Asphalt mixture stiffness predictive models

Terhi Pellinen, Adam Zofka, Mihai Marasteanu, Noa Funk

Research output: Contribution to journalConference articlepeer-review

33 Scopus citations


The objective of the study is to evaluate the validity of several recent models used to predict asphalt mixture stiffness from the visco-elastic properties of asphalt binders. More specifically, the models by Di Benedetto et al. were compared to the existing Witczak model, which is implemented in the new pavement design guide, and the Hirsch models. Unlike other prediction models, Di Benedetto considers both binders and mixtures in the same rheological model. The research was conducted using two sets of asphalt mixture dynamic modulus |E*| and asphalt binder complex shear modulus |G*| test data from FHWA-ALF and MnROAD studies. Predictions were made using Rolling Thin Film Oven (RTFO) and Pressure Aging Vessel (PAV) aged binder data. Binder modulus was converted to viscosity using existing empirical conversion equations. The results indicate that all three models can be used to estimate the mixture stiffness when certain conditions are met. Binder aging conditions must match the desired mixture conditions and adjustment factors may be needed to remove bias in the predictions. Theoretically, the Hirsch model, which is a viscoelastic liquid model, is more flexible for further adjustments such as incorporating the creep behavior of mixture. The Di Benedetto model gave relatively precise predictions but they were biased at low and high temperatures as the model does not incorporate mixture properties as input parameters.

Original languageEnglish (US)
Pages (from-to)575-625
Number of pages51
JournalAsphalt Paving Technology: Association of Asphalt Paving Technologists-Proceedings of the Technical Sessions
StatePublished - Dec 1 2007
EventAsphalt Paving Technology 2007 AAPT - San Antonio, TX, United States
Duration: Mar 11 2007Mar 14 2007


  • Binder and mixture master curves
  • Dynamic modulus
  • Hot-mix asphalt
  • Stiffness predictive models


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