## 4 Results and Discussion
### Establishment and Application of Evaluation Models
#### 4.1.2 Modelling  
At present, traditional evaluation methods are more common in the prevention and control of water damage grouting, while TOPSIS and RSR evaluation methods are less commonly used. Traditional methods may have certain limitations and cannot comprehensively evaluate the grouting effect. TOPSIS and RSR methods have the advantage of comprehensive evaluation of multiple indicators. If introduced into this field, it is expected to more objectively measure the advantages and disadvantages of grouting schemes, construction quality, and prevention and control effects. In the study area, the final grouting pressure in each roof hole ranged between 9.3 and 10.0 MPa, which is 1.51 to 1.57 times the hydrostatic pressure of the \"Four Containers\" aquifer at the Cenozoic boundary. The final pressure in each lateral blocking hole ranged from 8.54 to 11.35 MPa, or 1.51 to 1.65 times the hydrostatic pressure of the sandstone aquifer in the 9-coal roof. For each floor hole, the final grouting pressure was between 14.36 and 16.49 MPa, representing 1.54 to 1.78 times the hydrostatic pressure of the Austrian ash aquifer. Similarly, the final pressure in the bottom plate ranged from 14.36 to 16.49 MPa, 1.54 to 1.78 times the hydrostatic pressure of the Austrian grey aquifer. All these values meet the design requirement of being no less than 1.5 times the hydrostatic pressure of the target layer. Based on the impact of the indicators on the evaluation results, the four indicators are classified as either positive or negative. Using the indicator weights derived earlier and following the TOPSIS method, the optimal and inferior matrix vectors were identified, and the relative proximity (C-value) of each borehole was calculated. As shown in Figure 3, the C-value ranges from 0.05 to 0.69, with low-value zones concentrated in the central and eastern parts of the 110504 area and the southern portion of the study area. As demonstrated in Table 5, based on the principles of the rank and ratio method, the values and rankings of the weighted rank and ratio are calculated. A distribution table is then compiled to determine the probability units. Subsequently, the regression equation is derived using Equation (7) as follows:  
\[WRSR=-0.104+0.122\times Probit \tag{15}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Norm** & **Subjective Weighting/Percent** & **Objective Weight/Percent** & **Combined Weight/Percent** \\ \hline Drilling fluid consumption & 6.25 & 6.25 & 4 \\ (m\({}^{3}\)/h) & 31.25 & 31.25 & 39 \\ \hline Dry material per unit length & 18.75 & 18.75 & 14 \\ (t/m) & 43.75 & 43.75 & 43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Combined weights of the indicators. After fitting and verification, the RSR method's calculation results are displayed in Figure 4, showing fitted values ranging from 0.04 to 0.78. The low-value areas are primarily concentrated in the central and eastern parts of 110504, as well as the southern region of the study area, aligning with the findings of the TOPSIS analysis. Figure 5 illustrates the RSR method's fitting curve, presenting both the original data and the model's predicted values. From the analysis of the RSR evaluation results, the model demonstrates a high goodness-of-fit, with a value of 0.938, indicating strong performance. Variations in the construction standards of workers, drilling and grouting processes, and the geological conditions within the boreholes contribute to the standard deviation in the fitting, though the model still satisfies the basic requirements for evaluation.