The compatibility between waste plastic polymers and bitumen is the most challenging issue hindering the improvement of modified bitumen performance. The current practice of recycled waste plastics includes the use of polyvinyl chloride (PVC), polypropylene (PP), polyethylene (PE), etc. This study was designed to investigate the compatibility of different waste plastic polymers with bitumen binders by conducting molecular dynamics (MD) simulations at different temperatures. The molecular models of these materials were constructed in this study for the compatibility analysis, and they include the base bitumen, polymers (PVC, PP, and PE), polymer- bitumen blending systems. Using the output and related calculations of these MD models, the properties of these blending systems were measured at different temperatures through the calculation of the solubility parameter (δ) and interaction energies. The compatibility analysis is discussed in the context of these simulation results. The simulation results for the solubility parameters and interaction energies show consistent trends. The results showed that PVC and PP had better compatibility with bitumen at 433.15 K and that PE and bitumen had good compatibility at 393.15 K. Moreover, it can be deduced that the order of compatibility of the three polymers with bitumen is as follows: PVC > PE > PP. In addition, these research results can be referenced for the industry and research development of modified bitumen.

The textural roughness of asphalt pavement surface is an important indicator to characterize pavement skid resistance. In this paper, multi-visual technology was applied to capture the surface image of asphalt pavement which was transformed into a visualized 3D point cloud model. Then, based on the principle of the digital elevation model (DEM), the disordered 3D point cloud is rasterized and projected into a 2D matrix which contains generalized point cloud elevation information. Meanwhile, the 2D matrix is transformed into grayscale to build the equivalent grayscale image. Furthermore, the fractal dimensions were calculated in terms of one-dimensional pavement section profile, two-dimensional grayscale, and equivalent grayscale to characterize the pavement roughness. The results showed that the fractal dimensions are positively correlated with the mean texture depth (MTD), and the fractal dimension of equivalent grayscale has the best correlation with MTD. It should be highlighted that the equivalent grayscale image is directly transformed by the reconstruction of the three-dimensional point cloud, and the grayscale value of each point can represent the elevation of different pavement surfaces. Therefore, the equivalent grayscale image can better reflect the real roughness of the pavement surface. Meanwhile, the proposed method in this paper can effectively reduce the influence of some factors (e.g., light and color, etc..) on the texture detection of the pavement surface.

According to the latest statistics at the end of 2022, the total length of highways in China has reached 5.3548 million kilometers, with a maintenance mileage of 5.3503 million kilometers, accounting for 99.9% of the total maintenance coverage. Relying on inefficient manual pavement detection methods is difficult to meet the needs of large-scale detection. To tackle this issue, experiments were conducted to explore deep learning-based intelligent identification models, leveraging pavement distress data as the fundamental basis. The dataset encompasses pavement micro-cracks, which hold particular significance for the purpose of pavement preventive maintenance. The two-stage model Faster R-CNN achieved a mean average precision (mAP) of 0.938, which surpassed the one-stage object detection algorithms YOLOv5 (mAP: 0.91) and YOLOv7 (mAP: 0.932). To balance model weight and detection performance, this study proposes a YOLO-based optimization method on the basis of YOLOv5. This method achieves comparable detection performance (mAP: 0.93) to that of two-stage detectors, while exhibiting only a minimal increase in the number of parameters. Overall, the two-stage model demonstrated excellent detection performance when using a residual network (ResNet) as the backbone, whereas the YOLO algorithm of the one-stage detection model proved to be more suitable for practical engineering applications.

The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior optimization of the L-ANN model. Subsequently, by incorporating structures such as self-attention mechanism, a novel predictive approach based on the Transformer architecture was proposed. The objective is to devise a more accurate predictive methodology for pavement performance indices, with the goal of guiding pavement maintenance and management efforts. Experimental results indicate that, through comparative analysis of three quantitative evaluation metrics (root mean square error, mean absolute error, coefficient of determination), along with visual scatter plots, the predictive model employing the fused Transformer architecture demonstrates higher robustness and accuracy within the domain of pavement performance prediction when compared to the L-ANN model. This outcome substantiates the efficacy and superiority of the model in terms of predictive performance, establishing it as a reliable tool for accurately reflecting the evolution of asphalt pavement performance. Furthermore, it furnishes a theoretical reference for determining optimal preventive maintenance timing for pavements.