Viscoelastic Genetic Algorithm for Inverse Analysis of Asphalt Layer Properties from Falling Weight Deflections

The falling weight deflectometer (FWD) is a nondestructive test whose results are typically used for backcalculating in situ layer properties of pavements. Most backcalculation methods assume the pavement to be a layered elastic half-space. However, asphalt pavements behave more like multilayered viscoelastic systems, especially in response to small or short-duration load applications. Hence, although elastic analysis is computationally efficient and well accepted in the engineering community, the theory cannot produce the viscoelastic properties of the asphalt concrete (AC) layer. In this study, a new inverse analysis method is proposed to backcalculate both linear elastic and viscoelastic properties of pavement layers as well as the AC time–temperature shift factor. In this method, the FWD load–response history of a single FWD drop and variation in temperature along the depth of the AC layer during the drop are used for performing the computations. The underlying (viscoelastic) forward solver is approximate and disregards dynamic effects; these factors, in return, make it computationally efficient. A genetic algorithm–based optimization scheme is offered to search for the pavement properties. As an example, two sections from the long-term pavement performance study were selected for investigation. The back-calculation results were positive, which indicates that unless a stiff layer exists close to the surface, it should be possible to infer linear viscoelastic properties from a single FWD drop.