The sustainable utilization of multiple reclaimed pavement materials is a critical pathway toward green highway construction. This study investigates the performance and synergistic mechanisms of cold-recycled mixtures incorporating both Reclaimed Asphalt Pavement (RAP) and Reclaimed Cement-Stabilized Base (RCSB), using emulsified asphalt as the primary binder. A comprehensive experimental program was conducted to evaluate the effects of reclaimed material proportions, mixing sequences, and curing ages on the mechanical strength, moisture susceptibility, and high-temperature stability of the mixtures. Microscopic characterization via Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS) were employed to elucidate the Interfacial Transition Zone (ITZ) evolution. Results indicate that an optimal RCSB incorporation range of 20–40% establishes a robust “stone-to-stone” rigid skeleton, significantly enhancing the splitting strength (up to 0.87 MPa) and durability (Splitting Strength Ratio, TSR > 91%). SEM observations confirm the formation of a dense interpenetrating network structure within this range, where cement hydration products and asphalt films achieve optimal chemo-physical bonding. Exceeding 40% RCSB leads to a moisture-starved state and a sharp decline in dynamic stability due to insufficient binder coating. Micro-morphological characterization reveals that the transition from macro-interfacial debonding to a robust cohesive failure mode is the fundamental driver for the performance peak at 20–40% RCSB. SEM observations confirm the formation of a dense interpenetrating network structure, where cement hydration products successfully anchor into the asphalt film. This optimized ITZ effectively eliminates the stress concentration and aggregate crushing seen in high-RAP mixtures, thereby ensuring superior mechanical integrity. Furthermore, a pre-wetting mixing sequence ensures a high-energy mineral surface that promotes the heterogeneous nucleation of cement. SEM results show that this prevents the competitive adsorption between cement and asphalt, transforming the ITZ from a friable, loose state into a densified crystalline adhesive matrix.

Conventional maintenance models often neglect the impact of pre-existing rutting on sealcoat performance, particularly in high-temperature regions like Chongqing in China, where rut-related failures are common. Existing ruts impose geometric constraints that significantly alter stress redistribution within the new sealcoat layer, yet this constraint mechanism remains poorly understood due to limitations in laboratory observation. This study developed a mesoscopic AC16 + MS3 composite discrete element model to simulate the mechanical behavior of a sealcoat applied over a rutted pavement. To replicate real-world conditions, a constant pressure of 0.7 MPa, representing the standard tire ground pressure in JTG E20-2011, was applied at a temperature of 70 °C, reflecting extreme high-temperature stability limits. Virtual rutting tests and contact force chain analyses were conducted across varying existing pavement rut depths, including 0 mm, 3 mm, 6 mm, and 10 mm. The results indicate that existing ruts redirect stress transfer paths, causing vertical compressive force chains to densify within the rutted zone and tensile stress to concentrate at rut edges. Mastic-mastic contacts transmit over 65% of the load, identifying asphalt mortar as the primary load-transfer phase. Notably, a 10 mm existing rut depth induces a tensile vacuum zone at depths of 15–40 mm, disrupting the standard U-shaped stress distribution. These findings clarify how pre-existing geometries govern structural degradation, suggesting that maintenance in high-temperature regions must prioritize asphalt mortar performance to mitigate edge cracking and deformation.

To address the issue of cracking damage under extreme high-temperature rutting, which is not sufficiently considered in the selection of preventive maintenance programs, the objective of this study was to investigate the preventive maintenance-oriented minor internal damage changes in asphalt concrete with a normal maximum aggregate size of 16 mm (AC-16) under extreme high temperature (70 °C) and load (1.4 MPa) conditions. The changes in void structure within the 0–10 mm rutting depth were tracked through the rutting test and Computer Tomography (CT) image analysis. It was observed that there were notable discrepancies in the three-dimensional (3D) space distribution of void, void volume development, and void morphology between the rut impact zones and the rutted part. The impact zone exhibited a greater prevalence of voids and an earlier onset of cracking. At a rutting depth of only 5 mm, multiple top-down developed cracks (TDCs) of over 6 mm length were observed in the impact zone. At a rutting depth of 10 mm, the TDCs in the impact zone were more numerous, larger, and wider, indicating the necessity for a tailored repair program that includes milling. TDC damage caused by high-temperature rutting is predominantly observed in the upper and middle positions of the height direction, with the bottom position data exhibiting greater inconsistency due to the influence of molding. Furthermore, the combination of void morphology indicators with void volume can effectively track the occurrence and development of microcracks. However, the fine-scale assessment of compaction degree and deformation process using the equivalent void diameter indicator is not sufficiently differentiated.

Automated pavement distress detection (PDD) is critical for the structural health monitoring (SHM) of transportation infrastructure, yet existing methods struggle with real-time multi-target detection under resource constraints. In this paper, YOLOv8-PDD was constructed based on YOLOv8 by introducing the large separable kernel attention (LSKA) mechanism module into the Spatial Pyramid Pooling—Fast (SPPF) module, replacing Complete-IoU (CIoU) loss with Distance-IoU (DIOU) loss as the loss function, and adopting Soft-Non-Maximum Suppression (NMS) to replace the original NMS algorithm. The proposed YOLOv8-PDD achieved 78.3% mean average precision with intersection over union above 0.5 (mAP@0.5 +8.1%) with a minimal complexity increase of +0.2 GFLOPs compared to the baseline YOLOv8n model. While incurring a negligible increase in latency (+0.09 ms), YOLOv8-PDD significantly outperforms YOLOv8n in detection accuracy (mAP@0.5 +8.1%), offering a superior accuracy–efficiency trade-off for real-time applications. YOLOv8-PDD performed well in detecting all categories, with AP values above 75% except for transverse crack and strip patch. Significant improvements in pothole detection AP@0.5 (+22.1%) and strip patch detection AP@0.5 (+17.7%) indicate superior small target and complex background adaptability. Our model achieved a detection efficiency of 68 frames per second (FPS) on consumer-grade CPUs (OpenVINO-optimized), outperforming 10 models (e.g., YOLOv5n and RTDETR-l) in accuracy–speed balance.