@article{Zhang2024, 
author = {Jun Zhang and Wentao Jiang and Qiuqin Yu and Youxin Li and Tingyi Luo},
title = {Identification of Cement Pavement Defects Based on GPR Time-Frequency Features},
year = {2024},
journal = {Chinese Journal of Underground Space and Engineering},
volume = {20},
number = {4},
pages = {1334-1344},
keywords = {road engineering, cement pavement, NDT, void recognition, GPR},
url = {https://www.sciopen.com/article/10.20174/j.JUSE.2024.04.27},
doi = {10.20174/j.JUSE.2024.04.27},
abstract = {The occurrence of void defects in cement pavement is inevitable and poses a threat to the structural safety of roads. It is urgently necessary to establish an accurate positioning method for void defect areas. Ground-penetrating radar (GPR) was used as a non-destructive testing tool to extract time-frequency features from GPR signals and construct a feature dataset for void defects through forward and inverse experiments. The feature set was then subjected to PCA dimensionality reduction, and SVM and ANN models were respectively established with PCA principal components as input and normal / void output to identify voids. The window energy method was proposed to determine the depth of the void area in the GPR signal of the void. The identification of voids was verified on actual pavement surfaces. The results showed that time-frequency features could characterize void defects, and the subset of data containing parts of the defects was more suitable for modeling and identification. The recognition accuracy of ANN was superior to that of SVM, and the window energy method could effectively locate the depth of the void area and had robustness against noise. In the indoor model, the average positioning error was 1.51%. The research results provide a scientific basis for accurate maintenance of road surfaces.}
}