Analysis of crumb rubber content influence on damage evolution and pattern recognition of rubberised epoxy asphalt mixture using acoustic emission techniques

Shi, Chenguang, Wu, You, Wang, Haopeng ORCID: 0000-0002-5008-7322, Liu, Song, Liu, Pengfei, Yang, Jun and Huang, Wei (2024) Analysis of crumb rubber content influence on damage evolution and pattern recognition of rubberised epoxy asphalt mixture using acoustic emission techniques INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 25 (1). 2356762-. ISSN 1029-8436, 1477-268X

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Abstract

The application of coarse crumb rubber (CR) to toughen the epoxy asphalt mixture has emerged as a viable choice. However, the evolution rule of strength and damage modes of mixtures with different crumb rubber contents remains elusive. The objective of this study is to clarify the failure mechanisms in rubberized epoxy asphalt mixture combined acoustic emission (AE) data and k-means clustering theory. Four types of epoxy asphalt mixture samples are tested with the monitor of AE sensors. Based on the temporal and frequential features of AE signals, damage evolution is evaluated by the Weibull function, b-value, and the relationship between the raise angle and average frequency at the peak loading. Moreover, the k-means algorithm is conducted to cluster the AE events and damage characteristics after pre-processing the data. The test results indicate that CR particles enhance the toughness of epoxy asphalt mixtures and the mixtures with fewer CR are susceptible to occur sudden fracture due to brittle coarse aggregates. The k-means method effectively identifies AE events into four types of damaged behaviours. The findings offer valuable insights into the strength attenuation, modification characteristics, damage evolution and damage modes of rubberized epoxy asphalt mixtures, contributing to the development of more durable and resilient asphalt materials.

Item Type:	Article
Uncontrolled Keywords:	Rubberised epoxy asphalt mixture, acoustic emission, clustering, damage evolution, damage pattern recognition
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	23 Sep 2024 07:32
Last Modified:	16 Jun 2026 20:03
DOI:	10.1080/10298436.2024.2356762
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3184678
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