Research on the Influential Factors of Lining Detection by Non-Contact GPR Vehicle-Based Detection System
(1 Geotechnical Research Institute, China Institute of Water Resources and Hydropower Research, Beijing 100038; 2 School of Civil Engineering, Beijing Jiaotong University, Beijing 100044; 3 The Testing Institute of Base-Structure of CARS, Beijing 100044)
Abstract：
Based on numerical modeling and field testing, this paper discusses several key technical problems of Ground Penetrating Radar (GPR) regarding the change from traditional contact detection technology to non-contact multi-channel detection technology, especially the influences of detection distance, detection speed and antenna selection of non-contact GPR technology on detection results. The following can be observed from the results: 1) 200 mm is the best detection distance, from which the geological radar signal can form an entire clear radar graph to identify lining quality defects by employing horizontal high-pass filtering, eliminating background and vertical filtering gain. From the radar graph, a tunnel lining defect can be accurately identified, with a lining thickness detection precision of ±3 cm; 2) with a detection speed of 20 km/h, the radar graph is clear and the vehicle-based system performs well in both defect identification and detection efficiency; 3) For defect identification at the bottom of the tunnel, 400 MHz horn antenna can obtain a clearer hierarchy and defect image than the 400 MHz shielded antenna; 4)Most defects can be identified by the GPR graph by applying combined 400 MHz+900 MHZ antennas using mutual rectification. Employing antennas of different frequencies is effective to improving the efficiency and precision of the vehicle-based detection system. This research provides an important technical reference for the development of the non-contact GPR vehicle-based detection system in China.
CAO Rui-Lang- 1 Qi-Fa-Lin- 2,
3 He-Shao-Hui- 2
.Research on the Influential Factors of Lining Detection by Non-Contact GPR Vehicle-Based Detection System[J] MODERN TUNNELLING TECHNOLOGY, 2016,V53(5): 17-24
2016, Vol. 53 