Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Viscosity prediction of CRM binders using artificial neural network approach
The primary objective of this study was to develop a series of artificial neural network (ANN) models to predict the viscosity values of crumb rubber-modified (CRM) binders using four input variables: asphalt binder source, rubber size, mixing duration and rubber content. The results indicated that ANN-based models are effective in predicting the viscosity values of CRM binders regardless of rubber type and can easily be implemented in a spreadsheet. In addition, the developed ANN model can be used to predict viscosity values of other types of CRM binders. Furthermore, the results also show that asphalt binder source, rubber size and rubber content are the most important factors in the developed ANN models while the mixing duration is relatively unimportant. The sensitivity analysis of input variables indicated that the viscosity changes significantly with changes in asphalt binder source, rubber size and rubber content.
Viscosity prediction of CRM binders using artificial neural network approach
The primary objective of this study was to develop a series of artificial neural network (ANN) models to predict the viscosity values of crumb rubber-modified (CRM) binders using four input variables: asphalt binder source, rubber size, mixing duration and rubber content. The results indicated that ANN-based models are effective in predicting the viscosity values of CRM binders regardless of rubber type and can easily be implemented in a spreadsheet. In addition, the developed ANN model can be used to predict viscosity values of other types of CRM binders. Furthermore, the results also show that asphalt binder source, rubber size and rubber content are the most important factors in the developed ANN models while the mixing duration is relatively unimportant. The sensitivity analysis of input variables indicated that the viscosity changes significantly with changes in asphalt binder source, rubber size and rubber content.
Viscosity prediction of CRM binders using artificial neural network approach
Xiao, Feipeng (Autor:in) / Putman, Bradley J. (Autor:in) / Amirkhanian, Serji N. (Autor:in)
International Journal of Pavement Engineering ; 12 ; 485-495
01.10.2011
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Viscosity prediction of CRM binders using artificial neural network approach
| Online Contents | 2011
| DOAJ | 2024
| British Library Online Contents | 2009