Abstract
This study aims to evaluate the interlayer bonding characteristics between double-layered asphalt systems. Three typical asphalt structures were chosen for evaluation. In each case, the bottom layer comprised of a dense graded asphalt concrete (AC-20) while three different asphalt mixtures were applied in surface layer: a dense graded mixture (AC-13), stone mastic asphalt (SMA-13), and open-graded friction course (OGFC-13). The effects of interface morphology, loading, and temperature conditions on interlayer bonding properties were considered. The interlayer shear and tensile strengths of the asphalt systems were tested using ‘shear-compression’ and ‘shear-tensile’ loading mode set ups, and the relationship between interface morphology and interlayer bond strength was quantitatively analyzed using 3D laser scanning. In addition, statistical analysis was performed to determine the significance level of each factor, followed by developing and validating prediction models for interlayer bond strength using multivariate nonlinear regression. The results showed that all factors had a significant effect on interlayer bonding performance, with temperature being the most prominent contributing factor. Increasing normal pressure had a positive effect on preventing the attenuation of interlayer shear strength, while an increase in shear stress was found to intensify the degradation of interlayer tensile strength. Moreover, interlocking and ensuring sufficient contact at the layer interface were demonstrated to enhance the interlayer friction effect positively. However, high normal stress could potentially lead to damage at the interface. Finally, the developed prediction models exhibited a high level of accuracy in estimating interlayer bond strengths and were able to explain over 97% and 98% of measured interlayer shear and tensile strengths, respectively. Employing a properly graded surface layer can improve the frictional and contact effects at the layer interface, thereby enhancing the overall interlayer bonding performance.
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