Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives

Document Type

Article

Publication Date

9-1-2024

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 TBCRMB 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.

Keywords

Machine-learning algorithms, Prediction models, Terminal blend-crumb rubber modified, bitumen, Crosslinking additive, Composite modification, High interaction parameters

Divisions

sch_civ

Funders

Ministry of Higher Education (MOHE) Malaysia under the Fundamental Research Grant Scheme (FRGS) (18255) ; (FRGS/1/2020/TK0/UM/02/35)

Publication Title

Construction and Building Materials

Volume

444

Publisher

Elsevier

Publisher Location

125 London Wall, London, ENGLAND

This document is currently not available here.

Share

COinS