Waste tire rubber powder-modified asphalt (RMA) has been widely used in road construction, which was traditionally limited by the poor compatibility of RMA, affecting pavement performance. By synthesizing epoxy soybean oil with amide groups (ESO/TETA) and grafting it onto desulfurized rubber powder (DCR) through microwave irradiation, a surface-functionalized rubber powder (MDCR) was produced successfully. The effects of the physical properties, storage stability, thermal stability, and rheological behavior of the modified asphalt were studied. The results show that the MDCR with a polar surface improved the compatibility and adhesive interactions between the modified crumb rubber and the asphalt. The MDCR content could reach 50%, and the phase separation could meet the requirements of 2.2 °C, which has application conditions in engineering for stable storage. Additionally, the inclusion of MDCR in the asphalt formulations significantly mitigated the temperature sensitivity of the modified asphalt. Importantly, when the MDCR constituted from 20% to 50% of the asphalt, there was a noted reduction in the phase angle at temperatures above 70 °C, indicating a significant improvement in the elastic efficiency. The MDCR also led to substantial enhancements in the resistance of the asphalt to high-temperature and high-stress rutting, addressing the crucial limitations in the consumption ability of waste tire rubber powder and improving the overall performance of RMA in pavement applications.

The pavement deflection measured by falling weight deflectometer (FWD) detection equipment can be used to determine the bearing capacity of a pavement structure through modulus backcalculation to provide suggestions for pavement structure maintenance. However, because asphalt mixtures are temperature-sensitive materials, they are greatly affected by temperature, and changes in temperature can cause their mechanical properties to vary and affect the structural bearing capacity. Therefore, this paper conducts a pavement deflection test on an in-service highway asphalt pavement at different temperatures in different seasons. The representative temperature of the asphalt layer was determined through comparative analysis. The temperature’s effective influence distance on the deflection basin was found, and a more suitable temperature correction model for the deflection basin of asphalt pavement was established. The results showed that the temperature at the middle layer depth of the asphalt layer is more suitable as the representative temperature of the asphalt layer. The effective influence range of temperature on the deflection basin is 90 cm. The deflection change rate before and after the temperature correction at a radial distance of 20 cm is more appropriate for the temperature correction of the deflection basin.

This study employed molecular dynamics simulation to investigate the mechanism of action of graphene-modified asphalt. A series of molecular models of graphene-modified asphalt were constructed and validated using thermodynamic parameters. The impact of the graphene (PGR) size and number of layers on its interaction with asphalt components were examined, and the self-healing process and mechanism of action of PGR-modified asphalt were analyzed. The results demonstrated that the size and number of layers of PGR significantly influenced its interaction with asphalt components, with polar components demonstrating a stronger affinity for PGR. When the size and number of layers of PGR were held constant, the interfacial binding energy between it and ACR-modified asphalt was the highest, followed by SBS-modified asphalt, and 70# matrix asphalt exhibited the lowest interfacial binding strength. This interfacial binding strength is primarily attributed to intermolecular van der Waals interactions. Furthermore, the incorporation of multi-layer PGR can markedly enhance the mechanical properties of matrix asphalt, whereas small-sized PGR is more efficacious in improving the low-temperature performance of polymer-modified asphalt. PGR can act as a bridge between asphalt molecules through rapid heat transfer and π-π stacking with aromatic ring-containing substances, which markedly increases the free diffusion ability of asphalt molecules, shortens the healing time of asphalt, and enhances the collective self-healing performance of asphalt. This study provides an essential theoretical basis for understanding the mechanism and application of PGR in asphalt modification.

Significant regional environmental differences result in varied patterns of pavement temperature changes. To enhance the cross-regional adaptability of temperature prediction models, transfer learning (TL) was introduced into the random forest (RF) model to improve its generalization capability. Firstly, meteorological data on air temperature, solar radiation, relative humidity, and wind speed were collected from different regions. Pavement temperatures at various depths were also monitored over a long period. Secondly, prediction models were constructed using the RF method. The prediction performance of the models was evaluated. Thirdly, the RF model was optimized using TL with a feature enhancement strategy. Finally, the optimized model was validated using data from other regions not included in the initial training set. The results indicated that although the RF model achieved good prediction accuracy within individual regions, its performance declined when applied across different regions. After optimization through TL with feature enhancement, the model’s prediction accuracy in target regions was significantly improved. Specifically, the mean squared error was reduced from 44.91 to 14.88, the mean absolute error from 4.91 to 2.78, and the coefficient of determination increased from 0.75 to 0.92. Further validation revealed that the determination coefficient exceeded 0.94 and the mean absolute error remained below 2.3 °C at all depths. In summary, the transfer learning approach based on the random forest model demonstrates strong adaptability to different regions. It effectively addresses the issue of reduced prediction accuracy caused by regional differences and provides a reliable method for accurate pavement temperature prediction across multiple regions.

1. The commonly observed top-down transverse cracks are considered to be caused by low-temperature environments (Behnia et al., 2018). Low-temperature cracking mechanisms focus on temperature chang...