Dandan Cao

The dynamic modulus of asphalt mixtures is a key design parameter in pavement design, which significantly impacts the mechanical properties of asphalt pavements. This study simulated dynamic modulus tests of asphalt mixtures using the three-dimensional (3D) discrete element method (DEM) to investigate mechanical behaviors such as the loading-bearing ratio of individual aggregates. Fine-grained AC-13 and medium-grained AC-20 asphalt mixture models were randomly constructed in the DEM program using user-defined methods. The dynamic modulus and phase angle values of the asphalt mixtures were predicted. By comparing laboratory experiments with DEM simulation results, the model was validated, and the effects of temperature and loading frequency on the dynamic modulus were explored. Further exploration was conducted on the loading-bearing ratio and mechanical interactions among aggregates of different sizes within the mixtures. The results show that the 3D DEM model can accurately predict the dynamic modulus and phase angle of asphalt mixtures. Temperature and frequency have an impact on these parameters, and the increase in gradation has an impact on the loading-bearing ratio, due to the proportion of coarse aggregates.

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 generalized Maxwell (GM) constitutive model has been widely applied to characterize the viscoelastic properties of asphalt mixtures. The parameters (Prony series) of the GM are usually obtained via interconversion between a dynamic modulus and relaxation modulus, and they are then input to a finite element model (FEM) as viscoelastic parameters. However, the dynamic modulus obtained with the common loading mode only provides the compressive and tensile properties of materials. Whether the compression or tensile modulus can represent the shear properties of materials related to flow rutting is still open to discussion. Therefore, this study introduced a novel method that integrates the Kriging model into the genetic algorithm as a surrogate model to determine the viscoelastic parameters of an asphalt mixture in rutting research. Firstly, a wheel tracking test (WTT) for AC-13 was conducted to clarify the flow rutting development mechanism. Secondly, two sets of the AC-13 viscoelastic parameters obtained through the optimization method and the dynamic modulus were used as inputs into the FEM simulation of the WTT to compare the simulation results. Finally, a sensitivity analysis of viscoelastic parameters was performed to improve the efficiency of parameter optimization. The results indicating the viscoelastic parameters obtained by this method could precisely characterize the development law of flow rutting in asphalt mixtures.