Isolating Surface Moisture in Ground-Penetrating Radar Signals for Accurate Asphalt Concrete Density Prediction

Abstract

A prevalent application of ground-penetrating radar (GPR) for asphalt concrete (AC) pavements is predicting in-situ density, which is an important pavement quality parameter. Accurate estimation of the dielectric constant is required when using GPR for pavement inspections. Surface moisture is a challenge because it masks the GPR reflection amplitudes. Surface moisture could result from natural rainfall events or from sprayed water by roller compactors during AC construction. This study introduces an efficient method to filter GPR signals from surface moisture effects while maintaining density information intact. Through numerical simulations and laboratory testing data, the surface moisture effect was isolated. A singular value decomposition-based signal correction approach was proposed. Data from six different AC sections were utilized to develop the correction algorithm. Finally, for validation, the developed algorithm was applied to GPR scans collected in two extreme conditions: dry and wet, which were collected on another AC pavement before and after a rain event. Results supported the need for surface moisture correction, especially for AC density monitoring during compaction. After applying the correction, the mean absolute error (MAE) of dielectric constant estimation (used to predict AC density) dropped from 16% to 2.6% and, therefore, the AC density prediction MAE dropped from 8.9% to 1.3%.