Curvelet transform‐based identification of void diseases in ballastless track by ground‐penetrating radar

Summary Void disease caused by construction quality and external loads contributes to the failure of reinforced concrete (RC) structures. Ground‐penetrating radars have been used for various applications to detect void diseases beneath RC. However, interferences, such as strong reinforcement bar (rebar) reflection and multiple waves, complicate the identification of internal void diseases in RC. On the basis of these problems, we consider the ballastless track of high‐speed railways as a research object and propose a void‐disease identification algorithm based on the directional features of target echoes. First, typical target echo models of rebar and void in RC were built, and the principal directional features of two kinds of target echoes were discussed. And, then, on the basis of differences between rebar and void echoes in direction, a horizontal filter was constructed to identify the void diseases. Finally, the reliability and robustness of the proposed algorithm were verified through forward simulation and field experiments. Results show that horizontal filters can effectively eliminate principal components in the asymptote direction of rebar echoes, reserve principal components in the horizontal direction of void echoes, and implement void disease detection and identification. This study promotes the application of ground‐penetrating radars to detect ballastless track subgrade diseases in high‐speed railways.