Pavement Diseases Research Articles

Experimental study of aggregate skeleton shear properties for asphalt mixture under different compaction stages

The permanent deformation diseases of asphalt pavement, such as rutting and slippage, are mainly influenced by its shear resistance. The shear properties of asphalt mixtures are the macroscopic result of mutual friction and contact between particles. Compared with the compaction degree, the shear properties can more directly reflect the compaction quality of mixtures. Based on the self-developed “asphalt mixture aggregate skeleton shear tester”, this research addressed the challenges in obtaining the compaction performance of asphalt pavement, and the aggregate skeleton shear tests were carried out under different test conditions with AC-13C and asphalt mortar. The test evaluation index was optimized by statistical analysis and ANOVA method, and the test means and coefficients of variation were obtained to evaluate plate-claw combinations and torsional loading speeds. Besides, the shear properties of asphalt mixtures under different test temperatures and compaction numbers were investigated. The results show that the shear failure strength (FP) exhibits the best regular statistics and displays good differentiation for various gradations, moreover, it is recommended to use the combination of 190&10 plate-claw and a loading speed of not more than 3°/min. As the test temperature decreases, the average value of FP increases in a power relationship. In addition, the average value of FP shows a good linear positive correlation to the compaction number. However, the shear strength of asphalt mixtures under the same compaction number still varies greatly, and the coefficient of variation reaches 22.1%. In summary, the effect of compaction test conditions on the shear performance of asphalt mixtures can be better evaluated by the proposed aggregate skeleton shear properties test.

Airport pavement performance evaluation of pavement fog seal based on optimized test technology

Fog seal technology is widely used in airport asphalt pavement. The research on the performance and application of fog seal material is basically at the level of modified emulsified asphalt. The fog seal test method in the existing specification has defects. Three kinds of fog seal materials with different substrates were selected in this paper. Based on the characteristics of fog seal treatment and the relevant specifications of fog seal and airport pavement. Wet Track Abrasion Test (WTAT), Cantabro test, Water seepage test, and Pendulum instrument tester pavement friction coefficient test were improved. Test the pavement performance of different types of fog seals. The results show that the mass loss of material C was less than 10% after 200 and 400 cycles of wear. The improvement effect of material C on raveling resistance and impermeability was far better than that of materials A and B. The improvement of material C on the raveling resistance of the mixture could reach 50%. The immediate improvement effect on the impermeability was nearly 100%. The skid resistance performance of the three fog seal materials decreased after treatment. The decrease in material C was more significant than in materials A and B. In constructing material C, the dose of emery should be appropriately increased. It was advisable to not be less than 0.35 kg/m2. The fog seal layer improves pavement performance. It has reference significance for using fog seal technology to treat early airport pavement diseases.

Image Data Visualization Using T-SNE for Urban Pavement Disease Recognition

Urban pavement disease recognition is for the most part, a mission performed manually. Recently, video analysis task has been one of the most important applications in various fields. Aims to renovate on the automated vision-based disease recognition and to experiment new methods of road disease detection, this paper analyzes the images took from a city and performed image data visualization of road issues. From image features extracted from histogram of oriented gradient, we perform principal components analysis to reduce the dimensionality of features. Results are presented by visualizing datapoints using t-distributed stochastic neighbor embedding. The experiment shows that the image data visualization using t-SNE is suitable for the growing field of urban road disease recognition.

Transferring heat accumulated by asphalt pavement from inside to outside through carbon fibers

On the Qinghai-Tibet Plateau, intense solar irradiation will cause serious heat accumulation in asphalt pavement. Downward heat transfer will disturb the permafrost under subgrade, thus leading to a series of subgrade and pavement diseases. This makes construction of asphalt pavement in the permafrost regions of the Qinghai-Tibet Plateau a world-class challenge. This paper proposes for the first time a transversal heat transfer channel in asphalt pavement, which can quickly transfer heat accumulated in pavement to road shoulders. Finite element software ABAQUS was used to simulate the rutting plate with high axial thermal conductivity carbon fibers inserted transversely. Under the constant heat load of 500 W/m2, the heat flux near the carbon fibers showed the characteristic of convergence. The maximum transverse heat flux in the carbon fibers reached 4.97×107W/m2, which was 14 times that in the asphalt mixture. Meanwhile, the maximum vertical heat flux in the carbon fibers reduced to 2.62×106W/m2, which was 12 % less than the asphalt mixture. The effective thermal conductivity of composite specimens with different carbon fiber parameters was studied through control experiments. When the axial thermal conductivity, volume fraction and number of carbon fiber bundles increased to 100 W/m·°C, 10.72 % and 4 bundles respectively, the transverse ETC of composite specimens increased to 4.05 W/m·°C, 7.09 W/m·°C and 16.18 W/m·°C respectively. They were 3.12 times, 5.45 times and 12.45 times of the control group. The radial thermal conductivity of carbon fibers has a significant effect on the vertical ETC. When it decreased to 0.2 W/m·°C, the vertical ETC decreased by 6.15 % compared with the control group. The temperature variation trend of specimens were measured by a laboratory irradiation experiment. The maximum temperature difference between the control group and the experimental group at 0 mm was 5.81 °C, and the average temperature difference within 2 h was 5.14 °C. The maximum temperature difference at 50 mm was 3.84 °C and the average temperature difference within 2 h was 3.24 °C. The results showed that the transverse heat transfer channel can reduce downward heat transfer. The application of the channels will enhance transverse heat release and relieve heat accumulation of asphalt pavement. This can create a low temperature state for pavement structure and has a positive effect on maintaining the stability of the permafrost regions on the Qinghai-Tibet Plateau.

