Asphalt pavement distress detection plays a pivotal role in highway maintenance, providing an essential basis for optimizing maintenance strategies and allocating funding. Consequently, quick detection and efficient identification of distress are crucial for enhancing the quality of highway maintenance. This study aims to acquire high-precision distress data using 3D laser point cloud technology, identify distress types via the YOLO algorithm, and extract geometric features such as length and angle. Specifically, a recognition method based on 3D laser point cloud images is proposed, where point cloud data are converted into planar images for processing. Experimental results indicate that the laser point cloud detection achieves millimeter-level precision, the distress recall rate exceeds 85%, and the identification precision reaches 79.5%, demonstrating satisfactory detection accuracy and efficiency.

In order to control the quality of the project and reduce the impact of asphalt pavement segregation on the early road service life, this paper investigates the asphalt mix paving uniformity. First of all, based on the theory of form-centered method, the article proposes an evaluation method using weighted form-centered distance coefficient of variation to evaluate the uniformity of asphalt mixture. Then, a series of operations such as preprocessing and image segmentation were performed based on digital image technology on the image data collected from the pavement construction site, and the data calculation results of HT, an evaluation index of asphalt mixture uniformity, were obtained. Finally, the construction depth ratio evaluation index was corrected, and the link between the asphalt mixture paving uniformity evaluation index HT and the construction depth correction value XTD was studied based on the field test and correlation analysis. The results surface, using image processing method and form-centered evaluation theory to obtain the weighted form-centered coefficient of variation HT and the corrected value of constructive depth have a significant correlation, when the coefficient of variation of uniformity HT is in the range of 0.307~2.143, it can be considered that the asphalt mixture in this area does not segregate, and the ranges of mild segregation and moderate segregation are 2.1431~4.026 and 4.026~6.460, respectively. When the weighted form-center distance coefficient of variation HT is greater than 6.460, it can be considered that the asphalt mixture in the detection area has produced heavy segregation; this study is of great significance for ensuring road construction quality and improving pavement construction technology.

The long-term performance trends of asphalt pavements are pivotal to the concerns of road maintenance authorities. However, predicting the longevity of asphalt pavement performance accurately is a complex challenge due to the multitude of factors influencing its deterioration. This paper constructs a predictive model for long-term behavior of asphalt pavements grounded in the mechanistic-empirical methodology. An extensive observation was conducted on highways, where data on the pavement condition index and the road quality index were meticulously gathered. Subsequently, the model’s life factor and shape factor parameters were corrected. The findings indicate that the predictive methodology derived from mechanistic-empirical methodology for asphalt pavement performance exhibits commendable accuracy and good generalization capabilities

Internal disease in asphalt pavement is a crucial indicator of pavement health and serves as a vital basis for maintenance and rehabilitation decisions. It is closely related to the optimization and allocation of funds by highway maintenance management departments. Accurate and rapid identification of internal pavement diseases is essential for improving overall pavement quality. This study aimed to identify internal pavement diseases using deep learning algorithms, thereby improving the efficiency of determining internal pavement diseases. In this work, a multi-view recognition algorithm model based on deep learning is proposed, with attention fusion mechanisms embedded both between channels and between views. By comparing and analyzing the training and recognition results of different neural networks, it was found that the multi-view recognition algorithm model based on attention fusion demonstrates the best performance in identifying internal pavement diseases.

