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