Influence of Thermo-Oxidative Aging on the Properties of SBS-Modified Asphalt Binders

The superior performance of styrene-butadiene-styrene (SBS) modified asphalt makes it widely used in asphalt pavement construction. However, SBS-modified asphalt pavement still faces an aging problem. Aging causes the performance of asphalt materials to decline and many pavement diseases. The dosage of SBS commonly used in industry is generally between 3% and 4.5%, and the performance of SBS with a high dosage needs to be explored. Therefore, in this paper, 12 modified binders were prepared with blending amounts of 3%, 5%, and 7% and the type of modifiers as linear and star. The softening point increment (SPI), viscosity aging index (VAI), rutting factor index (RFI), complex modulus aging index (CMAI), and phase angle aging index (PAAI) were used to study the antiaging performance of SBS-modified asphalt. Fourier transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) were used to study the microscopic and structural changes that occur during the aging process of modified asphalt. The result shows that when the amount of the same type of modifier is 3%–7%, the higher the amount of modifier, the better the performance of the modified asphalt. At the same content, a star modifier is not necessarily better than a linear modifier. For the same type of modifier, the larger the amount, the better the antiaging performance of the modified asphalt. Given an increase in aging time, the deterioration of the asphalt performance will deepen. Aging leads to the enhanced high-temperature performance of asphalt. The macroscopic performance of asphalt aging can be explained by the microscopic view. The aging process of modified asphalt is the cumulative process of oxidation products.

Application of High-Viscosity Modified Asphalt Mixture in Curved Bridge Pavement

Curved bridge deck pavement materials are easy to be damaged due to their complex stress conditions. Hence, the application of a high-viscosity modified asphalt mixture in curved bridge deck pavement is studied in this paper. Firstly, the severity of pavement diseases on curved bridge decks was introduced and compared with the deck pavement diseases in straight sections. Then, compared with SBS-modified asphalt mixture SMA-13, the advantages of high-viscosity modified asphalt mixture HM-SMA-13 in resistance to deformation, water damage, shear deformation, and fatigue were analyzed. The results show that HM-SMA-13 has 1.6 times the high-temperature deformation resistance, 1.2 times the shear deformation resistance, 1.1 times the low-temperature performance, 1.05 times the water stability, and 7 times the fatigue resistance of the SMA-13 mixture. Finally, HM-SMA-13 was applied to the curved bridge deck pavement. After one year of operation, slight rutting and cracking appear on the bridge deck pavement, which indicated that HM-SMA-13 is a suitable material for curved bridge pavement.

Measurement of Tire-Pavement Contact Tri-Axial Stress Distribution Based on Sensor Array

A tire’s three-dimensional stress for pavement is an important cause of asphalt pavement disease. In order to study the contact stress distribution between the tire and the pavement under real conditions, a sensor that can measure the tri-axial stress synchronously is designed, and a complete measurement system is established. The variation trend and stress value of tri-axial stress under steady rolling of the tire were obtained, and the stress distribution characteristics were analyzed. The results show that the stress in the three directions near the tire shoulder is greater than that in the crown area, and the stress peak moves gradually from front to back with the rolling of the tire. Compared with the simplified simulation model, these results provides valuable suggestions for exploring the real tire-pavement interaction.

A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing

In order to investigate the use of unmanned aerial vehicles (UAVs) for future application in road damage detection and to provide a theoretical and technical basis for UAV road damage detection, this paper determined the recommended flight and camera parameters based on the needs of continuous road image capture and pavement disease recognition. Furthermore, to realize automatic route planning and control, continuous photography control, and image stitching and smoothing tasks, a UAV control framework for road damage detection, based on the Dijkstra algorithm, the speeded-up robust features (SURF) algorithm, the random sampling consistency (RANSAC) algorithm, and the gradual in and out weight fusion method, was also proposed in this paper. With the Canny operator, it was verified that the road stitched long image obtained by the UAV control method proposed in this paper is applicable to machine learning pavement disease identification.

