Publication: Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
Date
2024
Authors
Jegatheesan N.
Ibrahim M.R.
Ahmed A.N.
Koting S.
El-Shafie A.
Katman H.Y.B.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. During the data collection, the properties of the modified binders prepared at 6, 10 and 14% of crumb rubber (CR), considering three types of modifications and eighteen blending scenarios with different interaction factors, were assessed in terms of penetration, softening point, rotational viscosity, storage stability, rheological parameters, and rutting and fatigue factors. Results showed that the Matern 5/2 Gaussian Process Regression (GPR) model demonstrated efficient performance in predicting physical, viscoelastic, rutting, and fatigue properties whereas wide artificial neural networks exhibited enhanced accuracy in predicting storage stability and rotational viscosity. The results also suggest that it is feasible to implement a single type of model developed using the Matern 5/2 GPR algorithm for accurately predicting all the TB-CRMB properties considered. The best models demonstrated that crosslinking additives significantly influenced TB-CRMB production and performance. In TB-CRMB production, sulfur as a crosslinking additive showed better compatibility than trans-polyoctenamer-rubber and significantly reduced interaction temperatures at lower CR content, leading to energy savings compared to the traditional TB production. ? 2024 Elsevier Ltd
Description
Keywords
Regression analysis , Composite modification , Crosslinking additive , Crumb rubber , High interaction parameter , Interaction parameters , Machine learning algorithms , Modified bitumen , Prediction modelling , Terminal blend-crumb rubber modified bitumen , Terminal blends , Prediction models