## Case Study
### Results of 3D Radar Detection  
To validate the results obtained from the normal cloud model, 3D ground-penetrating radar (GPR) detection was conducted on the reverse heavy traffic lanes of sections K68-K69 and K69-K70. GPR is a well-established nondestructive technique that has seen increasing usage for pavement subsurface investigation to supplement visual condition surveys.  
A Mala ProEx 600 MHz shielded antenna system was employed for data collection. This frequency facilitated the necessary 1-1.2 m penetration depth required to evaluate key structural layers like the asphalt-base interface and aggregate base essential for reconstruction decision-making. Continuous profiling was performed at 14 channels spaced 10.5 cm along wheel-paths and between using an integrated positioning console. This configuration ensured collection of high-resolution 3D condition imagery suitable for detailed structural analysis. The 3D radar detection and processing software utilized in this study were provided by Chengdu Guimu Robot Co., Ltd. Chengdu Guimu Robot Co., Ltd. is a supplier of pavement non-destructive testing equipment located in Chengdu, China.  
The 3D radar detection results were assessed based on the impact area, with the calculation method for the impact area being the sum of the areas affected by looseness, sub-grade settlement, and cracking. The calculation method for the cracking impact area involved multiplying the cracking length by a width of 0.4 m. All areas represented horizontally projected areas. The data presented in Table 6 were automatically computed using the software provided by Chengdu Guimu Robot Co., Ltd.  
Results showed that the reverse heavy traffic lane of section K69-K69 exhibited various types of structural issues, including looseness, cracking, and settlement, whereas there was only one type of issue in the reverse heavy traffic lane of section K69-K70. However, in terms of impact area, the total area affected by internal structural issues in the reverse heavy traffic lane of section K69-K70 was 1678 m\({}^{2}\), while it was 1326 m\({}^{2}\) in the reverse heavy traffic lane of section K68-K69. The total area affected by internal issues in the reverse heavy traffic lane of section K69-K70 was 126.5% of that in the reverse heavy traffic lane of section K68-K69. Therefore, based on the results of the 3D radar detection, the performance rating of the reverse heavy traffic lane in section K68-K69 was better than that of the reverse heavy traffic lane in section K69-K70.  
In summary, these subsurface conditions validated the normal cloud model evaluation of the reverse lane in section K69-K70 as \"good\" versus the PQI assessment of \"excellent\". GPR objectively verifies increased subsurface impact warranting a lower rating than assigned previously. This reinforces the normal cloud approach's sensitivity in differentiating conditions where conventional methods may overlook subtle deterioration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stake**} & \multirow{2}{*}{**Layer**} & **Looseness** & **Cracking** & **Settlement** & **Total Area** \\  & & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** \\ \hline \multirow{3}{*}{K68–K69} & Base layer & 1224.6 & 0.0 & / & \\  & Subbase layer & 32.2 & 46.4 & / & 1326.0 \\ \cline{1-1}  & Subgrade & 0.0 & / & 22.9 & \\ \hline \multirow{3}{*}{K69–K70} & Base layer & 1667.7 & 0.0 & / & \\  & Subbase layer & 10.7 & 0.0 & / & 1678.0 \\ \cline{1-1}  & Subgrade & 1.5 & / & 0.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: 3D radar detection results (reverse heavy traffic lane).