A multiscale model for predicting the viscoelastic properties of asphalt concrete Academic Article uri icon

abstract

  • © 2016, Springer Science+Business Media Dordrecht. It is well known that the accurate prediction of long term performance of asphalt concrete pavement requires modeling to account for viscoelasticity within the mastic. However, accounting for viscoelasticity can be costly when the material properties are measured at the scale of asphalt concrete. This is due to the fact that the material testing protocols must be performed recursively for each mixture considered for use in the final design. In this paper, a four level multiscale computational micromechanics methodology is utilized to determine the accuracy of micromechanics versus directly measured viscoelastic properties of asphalt concrete pavement. This is accomplished by first measuring the viscoelastic dynamic modulus of asphalt binder, as well as the elastic properties of the constituents, and this comprised the first scale analysis. In the second scale analysis, the finite element method is utilized to predict the effect of mineral fillers on the dynamic modulus. In the third scale analysis, the finite element method is again utilized to predict the effect of fine aggregates on the dynamic modulus. In the fourth and final scale analysis, the finite element method is utilized to predict the effect of large aggregates on the dynamic modulus of asphalt concrete. This final predicted result is then compared to the experimentally measured dynamic modulus of two different asphalt concretes for various volume fractions of the constituents. Results reveal that the errors in predictions are on the order of 60 %, while the ranking of the mixtures was consistent with experimental results. It should be noted that differences between the “final predicted results” and the experimental results can provide fruitful ground for understanding the effect of interactions not considered in the multiscale approach, most importantly, chemical interactions.

author list (cited authors)

  • Garcia Cucalon, L., Rahmani, E., Little, D. N., & Allen, D. H.

citation count

  • 6

publication date

  • April 2016