Prediction of field aging gradient in asphalt pavements

Xue Luo*, Fan Gu, Robert L. Lytton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The aging of asphalt pavements is a key factor that influences pavement performance. Aging can be characterized by laboratory tests and prediction models. Common aging prediction models use the change of physical or chemical properties of asphalt binders based on regression techniques or aging reaction kinetics. The objective of this study was to develop a kinetics-based aging prediction model for the mixture modulus gradient in asphalt pavements to study long-term in-service aging. The proposed model was composed of three submodels for baseline modulus, surface modulus, and aging exponent to define the change of the mixture modulus with pavement depth. The model used kinetic parameters (aging activation energy and preexponential factor) of asphalt mixtures and combined the two reaction rate periods (fast-rate and constant-rate). Laboratory-measured modulus gradients of 29 field cores at different ages were used to determine the model parameters. The laboratory testing condition was converted to the field condition at a given age and corresponding temperature by introducing the rheological activation energy to quantify the temperature dependence of field cores at each age. The end of the fast-rate period or the beginning of the constant-rate period was accurately identified to model these two periods and to determine the associated parameters separately. The results showed that the predictions matched well with the measurements and the calculated model parameters were verified. The proposed aging prediction model took into account the major factors that affect field aging speed of an asphalt pavement, such as the binder type, aggregate type, air void content, pavement depth, aging temperature, and aging time.

Original languageEnglish
Pages (from-to)19-28
Number of pages10
JournalTransportation Research Record
Publication statusPublished - 1 Jan 2015


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