Automatic Localization of Internal Crack in Concrete Structures Based on Hyperbolic Trajectory Extraction From 3-D GPR Data

Concrete structures constitute a critical component of infrastructure, including buildings, bridges, and tunnels. However, the initiation and propagation of internal cracks in concrete structures pose substantial challenges to public safety. Ground penetrating radar (GPR) serves as one of the primary tools for detecting internal cracks. However, the endpoint echoes of cracks acquired by GPR are often susceptible to clutter interference. Researchers typically cannot extract the precise locations of echoes from low signal-to-noise ratio (SNR) images, and conventional clutter suppression methods fail to achieve satisfactory results. Consequently, the inversion of 3-D crack information proves challenging. To address the issue of echo extraction from low-SNR images, this article proposes a method for identifying potential target regions from a 3-D slice perspective. The approach leverages the more comprehensive crack information available in 3-D slices, ensuring that some slices with acceptable SNR levels are included. By selecting a hyperbolic crack region from a high-SNR slice as a template, the method matches potential target regions in other slices with poor SNR. Subsequently, the hyperbolic contours of these regions are extracted, enabling the quantification of 3-D crack information and the reconstruction of crack locations. This method has been successfully validated through both simulation and real experiments. In simulations, for vertical cracks at the depth of 5 and 7 cm, the average errors in vertex horizontal position and relative burial depth were 1.14 and 1.49 cm, respectively. In real experiments, for vertical cracks at the depth of 5 and 7 cm, the average errors of horizontal position and relative buried depth were 1.23 and 0.44 cm, respectively. These results confirm that the proposed algorithm accurately determines the position and morphology of internal cracks, thereby providing robust technical support for the health monitoring of concrete structures.