In order to investigate the effect of high temperature on the performance of three high viscous asphalt in company with various aging degrees (ADs), multiple stress creep recovery (MSCR) test was employed. CRR and irrecoverable compliance were also carried out to measure the high temperature resistance of high viscous asphalt to external loading in the presence of various aging conditions (ACs) as well as to examine the sensitivity of high viscous asphalt towards the stress. The findings of the study demonstrated that after short- and long-term aging, the high-viscosity asphalt turned harder. Also, type I high-viscosity asphalt was observed to have the greatest deformation resistance acted upon by various stress fields and ACs. Furthermore, in company with various ACs, while the average creep recovery rate (CRR) of Type I and Type II high-viscosity asphalt was found to be higher than that of the SBS modified asphalt, the average irrecoverable compliance was observed to be smaller. This indicated the significant effect of high-viscosity modifiers on the improvement of the elastic recovery performance of asphalt and its resistance to high temperature flow deformation. Contrary to the order of their CRRs, the magnitude of stress sensitivity of the three high-viscosity asphalt at various ADs was observed to be type II < type I < SBS modified asphalt, indicating a good negative correlation between stress sensitivity and average CRR. Furthermore, the obtained results revealed that, in the presence of various ACs, the high temperature viscoelastic qualities of high viscosity modified asphalt is considered by the MSCR test. Also, to comprehensively evaluate the high temperature performance of high viscosity modified asphalt at various Ads, the use of such indexes as the average CRR, average irrecoverable compliance and stress sensitivity coefficient were essential.

公路沿线设施的尺寸特征是公路资产管理的关键指标,为准确提取公路沿线设施的尺寸,以三维激光点云与全景相机装置为核心,集成研发公路沿线设施巡检装备,开展检测速度对尺寸特征提取的对比分析实验.研究结果表明,检测速度对尺寸提取的平均测量误差和最大测量误差影响较大,呈正相关的关系.

针对三维探地雷达传统计算方法中直接取芯法和振幅全反射法存在代表性差、计算效率低等问题,文中对直接取芯和振幅全反射两种计算方法的优点与缺陷进行分析,结合三维探地雷达的电磁波在路面内部传播的几何关系,提出一种基于频率步进法(SFL)的计算模型.选取杭绍台高速公路K91+600～K101+700段开展沥青路面厚度和介电常数测试,比较不同方法的计算精度,以验证频率步进法计算模型的准确性.研究结果表明:频率步进法对于路面面层厚度和介电常数的测量误差分别为3.85%和4.69%,相对于振幅全反射法,路面厚度和介电常数测量误差分别降低6.37%和10.59%.频率步进法具有较高的检测精度,能为沥青路面厚度和介电常数测量提供新的计算途径.

There will be various apparent diseases on the road, which will affect the driving safety and cause economic losses, so it is very important to maintain the road. Researchers need to transfer the classification model of road diseases across scenes, but the data of road diseases in the target scene are few and only partially marked, so the existing transfer learning method has poor effect. Aiming at the problem of cross-scene transfer of road disease classification model, the DNN-Road algorithm is proposed in this paper. The experimental results show that the DNN-Road algorithm achieves the best results among the mainstream transfer learning algorithms.

Due to the high labeling cost, there are few available labeled data in the transportation field. At the same time, the characteristics of asphalt pavement cracks are not obvious and the similarity of different categories is high, which makes the task of asphalt pavement crack identification and classification more difficult. In order to realize the rapid and accurate identification of cracks in large-scale asphalt pavement data, a two-stage training method of self-supervised pre-training and supervised fine-tuning and a multi-branch network integrating multiple attention are proposed in this paper. Experiments show that our method can significantly improve the accuracy of asphalt pavement crack classification.

The images in the pavement distress dataset contain complex backgrounds, which makes manual identification more time consuming. In addition, manual identification requires expert experience and knowledge, which is inefficient and expensive. However, the general distress detection framework based on deep learning loses too much surface feature information, which is essential for crack detection. Therefore, we design an attention module that fuses spatial information and channel information and a feature fusion module that is good at integrating surface feature information. Experiments show that our simple method achieves good performance on the pavement distress dataset.

The radar dataset collected by the three-dimensional ground-penetrating radar is presented as multiple views, which is difficult to analyze manually. Disease detection based on multi-view radar maps extremely requires expert experience and knowledge. The high cost of labeling results in a small number of samples, which makes the task more difficult. One solution to this problem is to create a deeper network to extract disease features, but this is not conducive to practical use. Therefore, we propose a two-stage attention fusion and distillation model for multi-view road disease detection, which enables us to make full use of multi-view datasets and improve their practical application in road detection. Experiments show that our model can use fewer parameters and calculations to achieve high accuracy on both original and enhanced datasets.

