Nominal property based predictive models for asphalt mixture complex modulus (dynamic modulus and phase angle)

- Provides models that use only nominal inputs to make reliable property estimates during design phase.
- Presents generalized regression framework for developing asphalt property prediction models.
- Model is verified through statistical comparisons and comparisons with other predictive models.
- Application of proposed model for pavement performance prediction is demonstrated.

Dynamic modulus (|E∗|) and phase angle (δ) are necessary for determining the response of asphalt mixtures to in-service traffic and thermal loadings. While a number of |E∗| and δ predictive models have been developed, many of them require lab measured properties (e.g. binder complex modulus). The majority of previous work has focused only on prediction of |E∗|, limited models exist for prediction of δ. This research utilized generalized regression modelling of lab measurements (from 81 asphalt mixtures) to develop and verify prediction models for |E∗| and δ using only nominal asphalt mix properties that are readily available during the initial mixture design and specification process.