Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An artificial neural network model for virtual Superpave asphalt mixture design
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. Nini, Ndes and Nmax). 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 Va within Va (measured) ± 1.0% range 85–93% of the time and within Va (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.
An artificial neural network model for virtual Superpave asphalt mixture design
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. Nini, Ndes and Nmax). 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 Va within Va (measured) ± 1.0% range 85–93% of the time and within Va (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.
An artificial neural network model for virtual Superpave asphalt mixture design
Ozturk, Hande I. (Autor:in) / Kutay, M. Emin (Autor:in)
International Journal of Pavement Engineering ; 15 ; 151-162
07.02.2014
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
An artificial neural network model for virtual Superpave asphalt mixture design
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