Prediction of thin asphalt concrete overlay thickness and density using nonlinear optimization of GPR data

Abstract The processing of ground-penetration radar (GPR) signals collected from thin asphalt concrete (AC) overlay is a challenging task due to the limitation of GPR signal resolution. In this study, a gradient descent based nonlinear optimization approach was developed to analyze GPR signals collected from thin AC overlays to estimate their thickness and density. Both finite difference time domain (FDTD) simulation and field tests were conducted to validate the proposed algorithm. The simulation showed that the accuracy of dielectric constant estimation increased after the nonlinear gradient descent method was applied. This resulted in a thickness estimation error of less than 1 mm. When nonlinear gradient descent was applied to field test measured signals, the average AC overlay thickness prediction and AC density estimation errors were 3 mm and 1.81%, respectively. This study demonstrates that the nonlinear gradient descent is an effective approach for estimating thin AC overlay thickness and density from GPR data.