## 2 Accurate Identification Method of Fractured Rock Structure
### Concept and Determination Method of Occlusion Degree of Fractured Rock Masses
#### 2.3.5 Calculation Method of Occlusion Degree
Multi-Factor Fractured Rock Quality Classification Study based on Fragmentation and Occlusion Degree  
Determination of Evaluation Indexes and Weights of Multi-Factor Fractured Rock Quality Classification  
According to the engineering characteristics of fractured rock mass, six main factors affecting the quality classification of fractured rock mass are selected, namely, fracture degree, occlusion degree, rock mass RQD, rock uniaxial saturation compressive strength,  
Figure 4: Model diagram of the occluded arch of the broken rock. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fractured rock classification**} & \multirow{2}{*}{Relative fractured rock} & \multicolumn{3}{c}{Fractured rock} \\ \cline{3-5}  & & Non-filled hard structural surface & Mud-laden soft structural surface & Dispersed body \\ \hline
**Degree of occlusion** & Strong occlusion & Moderate occlusion & Weak occlusion & No occlusion \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification evaluation table of occlusion degree. rock mass permeability, and structural surface inclination angle of fractured rock mass in the cave as evaluation indexes, which are easily obtained in the field or through experiments. The quality evaluation system of fractured rock masses is established, and the quality classification of fractured rock masses is determined by different index assignment mechanisms. The fuzzy judgment matrix of the main factors affecting the quality evaluation of fractured rock masses is shown in Table 4.  
The final geometric mean of the fuzzy weight components obtained according to the above method, i.e., the weight vector containing the deterministic values, is:  
\[\text{W}=[0.3526,0.1020,0.2383,0.1712,0.1020,0.0339]\]  
Considering the subjective nature of decision making and the complexity of objective factors, it is necessary to test the consistency of the judgment matrix with the consistency indexes. \[CI=\frac{\lambda_{\text{max}}-n}{n-1} \tag{3}\]  
where \(\lambda_{\text{max}}\) is the maximum characteristic root of the judgment matrix and \(n\) is the order of the judgment matrix. In order to measure whether the judgment matrix of different orders has satisfactory consistency, it is necessary to introduce the average random consistency index RI value of the judgment matrix. The RI values of the judgment matrix of orders 1 to 15 are listed in Table 5 below. When the order is greater than 2, the judgment matrix is considered to have satisfactory consistency when the consistency ratio CR = CI/RI \(<\) 0.10. The CI = 0.0403 can be obtained by calculation, check Table 5, RI = 1.26 for \(n\) = 6, CR = 0.0403/1.26 = 0.0319 \(<\) 0.10, which indicates that the judgment matrix has satisfactory consistency and does not need to be adjusted. That is, the corresponding weights of fragmentation, occlusion, rock RQD (%), rock uniaxial compressive strength, rock permeability, and the inclination angle of joints in the cavern of broken rock tendency are 35.26%, 10.20%, 23.83%, 17.12%, 10.20%, and 3.39%, respectively.