An artificial neural network model for virtual Superpave asphalt mixture design

Date

2014-02-07

Author

Ozturk, Hande I.
Öztürk, Hande Işık

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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.

Subject Keywords

Mechanics of Materials, Civil and Structural Engineering

URI

https://hdl.handle.net/11511/48076

Journal

INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING

DOI

https://doi.org/10.1080/10298436.2013.808341

Collections

Department of Civil Engineering, Article

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Citation Formats

H. I. Ozturk and H. I. Öztürk, “An artificial neural network model for virtual Superpave asphalt mixture design,” INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, pp. 151–162, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48076.