Cement Pavement Void Identification Based on XGBoost and GPR Time-frequency Features

Abstract:

Ground penetrating radar （GPR） is an effective method of void detection， but GPR data interpretation depends on human experience， being time consuming and laborious， or even existing subjective bias. To address above issues， a cement pavement void identification method based on XGBoost and GPR time-frequency features was proposed. To automatically identify cement pavement void area， the finite difference time domain method， lab and field tests were carried out， and GPR void dataset with label was created. Then， the resampling method was used to obtain the same sample frequency. Thirty time and frequency domain features， including 18 time-domain features and 12 frequency-domain features， were extracted from the post-processed GPR data. Taking the time-frequency domain feature as an input， and void label as an output， XGBoost was used to build a void identification model. The random forest （RF） and artificial neural network （ANN） were also trained to compare with XGBoost. The comparison results indicate that the accuracy ranking is XGBoost（98.10%）>ANN（95.10%）>RF（93.17%）. The accuracy of the XGBoost method is the highest and verified by field tests.