Field Characterization of Pavement Materials using Falling Weight Deflectometer and Sensor Data from an Instrumented Pavement Section

Abstract

This study deals with the backcalculation of the mechanical properties of pavement layers using not only the falling weight deflectometer (FWD) sensor data but also pavement response under that FWD load from the embedded sensors in an instrumentation section. To perform the backcalculation, a layered viscoelastic pavement model incorporating asphalt concrete (AC) cross-anisotropy is developed as the forward model. Field degree of cross-anisotropy in AC is determined at the maximum magnitude frequency obtained through continuous wavelet transform of the material response signal. The material response signal is obtained from the deconvolution between the loading signal and the signal registered at the embedded sensors. An inverse analysis methodology is also developed to calculate the gross vehicle weight (GVW) of any vehicle passing through the instrumentation section using only the backcalculated material properties and pavement responses. From the results, it is observed that inclusion of the AC cross-anisotropy reduces the error norm, and a good agreement is observed with the laboratory dynamic modulus in both horizontal and vertical directions. It is also observed that the maximum magnitude frequency of material response and degree of cross-anisotropy in AC both decrease with an increase in average AC temperature. Furthermore, using the backcalculated material properties and pavement responses, it is possible to determine the GVW with 95% accuracy.