The Tunnel Quality Detection Based On Ground Penetrating Radar

In recent years, with the fast development of Chinese highway network, highway tunnel has been applied widely in highway network construction for shortening travel distance and avoiding large ramp. However, usually, the characteristics of tunnel construction include a wide range of subjects, a long construction period and high difficulty in the tunnel construction. Many quality problems which bring security risks to the tunnel traffic and decrease service life are exposed in tunnel operation such as lining leakage, lining erosion and crack, etc.The GPR-based tunnel quality detection technology, contains many advantages: non-destructive detection, high resolution (cm-level), high efficiency and strong anti-interference capability, etc. It can be determined that whether the tunnel lining layer thickness, the compactness as well as other quality problems reaches the designed index or not and repair it timely after discovering problems to guarantee the overall quality and exclude the risk for the tunnel operation ultimately through the GPR technology for highway tunnel quality inspection. The study of the GPR-based tunnel quality detection technology in this paper is around the following contents:Firstly, it introduces the basic principles of the GPR tunnel quality detection. The basic GPR principle, the detection equipment to be adopted, the detection basis, frequency and testing line layout for general detection content, the radar parameters setting standard, the procedures of detection and data processing will be described. Afterwards, selecting corresponding parameters of GPR based on these principles and criteria, radar data of the tunnel secondary lining somewhere in Nanchang will be collected and processed, and then the quality of the tunnel will be evaluated.Secondly, it estimates the quantity of rebar used in the tunnel lining. it primarily uses the symmetry algorithm to estimate the quantity, locate the vertexes in hyperbolic curves reflected by the rebar and then optimize them by genetic algorithm. The rebar quantity can be estimated via counting the number of minimal value in the symmetric function at last. Thirdly, it calculates the rebar diameter inversion. Simulating the GPR-based tunnel detection by means of the GPRmax2.0software will be processed at beginning and next extracting the radar echo amplitudes in the rebar vertexes, the rebar depth in the tunnel lining layer and its corresponding diameter as the characteristics to finish the inversion calculation finally by BP neutral network.