## 4 Case Study  
### Field Test Comparison Test of Asphalt Pavement  
To systematically evaluate the reconstruction performance of the proposed CNN model for asphalt pavements with different gradation types, four typical main roads in Zhangdian District, Zibo (Liuquan Road, Xincun West Road, West Second Road, and Gongqinguan Road) were selected as the study subjects. These roads encompass three types of asphalt pavement structure: dense-graded (0.5-1.5 mm), semi-open-graded (1.5-2 mm), and open-graded (2-3.0 mm). Based on the collected pavement image data, a three-dimensional reconstruction of asphalt surface texture was performed, and the results were compared with sand patch test measurements to comprehensively validate the applicability and reliability of the model. A 500-m section on each of the three roads was selected as the analysis area for comparing the reconstructed results with measured values. Measurement points were arranged at 10-m intervals along the driving trajectory, resulting in a total of 50 measurement points per section. High-resolution industrial-grade cameras were employed for image acquisition to ensure exceptional clarity and detailed representation. The captured images were uniformly cropped to a resolution of 208 \(\times\) 144 pixels, a size selected to retain sufficient detail while effectively managing data volume, thereby mitigating excessive computational overhead. The preprocessed images were input into the proposed CNN model for three-dimensional reconstruction. The reconstructed results were then compared with the Mean Texture Depth measured via the sand patch test, and the reconstruction outcomes for different gradation types are summarized in Tables 4-6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Point** & **MTD** & **MTD\({}^{\prime}\)** & **REL (\%)** \\ \hline
1 & 1.45 & 1.38 & 5.07\% \\
2 & 1.38 & 1.44 & 4.17\% \\
3 & 1.19 & 1.23 & 3.25\% \\
4 & 1.30 & 1.35 & 3.70\% \\
5 & 1.46 & 1.39 & 5.04\% \\
6 & 1.33 & 1.26 & 5.56\% \\
7 & 1.12 & 1.18 & 5.08\% \\
8 & 1.36 & 1.32 & 3.03\% \\
9 & 1.44 & 1.50 & 4.00\% \\
10 & 1.33 & 1.41 & 5.67\% \\
11 & 1.12 & 1.16 & 3.45\% \\
12 & 1.49 & 1.53 & 2.61\% \\
13 & 1.30 & 1.33 & 2.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of reconstructed texture results for dense asphalt pavement. According to the test results in Table 4, the Relative Error (REL) of the MTD field measurements was 7.64%, and the RMSE was 5.8%. At thresholds of 1, 2, and 3, the detection accuracies reached 93.5%, 95.3%, and 98.1%, respectively. Both the error and accuracy of the MTD\({}^{\prime}\) and MTD detection results met the expected requirements, demonstrating that the proposed CNN model achieves high accuracy and reliability in reconstructing the texture of dense-graded asphalt pavements. According to the test results in Table 5, the maximum REL of the Mean Texture Depth field measurements was 5.42%, and the RMSE was 5.5%. At thresholds of 1, 2, and 3, the detection accuracies reached 94.03%, 95.97%, and 98.89%, respectively. Both the error and accuracy of the MTD\({}^{\prime}\) and MTD detection results met the expected requirements, demonstrating that the proposed CNN model achieves high accuracy and reliability in reconstructing the texture of dense-graded asphalt pavements. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Point** & **MTD** & **MTD\({}^{\prime}\)** & **REL (\%)** \\ \hline
1 & 2.04 & 1.94 & 5.15\% \\
2 & 2.14 & 2.24 & \(-\)4.46\% \\
3 & 2.36 & 2.27 & 3.96\% \\  
\hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of reconstructed texture results for open-graded asphalt pavement.