Structural assessment of asphalt pavement condition using backcalculated modulus and field data

This study aims to assess asphalt pavement condition in terms of backcalculated moduli of asphalt concrete (AC) and unbound material layers. An integrated Artificial Neural Network - Genetic Algorithm (ANN-GA) program was developed to backcalculate layer moduli using pavement surface deflection measured under falling weight deflectometer (FWD) test. Three pavement sections with FWD testing conducted at different time periods were used in the analysis. As damage developed in AC layer over years, the master curve of AC dynamic moduli showed the similar sigmoidal shapes but the modulus decreases as compared to the undamaged AC. The relatively greater variation of modulus degradation was observed for dynamic modulus in the high frequency range. On the other hand, the nonlinearity parameters obtained from backcalculation can be used to characterize modulus distribution at different depths in granular base layer and subgrade. The slight modulus degradation was observed in granular base layer and subgrade. The deterioration trend of AC modulus was found consistent with fatigue cracking measured at pavement surface based on field distress survey results. The study results indicate that FWD testing with advanced analysis can be successfully used to evaluate in situ pavement condition from structural point of view. (C) 2019 Elsevier Ltd. All rights reserved.