## 4 Case Study
### Field Test Comparison Test of Asphalt Pavement
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Point Number** & **MTD** & **MTD\({}^{\prime}\)** & **REL (\%)** \\ \hline
1 & 1.74 & 1.80 & 3.33\% \\
2 & 1.92 & 1.96 & 2.04\% \\
3 & 1.83 & 1.89 & 3.17\% \\
4 & 1.78 & 1.73 & 2.89\% \\
5 & 1.9 & 1.93 & 1.55\% \\
6 & 1.72 & 1.67 & 2.99\% \\
7 & 1.74 & 1.69 & 2.96\% \\
8 & 2.00 & 2.05 & 2.44\% \\
9 & 1.95 & 1.90 & 2.63\% \\
10 & 1.91 & 1.96 & 2.55\% \\
11 & 1.68 & 1.75 & 4.00\% \\
12 & 1.75 & 1.68 & 4.17\% \\
13 & 1.57 & 1.66 & 5.42\% \\
14 & 1.66 & 1.63 & 1.84\% \\
15 & 1.83 & 1.79 & 2.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of texture reconstruction results for semi-open graded asphalt pavement. demonstrating that the proposed CNN model achieves high precision and reliability in texture depth reconstruction for asphalt pavements. As shown in Table 6, the maximum REL for the MTD field measurements was 5.15%, and the RMSE was 6.9%. At thresholds of 1, 2, and 3, the detection accuracies reached 92.76%, 95.02%, and 97.86%, respectively. While the results met the detection requirements, the errors were higher compared to those of dense-graded and semi-open-graded asphalt pavements. In field measurements, image acquisition requires the camera to remain perpendicular to the inspection area. Unlike controlled laboratory environments, road surfaces in practical engineering often exhibit slight inclinations. While image preprocessing and stereo rectification techniques effectively mitigate the impact of these inclinations on detection results, residual errors persist. Consequently, depth detection values derived from the 3D pavement model are slightly higher than those obtained via the sand patch test, as illustrated in Figure 14.  
To visually demonstrate the macrotexture reconstruction capability of the proposed CNN model, Figures 15-17 present the reconstructed results for dense-graded, semi-open-graded, and open-graded asphalt pavements. The results indicate that the CNN model can effectively reconstruct the macrotexture of real-world asphalt pavements, with reconstruction accuracy  
must be corrected using Equation (24) to derive standardized values (\(F_{B20}\)) at the reference temperature of 20 \({}^{\circ}\)C [30]:  
\[F_{B20}=F_{BT}+\Delta F \tag{24}\]  
where \(F_{B20}\): \(BPN\) converted to standard temperature of 20 \({}^{\circ}\)C, \(F_{BT}\): Actual BPN measured at pavement surface temperature \(T\), \(T\): Measured pavement surface temperature (\({}^{\circ}\)C) under wet conditions, \(\Delta F\): Temperature correction factor (selected from Table 7.)  
Figure 16: 3D Reconstruction of Macrotexture for Semi-Open-Graded Asphalt Pavement. Figure 15: 3D Reconstruction of Macrotexture for Dense-Graded Asphalt Pavement. The British Pendulum Tester was utilized to conduct skid resistance measurements across all 50 predetermined test sections. Field testing was performed at an ambient pavement temperature of 10 \({}^{\circ}\)C, with the on-site measurement conditions visually documented in Figure 18. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Observed Temperature** & **Temperature Correction** & **Observed Temperature** & **Temperature Correction** \\
**T (\({}^{\circ}\)C)** & **\(\Delta F\) (\({}^{\circ}\)C)** & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) \\ \hline
0 & \(-6\) & 25 & +2 \\
5 & \(-4\) & 30 & +3 \\
10 & \(-3\) & 35 & +4 \\
15 & \(-1\) & 40 & +7 \\
20 & 0 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Temperature correction value. Figure 17: 3D Reconstruction of Macrotexture for Open-Graded Asphalt Pavement. Figure 18: Pendulum friction instrument on-site detection. The British Pendulum Test results for pavement surface friction are presented in Table 8.