High percentage reclaimed asphalt pavement (RAP) is prevailing in pavement engineering for its advantages in sustainability and environmental friendliness, however, its fatigue resistance remains a major concern. Fine aggregate matrix (FAM) is a crucial part in the fatigue resistance of asphalt mixtures with high RAP content. Hence, the linear amplitude sweep (LAS) test of FAM has been developed to study the fatigue resistance of asphalt mixtures. However, the torsional loading mode of the LAS test with a dynamic shear rheometer (DSR) is a limitation to simulate traffic load. In this paper, an alternative LAS test for FAM with high RAP content was proposed. Beam FAM specimens were tested using a dual-cantilever flexural loading fixture in a dynamic mechanical analyzer (DMA). To investigate the influence of RAP content and the rejuvenating agent (RA), four kinds of FAM mixes were tested with this method to study their fatigue resistance. The test results suggested that the repeatability of this alternative approach was reliable. A fatigue failure criterion based on maximum $C\times N$ was defined. Then, fatigue life prediction models based on viscoelastic continuum damage (VECD) analysis were established according to the LAS test results and validated by a strain-controlled time sweep (TS) test. It turned out that as RAP content increased, the modulus of FAM would be significantly raised, accompanied with a drop in the phase angle. The fatigue life of FAM would be greatly shortened when the RAP binder replacement rate reached 50%. Adding RA could considerably improve the dynamic properties of FAM mixes with high RAP content, resulting in a decrease in modulus, increase in phase angle and elongating fatigue life, but could not recover to the level of virgin binder.

As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R² = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.

The pavement skid resistance performance index is one of the most important indexes to ensure driving safety. Based on the test data of RIOHTrack full-scale track, this paper analyzes the decay law of pavement skid resistance performance, including the sideway force coefficient (SFC), British pendulum number (BPN), mean texture depth measured by sand patch method (MTD) and sensor measured texture depth by laser method (SMTD), under different equivalent single axle load times (ESALs). The paper analyzes the influence of different methods and conditions on the different indicators and excavates the internal correlation of different pavement skid resistance performance indexes, aiming to improve the effectiveness and accuracy of pavement skid resistance performance detection. The shortcomings of the temperature correction method of BPN and SFC are verified, which cannot correct the skid resistance performance effectively in different temperatures. Based on the assumption that there is a sensitive range of temperature influence on skid resistance performance, a new temperature correction method of skid resistance performance index is proposed based on the Boltzmann model and equivalent temperature of the asphalt surface layer. It can truly reflect the decay law of skid resistance pavement performance. At the same time, the internal correlation between BPN and MTD indicators is analyzed. It is found that there is a linear growth law between two indexes whose correlation coefficient is 0.999, which provides a reference for the research of pavement skid resistance performance.

Asphalt mortar is a typical temperature-sensitive material that plays a crucial role in the performance of asphalt mixture. This study evaluates the high- and low-temperature performance of asphalt mortar based on the dynamic mechanical analysis (DMA) method. Temperature-sweep tests of asphalt mortars were conducted using the DMA method under fixed strain level, frequency, and heating rate conditions. The dynamic mechanical response curves, characteristic temperature, and other indices were obtained and used to investigate the high- and low-temperature performance of asphalt mortar. The results showed that the phase transition temperatures T₁, T₀, and T_g can be used to evaluate the low-temperature performance of asphalt mortar. Additionally, they had a good linear relationship, and the evaluation results were consistent. Meanwhile, T₂, E₆₀, and tan(δ)_max indicators can effectively evaluate the high-temperature performance of asphalt mortar. Asphalt plays a key role in the performance of asphalt mortar. Mortars with neat asphalt A70 and modified asphalt AR had the worst and best high- and low-temperature performances, respectively. Furthermore, the finer gradation improved the low-temperature performance of asphalt mortar, while the coarser gradation improved the high-temperature properties of modified asphalt mortars but had the opposite effect on neat asphalt A70.

Asphalt suffers from a series of aging processes in the natural environment. This is a significant factor in asphalt pavement diseases. Research of the properties of the decay processes in the natural weathering of asphalt will be helpful in distinguishing the characteristics of the various types of asphalt and in the selection of pavement materials. Neat asphalt A70, a styrene‐butadiene‐styrene‐ (SBS‐) modified asphalt, and crumb‐rubber‐modified asphalt AR are exposed to outdoor conditions to weather naturally. This process is traced by testing the basic physical properties and the surface free energy of asphalts, using the sessile drop method. Results illustrate that the basic physical properties of asphalt change significantly during the natural weathering process and that the rubber asphalt has the superior aging resistance, while the neat asphalt A70 has a high aging susceptibility. Furthermore, the presence of the SBS and rubber‐powder modifiers transforms the change trend of the surface free energy of neat asphalt. The adhesion work between aggregate and asphalt can be used to quantitatively evaluate the adhesive properties between them. Meanwhile, the presence of moisture between the asphalt and aggregate changes cohesive failure into adhesive failure.