Application of artificial intelligence to modelling asphalt–rubber viscosity

Abstract

The viscosity of binder is of great importance during the handling, mixing, application and compaction of asphalt in highway surfacing. This paper presents experimental data and the application of artificial intelligence techniques (statistics, artificial neural networks (ANNs) and fuzzy logic) to modelling of apparent viscosity in asphalt–rubber binders. The binders were prepared in the laboratory by varying the rubber content (RC), rubber particle size, duration and temperature of mixture in conformity with a statistical design plan. Multi-factorial analysis of variance showed that the RC has a major influence on the viscosity observed for the considered interval of parameters variation. When only limited experimental data of design matrix are available for modelling, the fuzzy logic model is the best model to be used. In addition, the combined use of ANN and multiple regression analysis improved the characteristics of the neural network.

Keywords::

• viscosity
• asphalt–rubber
• statistics
• neural network
• fuzzy logic
• artificial intelligence

Notes

^1. Email: [email protected]

Additional information

Funding

The authors would like to thank CNPq for the research grant [process 302860/2011-8] and [process 313706/2009-3].