To study the effect of aggregate type on the adhesion between asphalt and aggregate, limestone, basalt, diabase, and 70# asphalt with SBS asphalt were selected. The mineral phase composition of the aggregates was analyzed by X-ray diffraction. The surface energy theory was used to calculate the adhesion work and the work of flaking. The modified water boiling method combined with image processing technology was used to quantitatively characterize the flaking behavior of the asphalt. The results show that the aggregate type is closely related to the asphalt–aggregate adhesion. The mineral compositions of different types of aggregates vary significantly, with limestone, being a strongly alkaline aggregate predominantly comprising CaCO₃, exhibiting better adhesion with asphalt. The contact angle test and modified boiling method also yielded the same results, and the adhesion relationship with asphalt was limestone > basalt > diabase. Image processing technology effectively characterizes the spalling situation of asphalt and conducts quantitative analysis.

Long-term aging deteriorates asphalt’s self-healing capacity, yet the molecular mechanisms of bio-oil rejuvenation remain unclear. The fluidity and healing index of an asphalt binder were tested using a dynamic shear rheometer, and a healing model was established using molecular dynamics software to analyze the movement state. The results show that after adding the bio-oil, the healing index of aged asphalt increases significantly, lowering the optimal healing temperature by 10.1 °C. MD simulations demonstrate that bio-oil weakens van der Waals forces (with a 15.3% reduction in non-bonded energy) to enhance molecular diffusion, with a critical healing distance of 0.87 Å and aggregation at 1.11 Å. The bio-oil reduces the activation energy for healing from 4.97 kJ/mol (aged asphalt) to 3.75 kJ/mol. Molecular dynamics simulations can effectively aid scholars in understanding the asphalt healing process and movement patterns.

In order to investigate the influence of different tire textures, pavement types, and vehicle parameters on the tire/road noise level and its spectrum characteristics, 19 kinds of asphalt pavement main structures of RIOHTrack full-scale test track were tested by the close-proximity trailer (CPXT) tire/road noise detection method. Considering investigated parameters such as tire texture, vehicle speed, and trailer axle weight, and relying on multi-functional road condition rapid detection vehicle and laboratory tests to collect a variety of road surface information and material parameters, a multiple-linear-regression model of tire/road surface noise level of RIOHTrack (Research Institute of Highway Full-scale Test Track) asphalt pavement was constructed. Finally, the causes of noise level differences among different influencing factors were further analyzed through spectrum characteristics. The results show that vehicle speed is the most important factor affecting tire/road noise. The noise level of different tires varies due to different textures, but the noise level among different trailer axle weights is roughly the same. Vehicle speed (v), FWD center deflection (D₀), surface asphalt mixture air voids (VV), sensor-measured texture depth (SMTD) and international roughness index (IRI) were selected to establish the noise prediction models of different tire textures. Noise spectrum analysis shows that the spectrum of different vehicle speeds is significantly wide in the full frequency range, and the spectrum variation of differently textured tires is mainly concentrated in a certain range of the peak frequency. The noise spectrum curve of porous asphalt concrete (PAC13) is significantly lower than that of other asphalt mixtures in the full frequency range above 800Hz, indicating a greater noise reduction effect.