Fiberglass prepared from broken waste glass can be used in epoxy asphalt mixtures for performance enhancement and a toughening effect. There is no systematic study on the influence mechanism of the size and the amount of glass fiber on the properties of epoxy asphalt mixtures. The effects of fiberglass on the properties of epoxy asphalt concrete were evaluated using a tensile test, three-point bending test, four-point bending fatigue test and an SEM scanning test. The results verify that the tensile strength of epoxy asphalt mastic with a 6 mm length and 2% content increased the most. Compared with the nondoped glass fiber, it increased by 69.2%. Under the influence of the internal composition of the asphalt mixture, the optimal ratio scheme is different from that of epoxy asphalt mastic. A microscopic analysis showed that uniformly dispersed fiberglass in the epoxy asphalt mixture forms a spatial network structure, leading to reinforcement and the restraint of microcrack expansion. The addition of fiberglass with a length of 9 mm and at a concentration of 5% to the epoxy asphalt mixture resulted in the maximum road performance. The Marshall stability increased by 43.5%, and the flexural and tensile strength increased by 33.7%. The fiberglass length is the most important factor limiting the strength and toughening effects of epoxy asphalt mixtures.

Titanium dioxide (TiO2) was recently employed to apply onto road surfaces to degrade the harmful compounds from vehicle emissions. However, it remains a challenging task to find a highly compatible pavement type for TiO2 application to achieve durable and efficient air-purifying performance. This study proposed to coat TiO2 particles onto semi-flexible pavement surface and tried to investigate an optimum coating method. Three coating methods, including direct mixing TiO2 (MT) with asphalt mixture, spraying dry TiO2 (ST) coating and water-solution-based TiO2 (WT) coating on semi-flexible pavement surface. To achieve this objective, semi-flexible samples were prepared to evaluate and compare the performances of three coating methods by employing resistance to wearing, NO removal efficiency tests and residual texture depth tests. It was found that the ST method not only provided better NO degrading efficiency but also improved the resistance to wearing than the other two methods.

通过智能手机自身携带的传感器采集振动数据具有高效和简便的优势,为探究智能手机在车辆上的放置方式对振动数据采集质量的影响,实验以安卓智能手机为采集终端,通过智能手机采集车辆不同固定位置和放置姿态下的振动数据,分析了振动数据的采集质量.实验结果表明:车头部位的振动数据变异系数最小,数据的偏离程度最低,采集系统的稳定性和数据质量最好.智能手机在竖直姿态下采集的数据质量均高于水平姿态.在车头部位竖直采集振动数据是最佳的放置方式.

为了获得催化活性高、抗磨耗性能强的光催化复合材料,研究通过冷-碱腐蚀处理手段和高温黏附技术,制备空心玻璃微珠-纳米TiO2光催复合材料.利用扫描电子显微镜(SEM)、X射线衍射(XRD)和UV-Vis等设备,对样品进行表征.以汽车尾气为降解对象,采用搓揉试验机和自制的环境测试系统,分别测试复合材料的抗磨耗性能与光催化效能.结果表明,纳米TiO2能够较好地附着到空心玻璃微珠表面,空心玻璃微珠-纳米TiO2光催化复合材料相对于纯纳米TiO2具有更强的透光能力和光催化降解能力.该复合材料对汽车尾气中的一氧化氮和二氧化氮均有显著的降解效果,氮氧化物的净化效果高于一氧化碳和二氧化硫,具有较好的抗磨耗能力.

通过测量车辆在沥青路面上行驶的振动数据,从而实现沥青路面平整度的预测.而在实际测量过程中,振动信号受到外界环境的影响,掺杂了大量的散粒噪声、热噪声、高斯噪声,严重影响了路面平整度的计算与评价.实验采用最小二乘法进行趋势项消除,通过功率谱分析不同滤波方法的处理结果,确定出卡尔曼滤波技术对沥青路面振动信号的降噪效果.结果 表明:卡尔曼滤波降噪技术在振动信号处理中不仅能消除外部干扰噪声,还能较大程度地保持原有信号的特征和强度.验证了卡尔曼滤波降噪技术的可行性与可靠性.

