Prediction of asphalt creep compliance using artificial neural networks

• Autorzy: Zofka A. , Yut I.
• Języki publikacji: EN

Abstrakty

EN

Creep compliance of the hot-mix asphalt (HMA) is a primary input of the current pavement thermal cracking prediction model used in the US. This paper discusses a process of training an Artificial Neural Network (ANN) to correlate the creep compliance values obtained from the Indirect Tension (IDT) with similar values obtained on small HMA beams from the Bending Beam Rheometer (BBR). In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance functions built on the ANN-predicted discrete values also exhibited a good correlation when compared with the laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the measured values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.

Słowa kluczowe

EN

asphalt binder; hot mix asphalt; artificial neural network; bending beam; rheometer; creep compliance; indirect tension test

PL

lepiszcze asfaltowe; mieszanka asfaltowa gorąca; sieć neuronowa sztuczna; belka zginana; reometr; podatność na pełzanie; badanie napięcia pośredniego

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2012
• Tom: Vol. 58, nr 2
• Strony: 153--173
• Opis fizyczny: Bibliogr. 36 poz., il.

Twórcy

autor

Zofka A.

autor

Yut I.

• Department of Civil and Environmental Engineering, University of Connecticut, USA

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