Investigation on strength characteristics of bio-asphalt mixtures based on the time–temperature equivalence principle

沥青 材料科学 复合材料 极限抗拉强度 乙状窦函数 岩土工程 工程类 计算机科学 人工神经网络 机器学习
作者
Xinghai Peng,Jiang Yuan,Zheng-Da Wu,Lingyun You,Xuan Zhu,Jing Liu
出处
期刊:Construction and Building Materials [Elsevier]
卷期号:309: 125132-125132 被引量:10
标识
DOI:10.1016/j.conbuildmat.2021.125132
摘要

Bio-asphalt mixture is prone to strength damage due to its unfavorable high-temperature performance and anti-aging properties. In order to improve the strength of the bio-asphalt mixture, this paper selected rock asphalt to modify bio-asphalt and studied the strength characteristics of rock asphalt modified bio-asphalt mixture. First, the neat asphalt mixture, rock asphalt modified asphalt mixture, bio-asphalt mixture, and rock asphalt modified bio-asphalt mixture were prepared. After that, the indirect tensile strength (ITS) tests at different temperatures and various loading rates were conducted. Based on the test results, linear function and Sigmoidal function were used to establish prediction models of four asphalt mixtures strength. Finally, the strength behavior of four asphalt mixtures was compared and analyzed according to the master curve. The test results indicated that the strength of the four asphalt mixtures increases with the increase in the loading rate and decreases with the increase in the temperature. Compared with the bio-asphalt mixture, the strength of the rock asphalt modified bio-asphalt mixture was better, proving the feasibility of the rock asphalt modification technology. Compared with the linear function, the master curve determined by the Sigmoidal function is smoother. The fitting correlation coefficient by the Sigmoidal function is higher than that by the linear function. The strength prediction model proposed in this study can effectively predict the strength of the asphalt mixture outside the test range through limited test data, which provides a test basis for applying bio-asphalt mixture in pavement design under different climate and traffic load conditions.
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