An artificial neural network model for virtual Superpave asphalt mixture design

Abstract

This study presents an artificial neural network (ANN) model to predict the asphalt mixture volumetrics at Superpave gyration levels. The input data-set needed by the algorithm is composed of gradation of the mix, bulk specific gravity of aggregates, low- and high-performance grade of the binder, binder content of the mix and the target number of gyrations (i.e. N_ini, N_des and N_max). The proposed ANN model uses a three-layer scaled conjugate gradient back-propagation (feed-forward) network. The ANN was trained using data obtained from numerous roads with a total of 1817 different mix designs. Results revealed that the ANN was able to predict V_a within V_a (measured) ± 1.0% range 85–93% of the time and within V_a (measured) ± 0.5% range 60–70% of the time. Currently with the developed ANN model, Superpave mix design can take approximately between 1.5 and 4.5 days, which corresponds to 3–6 days of savings.

Keywords::

• Superpave
• artificial neural network
• asphalt mixture design
• virtual mix design
• compaction
• volumetrics

Acknowledgements

The authors would like to thank Robert C. Rea (Assistant Materials and Research Engineer) and Larry J. Koves from Roads Materials and Research Division from Nebraska DOT for providing the Superpave mix design data.