Fatigue cracking is one of the most notable distresses in steel bridge deck pavement (SBDP), necessitating the development of high performance pavement materials to extend service life. In this paper, the fatigue resistance characteristics of natural rock asphalt (NRA)/SBS systematically evaluated. Twelve types of composite-modified asphalt binders were prepared by incorporating Iranian rock asphalt (IRA), Buton rock asphalt (BRA), and SBS as modifiers. IRA and BRA were added at 5 wt%, 10 wt%, and 15 wt%, while SBS was introduced at 2 wt% and 3 wt%. The modified asphalt binders subjected to time sweep (TS) test and linear amplitude sweep (LAS) test to comparatively analyze fatigue damage and fatigue life. DSR-C model was applied to calculate the fatigue crack length. The research also comparative evaluate differences and correlations between fatigue crack length indexes and conventional fatigue life indexes. The results showed that NRA enhanced the fatigue performance of modified asphalt binders. With the increase of NRA content, the fatigue properties of modified asphalt also become better. S₃I₁₅ is better than other composite modified asphalt binders in both fatigue life and fatigue cracking indexes, demonstrating that it offers the best resistance to fatigue damage. The fatigue cracking index calculated by the DSR-based cracking (DSR-C) model could effectively evaluate the fatigue performance of NRA/SBS composite modified asphalt binders. NRA/SBS composite modified asphalt could be widely used in steel bridge deck pavement.

In order to explore the feasibility and efficacy of reed-fiber-modified bitumen (RFMB), three lengths and three dosages of reed fibers were selected to modify bitumen and bituminous mortar, while the physicochemical properties of RFMB and RFMB mortar were analyzed. In this work, FTIR spectroscopy was employed to characterize the chemical impact of fiber on bitumen. The viscidity and rheology of RFMB and the tensile strength of RFMB mortar were evaluated using a Brookfield viscometer, dynamic shear rheometer, and monotonic tensile test. The results showed that adding fibers primarily affects the physical structure rather than the chemical composition of bitumen, confirmed by FTIR spectroscopy. RFMB viscosity increased with higher fiber dosage and fiber length. Rheological evaluations showed an enhanced complex shear modulus for RFMB, suggesting improved performance at higher temperatures but increased stiffness at lower temperatures, with the latter indicating reduced flexibility. RFMB also demonstrated superior fatigue and rutting resistance, albeit with compromised stress sensitivity. Tensile tests on RFMB mortar highlighted significant improvements, especially with longer fibers, while shorter 0.4 mm fibers showed modest reinforcement effects, possibly due to uneven distribution during sample preparation.

The surface texture of asphalt pavement has a significant effect on skid resistance performance. However, its contribution to the performance of skid resistance is non-homogeneous and subjects to local validity. There are also a few deep learning models that take into account the effective contact texture region. This paper proposes a convolutional neural network model based on the effective contact texture region, containing macro- and micro-scale awareness sub-modules. In this study, the asphalt mixture with varying gradations was designed to accurately obtain the effective contact texture region. Then, the textures were disentangled into macro- and micro-texture scales by applying the fast Fourier transform and fed into the model for training. Finally, the area of effective contact texture region was calculated, and the effective contact ratio parameter was then proposed using the triangulation algorithm. The results showed that the effective contact texture area of pavement varies by the asphalt mixture type. The effective contact ratio parameter exhibited a significant positive correlation (Pearson correlation coefficient is 0.901, R²= 0.8129) with skid resistance performance and was also influenced by key sieve aggregate content from 2.36 to 4.75 mm. The data of effective contact texture region following disentanglement significantly released the model performance (the relative error dropped to 1.81%). The model exhibited improved precision and performance, which can be utilized as an efficient, non-contact alternative method for skid resistance analysis.