  Understanding stress-strain behavior of asphalt pavement under repetitive
traffic loading is of critical importance to predict pavement performance and
service life. For viscoelastic materials, the stress-strain relationship can be
represented by the dynamic modulus. The dynamic modulus test in indirect
tension mode can be used to measure the modulus of each specific layer of
asphalt pavements using representative samples. Dynamic modulus is a function
of material properties, loading, and environmental conditions. Developing
predictive models for dynamic modulus is efficient and cost effective. This
article focuses on developing an accurate Finite Element (FE) model using
mixture elastic modulus and asphalt binder properties to predict dynamic
modulus of asphalt mix in indirect tension mode. An Artificial Neural Network
(ANN) is used to back-calculate the elastic modulus of asphalt mixtures. The
developed FE model was verified against experimental results of field cores
from nine different pavement sections from five districts in the State of
Minnesota. It is demonstrated that the ANN modeling is a powerful tool to
back-calculate the elastic modulus and FE model is capable of accurately
predicting dynamic modulus.