以半柔性路面为母体,采用不同的方式将纳米TiO2负载到半柔性路面上,开展了一系列基本路用性能试验,研究负载方式对路用性能的影响.结果表明:将纳米TiO2替代矿粉拌和到沥青混合料中,会出现高温力学性能下降、低温性能改善、残余空隙率降低等现象.喷洒嵌入式和水溶液涂覆式相对于拌和式,高温下的动稳定度提高3 500次/mm,低温平均劲度模量分别增加24.10％和16.40％.因此,喷洒嵌入式和水溶液涂覆式的高温性能较好,低温性能较差;而拌和式则与之相反,高温稳定性较差,低温抗裂性较好.

为了掌握水溶液涂覆式玻璃微珠-纳米TiO2复合材料的汽车尾气降解效能,选择美国进口的波特Q-CEL5020型玻璃微珠和国产的锐钛矿型纳米TiO2为原材料,制备了掺量分别为2％,4％,6％,8％和10％的玻璃微珠-纳米TiO2复合材料,通过自制的环境试验测试设备,研究了水溶液涂覆式复合材料对汽车尾气氮氧化物浓度降解的规律,计算评价了NO,NO2,NOx气体在单阶段和多阶段的降解效能,分析了玻璃微珠-纳米TiO2对尾气降解效率和衰减率的影响,探讨了水溶液涂覆方式下玻璃微珠-纳米TiO2复合材料的最佳掺量.试验结果表明:单阶段汽车尾气降解过程中,玻璃微珠-纳米TiO2复合材料对氮氧化物的降解效果非常显著,平均氮氧化物的转化率达到88.5％;多阶段循环降解方式下,该复合材料对NO的持续降解能力高于NO2和其他NOx气体.水溶液涂覆方式下,玻璃微珠-纳米TiO2复合材料最佳掺量为8％.

介绍了3种常用的描述沥青结合料线性黏弹流变特性的数学模型:Sigmoidal、GLS和CAM模型,并比较分析了3种数学模型的优缺点.应用3种数学模型分别对15 #、50 #、70#沥青、SBS改性沥青以及纳米二氧化硅-SBS复合改性沥青5种沥青结合料的复数剪切模量主曲线进行拟合分析.研究结果表明:采用合理的数学模型对复数剪切模量的主曲线进行拟合能够预测沥青结合料的流变性;该文介绍的3种模型均能对沥青结合料的复数剪切模量主曲线进行很好的拟合,其中Sigmoidal模型能够很好地预测材料的高温性能,但对于改性沥青有一定的局限性;GLS模型对于改性沥青有更强的适应性,且能很好地区分各种沥青结合料流变性差异;CAM模型不能直接预测沥青材料高温性能,但能很好地预测各种沥青结合料频率敏感性.

针对目前沥青混合料均匀性定义不清、评价指标不健全和均匀等级划分存在的问题,利用规范对物质均匀性的定义和材料力学对物质均匀性的假设,将沥青混合料定义为组分均匀、质量分布均匀和刚度均匀,建立了沥青混合料均匀性多指标评价模型,给出了沥青混合料均匀性计算方法和均匀性等级划分依据.开展了AC 13级配的沥青混合料均匀性实验,测试了沥青混合料的各体单元的毛体积相对密度、弹性模量特征值和沥青含量,研究了沥青混合料均匀性,并与传统均匀性评价进行对比分析.结果 表明:基于多指标的沥青混合料均匀性评价方法增加了沥青混合料的组成成分和力学性能指标,评价结果更加客观和准确,AC-13级配的沥青混合料的综合均匀指数为0.896,属于中度均匀;采用传统均匀性方法评价,沥青混合料的压实度和密度变异系数很小,均匀性很好,但合格率仅为63.2％.