The bonding performance of epoxy resin adhesives plays a critical role in ensuring interlayer adhesion and long-term durability in steel bridge deck pavements. However, the construction environment temperature and curing time significantly influence the bonding properties of epoxy resin adhesives. To address this issue, systematic evaluations of the bonding performance and tack-free time of epoxy resin adhesives were conducted. The results demonstrate that under identical curing durations, the tensile bond strength between the epoxy resin bonding layer and steel plate increases with higher curing temperatures. Similarly, at constant curing temperatures, extended curing times lead to improved tensile strength. Both higher temperatures and longer curing durations reduce the tack-free time of the epoxy resin adhesive. Under natural outdoor curing conditions, the epoxy resin adhesive achieves design requirements for both tensile strength and tack-free time after 48 h of curing, ensuring optimal interlayer bonding and workability. Conversely, prolonged curing beyond 72 h results in significantly reduced bonding strength while maintaining acceptable tack-free time. For ambient temperature conditions, the optimal curing duration for epoxy resin adhesive is determined to be 48~72 h, balancing both bonding performance and construction requirements (preventing adhesion to construction equipment). This research offers technical guidance for the field construction of epoxy pavement on steel bridge decks by establishing optimal curing protocols for epoxy resin adhesives to ensure reliable bonding performance and construction workability.

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements.

Accurate prediction of asphalt mixture density is critical for quality control in pavement engineering. This study develops a novel dielectric-based predictive framework by applying the Lichtenecker–Rother (L-R) dielectric mixing theory to asphalt composites. The model’s key microstructural parameter, the geometric arrangement factor c, was optimized to 0.3 using a combined experimental dataset: laboratory measurements on AC (asphalt concrete) mixtures produced in this study, supplemented with published data from open-graded friction course (OGFC), stone mastic asphalt (SMA), and asphalt mixture (AM) types reported in the literature. The resulting model, termed the Geometric Arrangement Optimization (GAO) model, was systematically compared against three established dielectric models: the complex refractive index method (CRIM), the Rayleigh mixing model, and the Bottcher-type model adapted by Leng et al. (denoted ALL). Validation on a total of 34 sets of laboratory specimens showed that GAO achieved the highest prediction accuracy, with a mean relative error of 1.83% and a coefficient of determination R² of 0.91. When tested on eight independent field cores, GAO maintained reliable performance, yielding a mean relative error of 3.01%. These results indicate that the GAO model provides a physically grounded and practically applicable approach for asphalt mixture density estimation, contributing a useful tool for pavement performance evaluation and quality assurance.