Historically, asphalt mixtures in Minnesota have been produced with fine gradations. However, recently more coarse-graded mixtures are being produced as they require less asphalt binder. Thus, it is important that pavement performance for coarse gradations be evaluated. It is of critical importance to obtain the dynamic modulus of asphalt pavements under repetitive traffic loading to predict its performance and service life. The indirect tension mode can measure the dynamic modulus of each layer of field cores without the dimensional requirement, e.g. a height of 6-inch is required for the traditional uniaxial test mode. Within this research work, performance evaluation took place with the use of the Dynamic Modulus Test in Indirect Tension mode on coarse-graded mixtures consisting of field cores from 9 different pavements located in five districts of Minnesota. From each pavement’s surface layer, 3 specimens were tested at three temperatures; 0.4à °C, 17.1à °C, and 33.8à °C each at nine frequencies ranging between 0.1 Hz and 25 Hz. Additional volumetric characterization of the field mixtures was done to determine asphalt content, air voids, and blended aggregate gradations. Asphalt binders were extracted and recovered for use in determining binder shear complex master curves. Through this information the modified Witczak model was used to create │E*│ master curves which were then compared against the indirect tension (IDT) test │E*│ experimentally created master curves. According to the results the Modified Witczak Model needs to be modified for IDT collected dynamic modulus data.

Another focus of this research is developing an accurate finite element (FE) model using mixture elastic modulus and asphalt binder properties to predict dynamic modulus of asphalt concrete mix in indirect tension mode. An Artificial Neural Network 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 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.