The stable skeletal structure formed by the interlocking mechanism of the spatial movement of aggregate particles during compaction is the mechanism by which asphalt pavements are compacted and shaped. A major problem facing conventional compaction monitoring is that lacking the ability to monitor particle movement and a real-time evaluation method for pavement compaction, which can easily lead to problems such as uneven compaction and over-compaction. In addition, vehicle speed is an important parameter for analysing the dynamic response of a pavement. Current vehicle speed monitoring relies on complex field equipment and the collected vehicle speed parameters are difficult to match with other sensors for fusion analysis. In this paper, a method for monitoring the compaction quality of asphalt pavements and a method for collecting vehicle speed is proposed based on intelligent aggregate and aggregates interaction mechanisms. The experimental results show that with the continuous action of the compaction machinery, the compaction state of the asphalt pavement can be reliably captured and monitored by studying the spatio-temporal movement pattern of the intelligent aggregates. Meanwhile, the analysis of the time nodes of intelligent aggregate attitude change under the action of vehicle load allows accurate acquisition of vehicle speed parameters. Therefore, the method proposed in this study can improve the accuracy of asphalt pavement compaction state control as well as the accurate acquisition of vehicle speed. It can provide a reference for the later analysis of the dynamic response of pavement loads and the intelligent compaction of pavements.

In the asphalt–aggregate system, the aggregate-bitumen interface cohesive and adhesive bond determine the mechanical properties of asphalt pavement. The presence of moisture leading to adhesive failure at the binder-aggregate interface and/or cohesive failure within the binder or binder-filler mastic is the main mechanisms of moisture damage in the spring-thaw season. In order to evaluate the effect of nano hydrophobic silane silica (NHSS) on aggregate-bitumen interface bond strength in the spring-thaw season, an aggregate-bitumen interface bond strength test was proposed to quantify the interface bond strength of base asphalt and NHSS modified asphalt. Then, the effect of temperature, freeze-thawing cycles and moisture on aggregate-bitumen interface shear strength of base asphalt and NHSS modified asphalt was also discussed. The results illustrated that the shear failure dominated the aggregate-bitumen interface bonding failure in the spring-thaw season, and temperature and moisture had a significant effect on interface shear strength of modified and unmodified asphalt. Moreover, the addition of NHSS could increase the aggregate-bitumen interface shear strength under any working conditions. Furthermore, the moisture damage model of aggregate-bitumen interface shear strength of base asphalt (BA) and NHSS modified asphalt was established based on a research method combining numerical calculations and laboratory tests.

Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.

Ice formation on concrete surfaces significantly challenges productivity, economic growth, and safety in diverse industrial sectors. Superhydrophobic coatings represent an effective solution to delay ice formation, although their functionality deteriorates under repeated freeze–thaw cycles. To address this issue, carbon nanotubes (CNTs) are frequently employed due to their exceptional photothermal conversion and mechanical properties, which contribute to extending the sustainability of the superhydrophobic coatings. However, the chemical inertness of CNTs often necessitates complex reactions to modify their functionalization. In this study, we have invented a simple method involving the sequential growth of silica on the surface of CNTs and the hydrophobic modification of the silica surface to enhance CNT functionality. These CNTs/SiO₂ functionalized nanoparticles were then incorporated into an epoxy resin using a simple spray technique, resulting in a superhydrophobic and photothermal coating on concrete. To fine-tune the coating’s properties, we explored the effects of varying the doping levels of the nanoparticles on the surface morphology, roughness, and wettability of the CNT/SiO₂-EP coatings. The optimal level of hydrophobicity was achieved by doping the coatings with 300 mg of functionalized nanoparticles, yielding an impressive contact angle of 159.6°. The integration of functionalized nanoparticles into the epoxy matrix not only enhances hydrophobicity but also improves mechanical robustness and abrasion resistance by creating multiscale surface roughness. Additionally, the coating exhibits outstanding chemical stability even under extreme conditions. One of the most significant advantages of these coatings is their ability to extend the ice nucleation time significantly. This effect is primarily attributed to the superior superhydrophobicity of the nanoparticles and the remarkable photothermal conversion capability of the CNTs. Upon exposure to Xenon lamp radiation, the ice droplets rapidly melt, underscoring the impressive performance of these coatings in preventing ice formation.

Rutting has always been considered the main disease in asphalt pavement. Dealing with rutting disease would be benefitted by understanding the formation of rutting and testing the rutting performance of mixtures more reasonably. The objective of this paper is to systematically investigate the rutting mechanism by employing a self-designed rutting tester along with the corresponding numerical simulations. The deformation of different positions of the existing tracking tester was found to be inconsistent, and the loading was not in line with reality. Accordingly, a more practical tester was proposed: the reduced scale circular tracking (RSCT) tester integrates the functions of asphalt mixture fabrication and rutting monitoring. The results demonstrated that the loading of the new tester is closer to the actual situation. In addition, determining the stress and displacement characteristics of particles in the asphalt mixture was found to be difficult due to the limitations of the testing methods. Therefore, a two-dimensional virtual rutting test based on the RSCT was built using PFC2D (Particle Flow Code 2 Dimension) to investigate the mechanism of formation in rutting and to obtain the corresponding guidance. The numerical simulation showed that all particles of the specimen tended to move away from the load location. The main cause of rutting formation was the eddy current flow of asphalt mastic driven by coarse aggregates. The aggregates with diameters ranging from 9.5 to 4.75 mm were observed to have the greatest contribution to rutting deformation. Therefore, the aggregate amount of these spans should be focused on in the design of mixture grading.