## 3 Result and Analysis
### TBM Selection Decision-Making  
According to the standards formulated in Table 9, the data in Table 4 are scored and the results are shown in Table 18.  
According to the method proposed in Section 2.5, this section makes TBM selection decisions for the three projects proposed in the previous section. A score of 0 represents a critical value that is not suitable for any TBM construction, 2 represents a critical value that is suitable for single-shield TBM and double-shield TBM construction and 4 represents a critical value that is suitable for double-shield TBM and open TBM construction. A value of 1 between 0 and 2 indicates that it is suitable to use single-shield TBM, a value of 3 between 2 and 4 indicates that it is suitable to use double-shield TBM and a value of 5 above 4 indicates that it is suitable to use open TBM. Using 0, 2 and 4 as the critical values for establishing the matrix can clearly determine the appropriate TBM type for the project. The above three scores are selected as the critical values of adaptability evaluation criteria to construct the evaluation matrix. Step 1. This paper constructs the initial evaluation matrix according to the method proposed in Section 2.5. First, place the critical value as the scoring standards 4, 2 and 0 from large to small in the first, second and third lines of the matrix, respectively, and then place the actual scores of various indicators of different projects in the fourth line. \begin{table}
\begin{tabular}{c c} \hline
**Indexes** & **Index Weight** \\ \hline \(U_{1}\) & 0.1016 \\ \(U_{2}\) & 0.1016 \\ \(U_{3}\) & 0.0722 \\ \(U_{4}\) & 0.3250 \\ \(U_{5}\) & 0.1691 \\ \(U_{6}\) & 0.1524 \\ \(U_{7}\) & 0.0781 \\ \hline \end{tabular}
\end{table} 
Table 16: Total weight of indexes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Score** & \(U_{1}\) **(MPa)** & \(U_{2}\) **(\(U_{3}\))** & \(U_{4}\) **(L/s)** & \(U_{5}\) **(m)** & \(U_{6}\) **(\%)** & \(U_{7}\) **(m)** \\ \hline
5 & (100, 150) & (0.65, 0.75) & \(>\)4 & (0, 5] & (0, 16] & (0.4, 0.45] & (300, 350] \\
4 & (80, 100) & (0.55, 0.65] & (2.5, 4] & (5, 15] & (16, 22.5] & (0.36, 0.4] & (350, 400] \\
3 & (60, 80] & (0.45, 0.50] & (2.0, 4] & (15, 20] & (22.5, 25] & (0.32, 0.36] & (400, 500] \\
2 & (30, 60] & (0.45, 0.50] & (1, 2.0] & (20, 45] & (25, 30] & (0.28, 0.32] & (500, 600] \\
1 & (10, 30] & (0.35, 0.45] & (0, 1] & (45, 100] & (30, 40] & (0, 0.28] & (600, 800] \\
0 & (0, 10] & (0, 0.35] & - & \(>\)100 & \(>\)40 & \(>\)0.45 & \(<\)300 \\ \hline \end{tabular}
\end{table} 
Table 17: Evaluation standard. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**\(U_{1}\)** & \(U_{2}\) **(\(U_{3}\))** & \(U_{4}\) **(\(U_{5}\))** & \(U_{6}\) **(\(U_{7}\))** \\ \hline Project 1 & 3 & 5 & 5 & 5 & 5 & 5 \\ Project 2 & 2 & 2 & 1 & 2 & 2 & 1 & 1 \\ Project 3 & 3 & 4 & 3 & 3 & 3 & 2 & 3 \\ \hline \end{tabular}
\end{table} 
Table 18: Scoring table of data required by the model. Project 1. \[P_{1}= \begin{pmatrix}4&4&4&4&4&4&4\\ 2&2&2&2&2&2&2\\ 0&0&0&0&0&0&0\\ 3&5&5&5&5&5&5\end{pmatrix} \]  
Project 2. \[P_{2}= \begin{pmatrix}4&4&4&4&4&4&4\\ 2&2&2&2&2&2&2\\ 0&0&0&0&0&0\\ 2&2&1&2&2&1&1\end{pmatrix} \]  
Project 3. \[P_{3}= \begin{pmatrix}4&4&4&4&4&4&4\\ 2&2&2&2&2&2\\ 0&0&0&0&0&0\\ 2&2&1&2&2&1&1\end{pmatrix} \]  
Step 2. Obtain the weighted standardized decision matrix. Project 1 standardized the decision matrixes as follows. \[R_{1}= \begin{pmatrix}0.1016&0.08128&0.05776&0.26&0.13528&0.12192&0.06248\\ 0.0508&0.04064&0.02888&0.13&0.06764&0.06096&0.03124\\ 0&0&0&0&0&0&0\\ 0.0762&0.1016&0.0722&0.325&0.1691&0.1524&0.0781\end{pmatrix} \]  
Project 2 standardized the decision matrixes as follows. \[R_{2}= \begin{pmatrix}0.1016&0.1016&0.0722&0.325&0.1691&0.1524&0.0781\\ 0.0508&0.0508&0.0361&0.1625&0.08455&0.0762&0.03905\\ 0&0&0&0&0&0&0\\ 0.0508&0.0508&0.01805&0.1625&0.08455&0.0381&0.019525\end{pmatrix} \]  
Project 3 standardized the decision matrixes as follows. \[R_{3}=