To investigate the effect of styrene-butadiene-styrene (SBS) modifier content on the viscoelastic behavior of SBS-modified asphalt (SBSMA) at different temperatures and phase structures, the star SBS modifier was chosen to fabricate seven types of SBSMA with different contents. Multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), and low-temperature frequency sweep tests were adopted to study the influence of SBS modifier content on the viscoelastic performance of SBSMA at high to low temperatures. The SBSMA’s microstructure with different contents was investigated using a fluorescence microscope. The results indicated that the change in non-recoverable creep compliance and creep recovery rate was bounded by 4.5% content at high temperatures, with an apparent turning point. The changing slope of content at less than 4.5% was much higher than that of the content greater than 4.5%. At medium temperatures, the fatigue life of SBSMA increased exponentially with the rising modifier content. The rate of increase in fatigue life was the largest as the content increased from 4.5% to 6.0%. At low temperatures, the low-temperature viscoelastic property index G (60 s) of SBSMA decreased logarithmically as the modifier content increased. In terms of the microscopic phase structure, the SBS modifier gradually changed from the dispersed to the continuous phase state with the increasing SBS modifier content.

This paper aims to study the interaction mechanism of waste tire/plastic modified asphalt from the microscopic perspective of molecules. Based on BIOVIA Materials Studio, a classic four-component asphalt model consisting of asphaltene (C₁₄₉H₁₇₇N₃O₂S₂), resin (C₅₉H₈₅NOS), aromatic (C46H50S), and saturate (C₂₂H₄₆) was constructed. Waste tires are represented by natural rubber (NR), which uses cis-1, 4-polyisoprene as a repeating unit. In contrast, waste plastics are characterized by polyethylene (PE), whose optimum degree of polymerization is determined by the difference in solubility parameters. Then, the above molecular models are changed to a stable equilibrium state through the molecular dynamics process. Finally, the interaction process is analyzed and inferred using the indexes of radial distribution function, diffusion coefficient, and concentration distribution; further, the interaction mechanism is revealed. The results show that the optimal degree of polymerization of PE is 12, so the solubility parameter between PE and NR-modified asphalt is the lowest at 0.14 (J/cm³) ^1/2. These models are in agreement with the characteristics of amorphous materials with the structures ordered in the short-range and long-range disordered. For NR-modified asphalt, the saturate moves fastest, and its diffusion coefficient reaches 0.0201, followed by that of the aromatic (0.0039). However, the molecule of NR ranks the slowest in the NR-modified asphalt. After the addition of PE, the diffusion coefficient of resin increased most significantly from 0.0020 to 0.0127. NR, PE, and asphaltene have a particular attraction with the lightweight components, thus changing to a more stable spatial structure. Therefore, using NR and PE-modified asphalt can change the interaction between asphalt molecules to form a more stable system. This method not only reduces the large waste disposal task but also provides a reference for the application of polymer materials in modified asphalt.

Durability is one important problem that pavement engineers need to address in pavement’s long service life. Furthermore, easily recycled pavement materials, and safe and efficient pavement construction are also important areas for development in road engineering. For these reasons, a new asphalt steel plastic (ASP) pavement structure was proposed with an asphalt mixture forming the surface layer, and steel plate and plastic materials functioning as the main load-bearing layers. Based on a comprehensive performance review and cost-benefit analysis, stone mastic asphalt (SMA) is recommended to be used as the surface layer; and A656 steel plate and acrylonitrile butadiene styrene (ABS) plastic materials should be the main load-bearing layer, on top of a foundation layer made with graded crushed stones. A glass fiber reinforced polymer (GFRP) insulation layer is recommended for use between the steel plate and ABS. Mechanical properties of the ASP pavement were analyzed using the finite element method. Laboratory tests were conducted to verify the thermal insulation performance of GFRP, the high-temperature stability and the fatigue resistance of ASP pavement. Results show that some of the mechanical properties of ASP pavement (with a structure of 80 mm SMA asphalt mixture, 8 mm steel plate, 140 mm ABS and 200 mm crushed stones) are comparable with conventional long-life pavement (with 350 mm asphalt layer overlaying 400 mm graded crushed stones). Dynamic stability of the ASP slab specimens can reach 10,000 times/mm, and the fatigue life is about twice that of SMA. Besides, the ASP pavement can be prefabricated and assembled on-site, and thus can greatly improve construction efficiency. From the lifecycle perspective, ASP pavement has many advantages over traditional pavements, such as durability, lower environmental footprint and recyclability, making it is worth further research.

Asphalt pavements are prone to cracking in low-temperature environments, and microwave heating (MH) can heal the cracks effectively. This research mainly investigates the different MH effects on the self-healing properties of asphalt mixtures. With this objective, the three-point splitting test is conducted to generate the cracks. A microwave oven is employed to heat the samples, and a thermal camera measures the surface temperature. Results indicate that heating power and time show a positive linear correlation with healing efficiency, and the HI of the samples can reach over 80%. The HI of the samples decreases with the heating cycle, but the sample with reasonable power and time still has a HI higher than 70% after 5 cycles. The temperature peaks on thermal images indicate that uneven heating exists during heating, but the heating uniformity is within an acceptable range. The healing efficiency level (HEL) suggests that asphalt mixtures have very low inefficient healing behavior if the heating time is below 45 s, but HEL can reach 86.14% at 700 W after 60 s. Furthermore, although the HI of strength shows ideal results, the recovery of other crack parameters, including stiffness, fracture energy, flexible index, and crack resistance index, are not satisfactory.

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.

This study presents a deep-learning method for characterizing carbon fiber (CF) distribution and predicting electrical conductivity of CF-reinforced cement-based composites (CFRCs) using scanning electron microscopy (SEM) images. First, SEM images were collected from CFRC specimens with different CF contents. Second, a fully convolutional network (FCN) was utilized to extract carbon fiber components from the SEM images. Then, D_SEM and D_sample were used to evaluate the distribution of CFs. D_SEM and D_sample reflected the real CF distribution in an SEM observation area and a specimen, respectively. Finally, a radial basis neural network was used to predict the electrical conductivity of the CFRC specimens, and its weights (d_i) were used to evaluate the effects of CF distribution on electrical conductivity. The results showed that the FCN could accurately segment CFs in SEM images with different magnifications. D_sample could accurately reflect the morphological distribution of CFs in CFRC. The electrical conductivity prediction errors were less than 6.58%. In addition, d_i could quantitatively evaluate the effect of CF distribution on CFRC conductivity.