properties of stabilized soils, provided that target densities are achieved. The binned effect analysis in Figure 11 provides deeper insights into the non-linear relationships between input parameters and UCS predictions. The curing period analysis  
Figure 10: Calibration assessment of uncertainty quantification system performance: (**a**) Empirical vs. predicted coverage reliability. (**b**) Quantitative calibration quality scores. reveals that strength contributions peak around 25.7 days, suggesting that while extended curing continues to benefit strength development, the rate of improvement may begin to plateau beyond this timeframe. The compaction rate analysis confirms its minimal influence, with the peak effect occurring at 0.8 mm/min but within a narrow range that validates the experimental observation of limited practical significance. Most importantly, the cement content analysis demonstrates a strong, nearly monotonic relationship that peaks at 9.2%, very close to the maximum experimental dosage of 10%, indicating that higher cement contents would likely continue to provide strength benefits if practical and economic considerations permit. The SHAP summary plot in Figure 12 provides a complementary perspective to the binned effect analysis, clearly illustrating both the magnitude and directional impact of each feature on UCS predictions. The plot confirms that high cement content values (red points) consistently drive positive impacts on predicted UCS, while low cement content values (blue points) result in negative impacts, demonstrating the monotonic relationship identified in the binned analysis. Similarly, the curing period shows a predominantly positive relationship with UCS, though with greater variability compared to cement content. The compaction rate feature clusters tightly around zero impact, visually reinforcing its minimal influence on strength predictions across the entire range of experimental conditions.