Abstract

To study the influence of temperature and reclaimed asphalt pavement (RAP) content (0%, 30%, 50%, and 70%) on the high-temperature performance and anti-reflection cracking performance of warm-mix recycled asphalt mixture, triaxial repeated creep tests and overlay tests (OT) based on digital image correlation technology (DIC) were used to analyze the high temperature and anti-reflection cracking performance of recycled asphalt mixtures. Based on the improved Burgers model, a permanent deformation (PD) model was established to evaluate the high-temperature performance of recycled asphalt mixtures. The anti-reflection cracking performance of recycled asphalt was quantitatively analyzed from macro and meso perspectives by using a macro index (load loss rate) and a meso index (vertical strain density DE). In addition, a reflection cracking damage model was developed by cumulative load loss rate relative change rate, and the fractal characteristics of reflection cracks were studied according to the fractal theory. The results show that with an increase in RAP content, the anti-deformation ability of a recycled asphalt mixture is enhanced, and the anti-reflection cracking performance becomes worse. However, with an increase in temperature, the anti-deformation ability of recycled asphalt mixture is weakened, and the anti-reflection cracking performance is enhanced. A PD mechanical model can well describe the high-temperature deformation resistance of recycled asphalt mixtures. The established reflection cracking damage model has high fitting accuracy and accurately describes the reflection cracking damage evolution process of recycled asphalt mixture. In addition, the analysis of the fractal characteristics of reflection cracks shows that the reflection cracks of a recycled asphalt mixture have fractal characteristics of statistical significance. DIC technology can be used to evaluate the anti-reflection cracking performance of recycled asphalt mixtures qualitatively and quantitatively from the mesoscopic perspective, which makes up for the current deficiency in macroscopic evaluation.

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