Asphalt aging often leads to rapid degradation of road performance, which seriously affects the service life of asphalt pavement. Exploring the influence of asphalt oil sources, asphalt grades, and filler types on asphalt microcharacteristics in the asphalt aging process can provide an essential reference to guide asphalt pavement maintenance. In this study, we selected seven kinds of asphalt and three fillers commonly used in China for research. The pressurized aging vessel (PAV) and homemade ultraviolet (UV) aging equipment were used to perform thermo-oxidative aging and UV aging tests, respectively, of asphalt. The microcharacteristics of asphalt before and after aging were analyzed via attenuated total reflectance fourier transformation infrared spectroscopy and nuclear magnetic resonance 1H spectroscopy. The results show that the oil source of asphalt exerted the most significant influence on the microcharacteristics of the aged asphalt, while the effect of the asphalt grade was relatively limited. The addition of fillers did not affect the aging mechanism of asphalt. UV and PAV aging generated apparent differences in the changes in the aged asphalt microstructure.@en

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.@en