Performance Evaluation of Epoxy Asphalt Mixtures Prepared with Basalt and Limestone Aggregate under Different Epoxy System Contents
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 7
Abstract
Epoxy asphalt mixture with the epoxy system (EPS) and basalt has excellent road performance, but due to its high price, epoxy asphalt mixture is limited in its applications in conventional pavement. In addition, the basalt aggregate commonly used in epoxy asphalt mixture has shortages of high cost and insufficient reserves. This paper tries to reduce the EPS content and select suitable aggregates to prepare epoxy asphalt mixture with good road performance for conventional pavement. Basalt epoxy asphalt mixture (BEAM) and limestone epoxy asphalt mixture (LEAM) were designed based on the Bailey method. The road performance of BEAM and LEAM was evaluated by the Marshall test, rutting test, low-temperature bending test, water stability test, and fatigue test. Meanwhile, the mechanism of the EPS on the road performance of the mixture was revealed by the laser scanning confocal microscopy test. The results showed that the high-temperature deformation resistance, water damage resistance, and fatigue resistance of BEAM and LEAM were improved with the increase of EPS content, but the low-temperature crack resistance of the two mixtures was slightly decreased. Especially when the EPS content was 35 wt%, the performance change of BEAM and LEAM was more significant, which was caused by the phase transition in epoxy asphalt (EA). Therefore, 35 wt% EPS was recommended in EA. In addition, compared with BEAM, LEAM had better low-temperature crack resistance, less favorable high-temperature deformation, and fatigue resistance. The addition of EPS availably decreased the difference in road performance between LEAM and BEAM. Meanwhile, LEAM showed obvious cost advantages over BEAM. The research results of this paper had positive significance for expanding the engineering application of epoxy asphalt mixtures.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the Natural Science Foundation of Jiangsu Province: (Grant No. BK20201281); and the Postgraduate Research & Practice Innovation Program of Jiangsu Province: (No. SJCX22_0060).
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Received: Aug 24, 2022
Accepted: Dec 5, 2022
Published online: Apr 29, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 29, 2023
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