## 4 Collected Database and Statistical Analysis
soils (H) (LL \(\geq\) 50), and 50 to low-plasticity soils (L) (LL \(<\) 50). Moreover, according to [PERSON]'s [45] criteria, 102 of these samples would be classified as moderately expansive and 51 as highly expansive. In summary, the dataset covers a wide range of geotechnical conditions representative of expansive soils in southeastern Spain. According to their index properties, the soils span low- to high-plasticity ranges and include both clays and silts, as confirmed by the subsequent USCS classification. This variety of properties supports the generalizability and robustness of the proposed models. Next, \(p\)-values and Variance Inflation Factor (VIF) for the predictive variables were calculated (Table 3). The \(p\)-value represents the probability that the observed relationships between the independent variables and the dependent variable occurred by chance. A lower \(p\)-value indicates a statistically significant effect, typically below a threshold of 0.05. VIF measures multicollinearity among independent variables, with values above 10 suggesting a high degree of correlation. In the analysis, the \(p\)-values for the percentage of soil passing #0.075 mm, LL, PI, and \(\gamma_{d}\) were well below 0.05, indicating that these variables had a statistically significant impact on the dependent variable. Conversely, w presented a high \(p\)-value (0.852), suggesting it is not significantly contributing to the model. The VIF values indicated strong multicollinearity, particularly for LL and the percentage of soil passing #0.075 mm, as these properties are related to most of the others. Additionally, the correlation matrix between the variables of the dataset is presented in Figure 5, using the Pearson correlation coefficient (r) and the Spearman coefficient. These \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SUCS** & **CL** & **CH** & **ML** & **MH** \\ \hline Samples & 73 & 70 & 15 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of soil samples according to the Unified Soil Classification System (USCS). Figure 4: Representation of the soil samples on the Casagrande plasticity chart, showing their classification based on the Unified Soil Classification System (USCS). correlation matrices revealed critical insights about the relationships between soil properties and \(\mathrm{p_{s}}\). Pearson correlations indicate linear dependencies, while Spearman correlations capture monotonic relationships, providing a comprehensive view of variable interactions. For the target variable (\(\mathrm{p_{s}}\)), both matrices showed consistently strong associations, though with notable differences in magnitude. PI exhibited the highest correlation with \(\mathrm{p_{s}}\) in both methods, suggesting PI is the most reliable predictor. LL also showed strong positive correlations, reinforcing its importance in swelling prediction models. Water content demonstrated moderate correlations, indicating a secondary role in swelling behaviour, as well as showing a moderate to high correlation with the percentage of soil passing #0.075 mm and a very high \(p\)-value. \(\mathrm{\gamma_{d}}\) showed a weak negative correlation with \(\mathrm{p_{s}}\), which may justify its inclusion as a secondary predictor, especially considering its low correlation with the other variables. The percentage of soil passing #0.075 mm presented moderate positive correlations, though the Spearman coefficient is notably lower, suggesting the relationship may involve nonlinear components.