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

Pavement disease detection and classification is one of the key problems in computer vision and intelligent analysis. This is an automated target detection technology with great development potential, which can improve the detection efficiency of road management departments. The research based on the convolutional neural network is aimed at realizing asphalt pavement disease detection based on low resolution, occlusive interference, and complex environment. Considering the powerful function of the convolutional neural network and its successful application in object detection, we apply it to asphalt pavement disease detection, and the detection results are used for subsequent analysis and decision‐making. At present, most of the research on pavement disease detection focuses on crack detection, and the detection of multiclass diseases is less, and its detection accuracy and speed need to be improved, which does not meet the actual engineering application. Therefore, a rapid asphalt pavement disease detection method based on improved YOLOv5s was proposed. The complex scene data enhancement technique was developed, which is used to enhance and extend the original data to improve the robustness of the model. The improved lightweight attention module SCBAM was integrated into the backbone network, which can enhance the feature extraction ability and improve the detection performance of the model for small targets. The spatial pyramid pooling was improved into SPPF to fuse the input features, which can solve the multiscale problem of the target and improve the reasoning efficiency of the model to a certain extent. The experimental results showed that, after the model is improved, the average accuracy of pavement disease reaches 94.0%. Compared with YOLOv5s, the precision of the improved YOLOv5‐pavement is increased by 3.1%, the recall rate is increased by 4.4%, the F1 score is increased by 3.7%, and the mAP is increased by 3.8%. For transverse cracks, longitudinal cracks, mesh cracks, potholes, and repaired pavement, the detection accuracy of pavement disease detection method based on YOLOv5‐pavement is improved by 3.4%, 3.1%, 4.0%, 7.5%, and 4.8%, respectively, compared with that based on YOLOv5s. The proposed method provides support for the detection work of pavement diseases.

Preparation and Performance Analysis of Epoxy Acrylic Resin for Repairing Asphalt Pavement Diseases

Preparation and Performance Analysis of Epoxy Acrylic Resin for Repairing Asphalt Pavement Diseases

Effect of Ultraviolet Aging on Rheological Properties and Microstructure of Rubber Asphalt in High Altitude Area

High altitude and strong ultraviolet radiation in Qinghai Tibet Plateau induces the brittleness and hardening of asphalt, which leads to temperature cracks, pits and other diseases of asphalt pavement. In this paper, the accelerated UV aging test of rubber asphalt was carried out by simulating the UV radiation intensity in Xining, Qinghai Province, and the rheological properties and microstructure changes of rubber asphalt were analyzed. The results show that the complex modulus of rubber asphalt increases exponentially with the extension of UV aging time. The phase angle of rubber asphalt decreases linearly with UV aging, and the deformation recovery performance of rubber asphalt increases; UV radiation has the most significant effect on the micro morphology of rubber asphalt. With the further extension of UV aging time, micro cracks are formed in rubber asphalt, and the low-temperature crack resistance and fatigue failure resistance of rubber asphalt are gradually reduced.

Critical transverse differential settlement between modern tram pile-plank-supported subgrade and surrounding pavement subgrade

Due to the great structural difference between the modern tram pile-plank-supported subgrade and the surrounding pavement subgrade, pavement disease induced by the uncoordinated transverse settlement between these infrastructures continue to appear. A centrifuge model test combined with a finite element method (FEM) was employed to investigate the development law of transverse differential settlement between these infrastructures subjected to long-term operation loads. According to the relationship between horizontal stress and differential settlement rate, the critical transverse differential settlement of tram pile-plank-supported subgrade was proposed. The results showed that the tram track layout, soil stress history and operation time had a great influence on the transverse differential settlement of the tram pile-plank-supported subgrade. During the operation time, the transverse differential settlement rate was linearly related to the horizontal stress of the tram pile-plank-supported subgrade. Finally, according to the requirements of allowable tensile stress of the pavement structure layer, definition of the value of 3.0‰ was suggested as the critical transverse differential settlement rate of the tram pile-plank-supported subgrade.

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques.

