Fiber-reinforced asphalt mixture has been widely used in pavement engineering to not only prevent asphalt binder leakage but also improve engineering properties of asphalt mixture. However, the research on three key parameters, namely fiber type, fiber length, and fiber content, which significantly affect the performance of fiber-reinforced asphalt mixture, have seldom been conducted systematically. To determine these three key parameters in the support of the application of fibers in mixture scientifically, three commonly used fibers were selected, basalt fiber, polyester fiber, and lignin fiber, and the testing on fibers, fiber-reinforced asphalt binders, and fiber-reinforced asphalt mixtures was conducted afterwards. The results showed: the favorable fiber type was basalt fiber; the favorable basalt fiber length was 6mm; the engineering properties including high temperature stability, low temperature crack resistance, and water susceptibility were clearly improved by the added basalt fiber, and the optimum basalt fiber content was 0.4 wt.%. The obtained results may be valuable from a practical point of view to engineers and practitioners.

Patching is one of the most common maintenance methods for potholes in roads. In order to improve the performance of cold-patching asphalt mixtures, an emulsified asphalt modified with waterborne epoxy resin was developed. Two waterborne epoxy resins and two curing agents were selected. The optimal experimental contents of the curing agents were obtained by measuring the compressive strength of the waterborne epoxy mortar (WEM) under different curing agent contents and curing period. The difference between the two waterborne epoxy resins was obtained by the flexural strength and stress–strain curves, which were measured by the modified bending test on the WEM. The evaluation method of the initial strength and forming strength of the waterborne epoxy emulsified asphalt mixture (WEEAM) was proposed by the experimental study of the compaction molding method and curing conditions. The high temperature performance, low temperature performance, and moisture susceptibility of the mixture were verified by comparing various kinds of WEEAM. The results show that using WEEAM as a road repair material has great advantages in improving pavement performance and road service levels.

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.

The objective of this study is to investigate the storage stability and rheological property of bio-oil/lignin composite-modified asphalt. The composite-modified asphalt with different proportions of bio-oil was prepared and cured at 105 °C, 135 °C, and 165 °C for 24 h and 48 h. The storage stability of the composite-modified asphalt was evaluated based on the softening point difference, the storage stability index derived from rotational viscosity, the segregation rate based on temperature sweep, and the non-recoverable creep compliance measured through the Multiple Stress Creep Recovery test. The storage stability of bio-oil/lignin composite-modified asphalt was evaluated through testing and analysis of its infrared spectroscopy and scanning electron microscopy before and after thermal storage. The research results indicate that the maximum difference in softening point is 0.9 °C, and the calculated storage stability index is generally below 0.1. The maximum value of the segregation rate is 0.43, indicating excellent storage stability of the bio-oil/lignin composite-modified asphalt. According to the results from infrared spectroscopy, no chemical reactions occurred during the storage process of the composite-modified asphalt. The scanning electron microscope confirmed that the samples became more stable after 48 h of storage.

The Qinghai–Tibet Plateau (QTP) is the highest altitude plateau in the world, characterized by strong solar radiation and large diurnal temperature differences and so on, which brings a great negative impact on the temperature and thermal stress field of asphalt pavement. The purpose of this study is to analyze the temperature field and thermal stress status of asphalt pavement in the QTP to provide a reference for pavement design and maintenance in high-altitude areas. The finite element method was applied to establish the temperature field model to study the distribution and variation of pavement temperature. On this basis, the influence of cooling amplitude on pavement thermal stress was studied during cold waves. In addition to this, the key internal factors affecting the thermal stress of pavement, such as surface thickness, surface temperature shrinkage coefficient, surface modulus, and base modulus, were analyzed by an orthogonal test. It was found that temperature and solar radiation have a significant effect on the pavement temperature field. When the cold wave came, the cooling rate had a considerable impact on the thermal stress of the pavement, that is, every 5 °C increase in cooling rate would increase the thermal stress by more than 50%. The temperature shrinkage coefficient and surface modulus of the surface layer material had the greatest influence on the pavement thermal stress. The thermal stress could be reduced by more than 0.4 Mpa for every 5 × 10⁻⁶/°C reduction in the surface temperature shrinkage coefficient or every 1000 Mpa reduction in the surface modulus. This study can provide a reference for improving the temperature field and thermal stress field of asphalt pavement in the plateau area.

With the aging of transportation infrastructure and the increasing frequency of use, the detection and identification of cracks in asphalt pavements are crucial for ensuring road safety and maintenance efficiency. Traditional manual inspection methods are not only inefficient and limited in accuracy but also susceptible to subjective factors and environmental conditions. In contrast, machine vision-based crack detection technology enhances the efficiency and reliability of detection through automated image acquisition and analysis processes. This article reviews the latest advancements in machine vision-based crack detection technology for asphalt pavements, with a particular focus on the applications of digital image processing and deep learning. Although image processing-based methods perform well in detecting cracks against simple backgrounds, they exhibit poor robustness under complex lighting and background conditions. On the other hand, deep learning-based methods, while effectively handling complex image data, rely on large amounts of annotated data and significant computational resources. Through critical analysis, the article evaluates the strengths and weaknesses of existing technologies and looks forward to future research directions that integrate multiple sensing data and automated data annotation tools, aiming to further advance and innovate road maintenance technology.