## 4 Conclusions  
This paper proposes a 3D reconstruction method for road structural defects by combining the BP imaging algorithm with GPR detection data. The method was validated \begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Defect Type** & **Initial Position** & **Termination Position** & **Initial Depth (m)** & **Termination Depth (m)** & **Size (m)** \\ \hline
1 & Pipeline & K62 + 96.70 & K62 + 94.53 & 0.47 & 1.21 & 2.17 \(\times\) 0.74 \\
2 & Looseness & K62 + 76.60 & K62 + 74.55 & 0.47 & 0.57 & 2.05 \(\times\) 0.10 \\
3 & Cavity & K62 + 71.66 & K62 + 66.56 & 0.55 & 0.64 & 5.10 \(\times\) 0.09 \\
4 & Crack & K62 + 31.99 & K62 + 31.08 & 0.42 & 0.45 & 0.91 \(\times\) 0.03 \\
5 & Pipeline & K62 + 12.10 & K62 + 10.57 & 0.45 & 1.03 & 1.53 \(\times\) 0.59 \\
6 & Pipeline & K62 + 9.46 & K62 + 8.96 & 0.43 & 1.19 & 0.50 \(\times\) 0.76 \\
9 & Cavity & K61 + 990.09 & K61 + 988.65 & 0.48 & 0.86 & 1.44 \(\times\) 0.38 \\
10 & Cavity & K61 + 987.62 & K61 + 986.82 & 0.65 & 0.91 & 0.80 \(\times\) 0.26 \\
11 & Pipeline & K61 + 977.32 & K61 + 976.78 & 0.10 & 1.47 & 0.54 \(\times\) 1.37 \\
12 & Crack & K61 + 951.53 & K61 + 949.85 & 0.60 & 0.82 & 1.68 \(\times\) 0.02 \\
13 & Cavity & K61 + 943.29 & K61 + 941.57 & 0.54 & 0.82 & 1.72 \(\times\) 0.27 \\
14 & Looseness & K61 + 935.49 & K61 + 932.42 & 0.45 & 0.95 & 3.07 \(\times\) 0.50 \\
15 & Cavity & K61 + 930.00 & K61 + 925.42 & 0.43 & 0.64 & 4.58 \(\times\) 0.21 \\
16 & Cavity & K61 + 921.64 & K61 + 919.06 & 0.40 & 0.94 & 2.58 \(\times\) 0.54 \\
17 & Cavity & K61 + 918.52 & K61 + 915.40 & 0.40 & 0.59 & 3.12 \(\times\) 0.19 \\
18 & Looseness & K61 + 873.15 & K61 + 870.80 & 0.43 & 1.03 & 2.35 \(\times\) 0.60 \\
19 & Cavity & K61 + 813.11 & K61 + 810.35 & 0.77 & 0.97 & 2.76 \(\times\) 0.19 \\
20 & Crack & K61 + 802.86 & K61 + 802.83 & 0.38 & 1.00 & 0.03 \(\times\) 0.61 \\ \hline \end{tabular}
\end{table} 
Table 7: Three-dimensional reconstruction results of the second lane on the right. Figure 16: Result of 3D reconstruction on the actual road. Figure 17: Core sample. using both gprMax simulations and laboratory test models. The main conclusions were as follows:  
1. Through the gprMax simulation images, it was observed that the hyperbolic reflection feature was the most prominent at the central position of the defect and gradually weakened toward the edges. 2. The BP algorithm effectively facilitated the accurate resetting of radar waves, which ensured that the reflected signal was concentrated at the actual location of the defect. 3. For the gprMax model, the method proposed in this paper could accurately realize the 3D reconstruction of the road structural defects, such as cavities, cracks, and interfacial failures, and achieved an _IoU_ of approximately 0.6. Additionally, the larger the size of the defects and the closer they were to the road surface, the higher the accuracy of the reconstruction results. For the laboratory test model, the accuracy of the 3D reconstruction was reduced due to the complex media composition and significant interference from environmental noise. Nevertheless, the average _IoU_ was around 0.52, which is still acceptable. 4. Combined with the actual road-drilling coring results, it could be verified that the algorithm could detect and reconstruct the road structural defects more accurately. The results of this work hold significant implications for the detection and maintenance of road structural defects. However, a notable limitation of this study was the simplification of defects into regular geometric shapes. Additionally, in the gprMax model, the materials of each structural layer were assumed to be uniform, with no consideration of other substances. The accurate detection of road structural defects continues to pose challenges. Future research should aim to acquire more measured data and develop more precise models to enhance the reliability of defect detection. The authors confirm the contributions to this paper as follows: study conception and design: [PERSON] and Z.L.