Journal of Transportation Research

Journal of Transportation Research

Presenting Predictive Models of Rheological Characteristics of Modified Bitumens Containing SBS and Carbon Nanotubes Using Nonlinear Regression and Neural Network

Document Type : Original Article

Author
Amir Amini
Department of Civil Engineering, Na.C, Islamic Azad University, Najafabad, Iran.
10.22034/tri.2025.522495.3341
Abstract
Despite the significant advantages of polymer bitumens modified with nanoparticles, only a few studies have focused on predicting their rutting performance. Correct estimation and prediction of pavement rutting behavior as an effective approach of functional and environmental variables can prevent the production of asphalt with high rutting potential in the laboratory stage and before factory production. This research deals with modeling the synergistic effect of SBS and carbon nanotubes (CNT) in improving the rheological properties of bitumens using artificial intelligence and statistical analysis. The rutting performance of bitumens containing 0, 0.6 and 1.2% CNT and 3 and 6% SBS have been investigated separately and in combination using temperature sweep test, analysis of variance and regression and neural network (ANN) models. The results showed that the synergism of polymer and nanoparticles increases the rutting resistance and improves the rheological behavior of bitumen at high temperatures. The variance test showed that the increase of CNT to 1.2% has the greatest effect on improving the rutting resistance. Comparing the performance of prediction models showed that R² for regression and ANN models is above 0.90. However, the ANN model performs better with a correlation coefficient of 0.976 and can be suggested as an effective tool in predicting the rheological behavior of nanocomposite-modified bitumen to prevent asphalt with high rutting potential in the pre-production stage.
Keywords
Rheological Characteristics
Artificial Neural Network
Multiple Regression
Nanocomposite
Modified Bitumen
Subjects
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