# Highway Tunnel Defect Detection Based on Mobile GPR Scanning
## 6 Conclusions  
This paper firstly develops the hardware system of the tunnel inspection vehicle and studies the hardware parameter selection and parameter calculation method of the image fast acquisition system. Then, images of cracks in the tunnel lining are analyzed, and practical tests are carried out at the tunnel site. Finally, a comparison is also made with pure detection. The main conclusions are as follows:  
1. The device has horizontal rotation, vertical rotation, length expansion, and antenna angle rotation functions. In the horizontal direction, it has a \(-90^{\circ}\)\(\sim\)\(90^{\circ}\) range, a vertical rotation range from 0 to \(90^{\circ}\), a length range from 5-9.5 m, and an antenna rotation between \(-40^{\circ}\)\(\sim\)\(40^{\circ}\). It can be applied to detect tunnels with different section shapes combined with mobile vehicles.
2. Reflections and multiple reflections in the air between the surface of the tunnel structure and the antenna can cause serious interference. The shaking of the vehicle as it moves forward can also cause disturbances. Recording the distance change during the scan with a rangefinder eliminates interference due to shaking. Regular multiple ripples can be removed through gain, filtering, and background elimination. The BBP algorithm can suppress interference caused by deformed steel skeletons and defects.
3. Through the method proposed in this paper, quantitative parameters such as the spacing of the reinforcement bars, the thickness of the second lining, and the location of the disease in the tunnel structure can be obtained.
4. The method proposed in this paper improves the working environment of highway tunnel structure detection, improves the operation speed, reduces the risk in operation, and has considerable economic benefits compared with traditional methods.  
Conceptualization, Q.W. and X.X.; methodology, H.W.; software, M.Z.; validation, J.Z. and H.W.; resources, J.Z.; data curation, D.Y. and W.Z.; writing--original draft preparation, J.Z.; writing--review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Traditional** & **Proposed Method** \\ \hline efficiency & 0.5 d/km & 12 min/km \\ Impact on traffic & Close the road & No impact \\ Cost & 30,000–40,000 Yuan/km & \textless{}10,000 Yuan/km \\ Safety & Fall risk and traffic risk & Safe \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of benefits.  
**Funding:** This research was funded by the National Key R&D Program of China under grants 2019 YFC0605100, 2019 YFC0605103, National Nature Science Funds of China under grants 52038008, 42107216 and 4210071391, the Key Science and Technology Projects in Transportation Industry by Ministry of Transport of the People's Republic of China in 2021(Grant No. 2021-MS2-061), the Project of Science and Technology Program of Department of Transport (Grant No.2021014) and the Independent Scientific Research Project of Zhejiang Scientific Research Institute of Transport (Grant No. ZK202205 and ZK202206).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Thanks to the Bureau of Transportation of Lishui for providing the tunnels for detection.  
**Conflicts of Interest:** The authors declare no conflict of interest.