An artificial neural network model for virtual Superpave asphalt mixture design

Ozturk H. I., Kutay M. E.

INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, vol.15, no.2, pp.151-162, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 2
  • Publication Date: 2014
  • Doi Number: 10.1080/10298436.2013.808341
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.151-162
  • Keywords: Superpave, artificial neural network, asphalt mixture design, virtual mix design, compaction, volumetrics, PREDICTION
  • Middle East Technical University Affiliated: No


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.