Deterioration of concrete structures has become a widespread problem with high repair costs. The corrosion of rebar is one of the major causes. GPR has potential for rebar corrosion detection. But the rapid survey generates a large amount of data, which require an automatic approach for effective data processing and information extraction. This paper proposes an automatic process to effectively extract the rebar reflection in the radargram image and estimate the concrete condition above the rebar. The process uses template matching to locate the hyperbola position, image processing to extract hyperbolic region, and algebraic fitting to rapidly estimate hyperbola parameters. The estimated parameters can be used to calculate the wave propagation velocity and relative permittivity in concrete above the rebar, which can be used to further evaluate the concrete condition. The effectiveness of the proposed method is validated using experiment testing.

Ground penetrating radar (GPR) is one of the most extensively used nondestructive evaluation methods with rapid development in civil engineering in the past few years for its efficiency and high resolution. The evaluation mechanism of concrete materials using GPR is generally based on the reflected signals from the rebar. According to rebar reflections, the corrosion level of the rebar and the material deteriorating condition above the rebar can be deduced. GPR has been successfully applied to shallow subsurface rebar detection in concrete structures. However, very few literatures have addressed the detectability of reflected signal from the deeper layer of rebar. As the result, only a small shallow section of the bridge deck can be evaluated by GPR data so far. In this paper, the detectability of the deeper rebar layer in concrete bridge decks using GPR is investigated with the help of data processing and image processing techniques. The GPR data collected from both a simulation model and a test slab are used to demonstrate the proposed methods and the preliminary results show the reflected signals from the second layer of rebar can be extracted using proposed methods.