## Case Study
### Evaluation Results of Normal Cloud Model  
Based on the calculation steps outlined in Section 4 and the data presented in Table 3, the calculation results of the combination weighting-normal cloud model can be derived, as illustrated in Table 5. Membership degrees at each performance level (\"Excellent\", \"Good\", etc.) were calculated using Equation (20) to determine comprehensive membership values. Table 5 shows the membership degrees and resultant model/PQI ratings. Several notable aspects emerged:  
1. Sections rated \"Good\" by PQI (forward lanes K67-K68 and K68-K69) exhibited higher membership in the \"Good\" class using the cloud model (0.519, 0.500). 2. Section K69-K70 reverse lane demonstrated higher membership in \"Good\" (0.668) versus \"Excellent\" (0.093). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Stake**} & \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Lane**} & \multicolumn{6}{c}{**Sub-Indicators**} & \multirow{2}{*}{**PQI**} & \multirow{2}{*}{**Performance**} \\ \cline{5-10}  & & & & **PCI** & & & **RQI** & & **RBI** & & **PSSI** & & **Rating** \\ \hline
6 & K69–K70 & Forward & Heavy &
\begin{tabular}{c} Heavy \\ traffic \\ traffic \\ traffic \\ traffic \\ traffic \\ traffic \\ traffic \\ \end{tabular} & 82.49 & 94.29 & 91.16 & 100.00 & 96.27 & 99.98 & 90.46 & Excellent \\
7 & K67–K68 & Reverse &
\begin{tabular}{c} Light \\ traffic \\ traffic \\ traffic \\ traffic \\ \end{tabular} & 90.90 & 94.80 & 97.41 & 100.00 & 96.18 & 99.73 & 94.48 & Excellent \\
8 & K68–K69 & Reverse &
\begin{tabular}{c} Light \\ traffic \\ traffic \\ traffic \\ \end{tabular} & 92.58 & 94.92 & 97.65 & 100.00 & 95.65 & 99.82 & 95.09 & Excellent \\
9 & K69–K70 & Reverse &
\begin{tabular}{c} Light \\ traffic \\ traffic \\ \end{tabular} & 93.96 & 95.23 & 98.00 & 100.00 & 94.76 & 99.88 & 95.63 & Excellent \\
10 & K67–K68 & Reverse &
\begin{tabular}{c} Heavy \\ traffic \\ traffic \\ \end{tabular} & 90.18 & 92.81 & 95.09 & 75.00 & 95.93 & 99.66 & 90.76 & Excellent \\
11 & K68–K69 & Reverse &
\begin{tabular}{c} Heavy \\ traffic \\ traffic \\ \end{tabular} & 91.16 & 94.50 & 94.09 & 100.00 & 97.57 & 99.81 & 94.13 & Excellent \\
12 & K69–K70 & Reverse &
\begin{tabular}{c} Heavy \\ traffic \\ traffic \\ \end{tabular} & 85.50 & 93.82 & 95.91 & 100.00 & 95.11 & 99.89 & 91.97 & Excellent \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Membership Degree of Inspection Values in Performance Ratings**} & \multicolumn{2}{c}{**Normal**} \\ \cline{2-7}  & **Excellent** & **Good** & **Average** & **Poor** & **Failed** & **Cloud Model** & **PQI Model** \\ \hline
1 & 0.798 & 0.374 & 0.042 & 0.028 & 0.002 & Excellent & Excellent \\
2 & 0.815 & 0.373 & 0.041 & 0.028 & 0.077 & Excellent & Excellent \\
3 & 0.646 & 0.399 & 0.047 & 0.029 & 0.002 & Excellent & Excellent \\
4 & 0.275 & 0.519 & 0.062 & 0.032 & 0.004 & Good & Good \\
5 & 0.307 & 0.500 & 0.059 & 0.031 & 0.002 & Good & Good \\
6 & 0.449 & 0.445 & 0.052 & 0.030 & 0.002 & Excellent & Excellent \\
7 & 0.970 & 0.254 & 0.009 & 0.020 & 0.001 & Excellent & Excellent \\
8 & 0.640 & 0.265 & 0.033 & 0.024 & 0.002 & Excellent & Excellent \\
9 & 0.734 & 0.334 & 0.037 & 0.027 & 0.002 & Excellent & Excellent \\
10 & 0.726 & 0.283 & 0.034 & 0.025 & 0.002 & Excellent & Excellent \\
11 & 0.660 & 0.269 & 0.033 & 0.025 & 0.002 & Excellent & Excellent \\
12 & 0.093 & 0.668 & 0.080 & 0.037 & 0.003 & Good & Excellent \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results of normal cloud model. Other sections generally aligned between the two approaches, with membership heavily favoring the identical rating. These results demonstrate the cloud model's ability to more sensitively represent indicator fuzziness and randomness, generating intermediate membership degrees where conventional methods deliver absolute ratings.