Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

Abstract The dimension of the void area in pavement is crucial to its structural safety. However, there is no effective method to measure its geometric parameters. To address this issue, a void size extraction algorithm based on the continuous wavelet transform (CWT) method was proposed using ground-penetrating radar (GPR) signal. Firstly, the Finite-Difference Time-Domain (FDTD) method was used to investigate void areas with different shapes, sizes, and depths. Next, the GPR signal was processed using the CWT method, and a 3D image of the CWT result was used to visualize the void area. Based on the differences between the void and normal pavement in the time and frequency domains, the signal with maximum energy at the CWT time-frequency result was extracted and combined to reconstruct the B-scan image, where void areas have energy concentration phenomenon, which represent the location of the void area. And width and depth of void detection algorithm was proposed to recognize the energy concentration area. Finally, the detection algorithm was verified both in numerical model and physical lab model. The results indicated that the CWT time-frequency energy spectrum can be used to enhance the void feature, and the 3D CWT image can clearly visualize the void area with a highlighted energy area. After fully testing and validating in numerical and lab models, our proposed method achieved high accuracy for void width and depth extraction, providing a precise method for estimating void dimension in pavement. This method can guide DOT departments to carry out pre-maintenance, thereby ensuring pavement safety.