Prediction of Rheological Parameters of Asphalt Binders with Artificial Neural Networks

Abstract

Recycling of industrial, agricultural etc. wastes is economically and environmentally important. In recent years, researchers was focused on the using wastes in structural materials. In this study, modified asphalt binders were obtained by adding 7 different ratios waste engine oil (2%, 4%, 6%, 8%, 10%, 12% and 14%), which released as a result of routine maintenance of automobiles, to the pure asphalt binder. Then, Dynamic Shear Rheometer (DSR) experiments were applied on pure and modified asphalt binders. The rheological properties of asphalt binders at different temperatures and frequencies (loading rates) were evaluated by performing the DSR Test at 4 different temperatures (40°C, 50°C, 60°C and 70°C) and 10 different frequencies (0.01-10Hz). Then, the obtained complex shear modulus and phase angle values were estimated with Artificial Neural Networks. The results showed that the addition of 2% waste mineral (engine) oil improved the elastic properties of the asphalt binder by increasing the complex shear modulus and decreasing the phase angle values. In addition, it was concluded that the rheological parameters of asphalt binders can be successfully obtained with Artificial Neural Networks, by estimating the results with low error rate and high accuracy.