# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 3 Results
### Construction of the Driving Model and Correlation Analysis  
This study employs a multiple linear regression (MLR) method to construct data-driven models that relate the compaction parameters, material source parameters, and meteorological parameters to the compaction quality indicator (dry density). These models help determine the influence of each parameter on the compaction quality, providing a scientific and rational theoretical foundation for controlling the compaction quality during earth-rock dam construction.  
To improve the accuracy of the MLR model, the 106 sets of data related to the influencing parameters and the dry density evaluation were normalized. Considering both the correlation coefficient (R) and the significance analysis results, only the parameters with significant correlation were selected for the regression models relating the response parameters to the overall dry density. Subsequently, MLR analysis was performed using SPSS software (www.ibm.com/legal/copytrade.shtml, accessed on 5 January 2025) to establish the dry density driving model, which is outlined as follows:  
\[\hat{y}_{i}^{c}=0.58-0.05x_{i}^{c1}+0.069x_{i}^{c2}-0.178x_{i}^{c3} \tag{10}\]  
\[\hat{y}_{i}^{l}=0.554-0.311x_{i}^{l1}-0.062x_{i}^{l2}+0.246x_{i}^{l3}-0.069x_{ i}^{l4} \tag{11}\]  
\[\hat{y}_{i}^{w}=0.539+0.029x_{i}^{w1}-0.079x_{i}^{w2} \tag{12}\]  
where \(\hat{y}_{i}^{c}\), \(\hat{y}_{i}^{l}\), and \(\hat{y}_{i}^{w}\) represent the linear regression evaluation values of the dry density-driven models for the compaction parameters, material source parameters, and meteorological parameters, respectively. \(x_{i}^{c1}\), \(x_{i}^{c2}\), and \(x_{i}^{c3}\) refer to the compaction thickness, static compaction cycles, and total cycles, respectively. \(x_{i}^{l1}\), \(x_{i}^{l2}\), \(x_{i}^{l3}\), and \(x_{i}^{l4}\) include the full material moisture content, curvature coefficient, \(P5\) content, and uniformity coefficient, respectively. \(x_{i}^{w1}\) and \(x_{i}^{w2}\) refer to temperature and humidity.  
By constructing the driving model for the compaction parameters and their correlation with the dry density, it was found that, under conditions where the basic parameters  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Moisture** & **P5 Content (\%)** & **Rolling** & **Humidity** & **Total Passes** & **Dry Density** \\ \hline Number of cases & 106 & 106 & 106 & 106 & 106 & 106 \\ \hline average & 8.526 & 42.38486 & 0.2556 & 44.75280 & 19.7 & 2.2104 \\ \hline median & 8.5 & 42.75 & 0.26 & 49.66935 & 19 & 2.21 \\ \hline standard deviation & 0.2756 & 4.54036 & 0.03186 & 17.28726 & 5.667 & 0.01702 \\ \hline minimum value & 8 & 2.35559 & 0.16 & 11.058 & 9 & 2.17 \\ \hline Maximum value & 9.4 & 48.2 & 0.31 & 72.42679 & 36 & 2.25 \\ \hline \end{tabular}
\end{table}
Table 2: The partial statistical summary of the sample set.  
cannot be guaranteed to remain constant, the number of static compaction cycles still shows a strong correlation with the dry density. Additionally, full material moisture content and _P5_ content are strongly correlated with compaction dry density, while temperature and humidity show weak correlations. Among these, humidity has a greater impact on compaction quality than temperature. Therefore, the input parameters for the model, such as the number of static compaction cycles from the compaction parameters, full material moisture content and _P5_ content from the material source parameters, and humidity from the meteorological parameters, all significantly influence the compaction dry density. These parameters provide a scientifically sound theoretical basis for controlling compaction quality at the earth-rock dam construction site.