The image of expressway asphalt pavement crack disease obtained by a three-dimensional line scan laser is easily affected by external factors such as uneven illumination distribution, environmental noise, occlusion shadow, and foreign bodies on the pavement. To locate and extract cracks accurately and efficiently, this article proposes a three-stage asphalt pavement crack location and segmentation method based on traditional digital image processing technology and deep learning methods. In the first stage of this method, the guided filtering and Retinex methods are used to preprocess the asphalt pavement crack image. The processed image removes redundant noise information and improves the brightness. At the information entropy level, it is 63% higher than the unpreprocessed image. In the second stage, the newly proposed YOLO-SAMT target detection model is used to locate the crack diseases in asphalt pavement. The model is 5.42 percentage points higher than the original YOLOv7 model on mAP@0.5, which enhances the recognition and location ability of crack diseases and reduces the calculation amount for the extraction of crack contour in the next stage. In the third stage, the improved k-means clustering algorithm is used to extract cracks. Compared with the traditional k-means clustering algorithm, this method improves the accuracy by 7.34 percentage points, the true rate by 6.57 percentage points, and the false positive rate by 18.32 percentage points to better extract the crack contour. To sum up, the method proposed in this article improves the quality of the pavement disease image, enhances the ability to identify and locate cracks, reduces the amount of calculation, improves the accuracy of crack contour extraction, and provides a new solution for highway crack inspection.

Pavement Disease Detection through Improved YOLOv5s Neural Network.

An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introduced into the YOLOv5s network to reduce the FLOPs (floating-point operations) in the feature channel fusion process. Mosaic data augmentation is also added to improve the feature expression performance. A public road disease dataset is reconstructed to verify the performance of the proposed method. The proposed model is trained and deployed to NVIDIA Jetson Nano for the experiment, and the results show that the average accuracy of the proposed model reaches 88.17%, increased by 4.01%, and the model FPS (frames per second) reaches 12.51, increased by 184% compared with the existing YOLOv5s. Case studies show that the proposed method satisfies the practical application requirements of pavement disease detection.

Automatic Defect Detection of Pavement Diseases

Pavement disease detection is an important task for ensuring road safety. Manual visual detection requires a significant amount of time and effort. Therefore, an automated road disease identification technique is required to guarantee that city tasks are performed. However, due to the irregular shape and large-scale differences in road diseases, as well as the imbalance between the foreground and background, the task is challenging. Because of this, we created the deep convolution neural network—DASNet, which can be used to identify road diseases automatically. The network employs deformable convolution instead of regular convolution as the feature pyramid’s input, adds the same supervision signal to the multi-scale features before feature fusion, decreases the semantic difference, extracts context information by residual feature enhancement, and reduces the information loss of the pyramid’s top-level feature map. Considering the unique shape of road diseases, imbalance problems between the foreground and background are common, therefore, we introduce the sample weighted loss function. In order to prove the superiority and effectiveness of this method, it is compared to the latest method. A large number of experiments show that this method is superior in accuracy to other methods, specifically, under the COCO evaluation metric, compared with the Faster RCNN baseline, the proposed method obtains a 41.1 mAP and 3.4 AP improvement.

Problems of Treatment and Maintenance Management of Asphalt Pavement Diseases in Rural Roads

Problems of Treatment and Maintenance Management of Asphalt Pavement Diseases in Rural Roads

PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement.

In the daily inspection task of the expressway, accuracy and speed are the two most important indexes to reflect the detection efficiency of nondeformation diseases of asphalt pavement. To achieve model compression, accelerated detection, and accurate identification under multiscale conditions, a lightweight algorithm (PDNet) based on improved YOLOv5 is proposed. The algorithm is improved based on the network structure of YOLOv5, and the improved network structure is called YOLO-W. Firstly, a novel cross-layer weighted cascade aggregation network (W-PAN) is proposed to replace the original YOLOv5 network. Secondly, more economical GhostC3 and ShuffleConv modules are designed to replace C3 and Conv modules in the original network model. In terms of parameter setting, CIoU is selected as the loss function of the model, and the K-Means ++ algorithm is used for anchor box clustering. Before the model training, the confrontation generation network (GAN) and Poisson migration fusion algorithm (Poisson) are used for data enhancement and the negative sample training (NST) method is used to improve the robustness of the model. Finally, Softer-NMS is used to remove the prediction box in the prediction stage. Seven common asphalt pavement disease data sets (FAFU-PD) are constructed at the same time. Compared with the original YOLOv5 algorithm, PDNet improves the scores of FAFU-PD data sets on F1-score by 10 percentage points and FPS by 77.5%.

An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on <uri>https://github.com/DearCaat/ioplin</uri>, and the CQU-BPDD dataset is able to be accessed on <uri>https://dearcaat.github.io/CQU-BPDD/</uri>.

Influence of Axle Load and Asphalt Layer Thickness on Dynamic Response of Asphalt Pavement

Based on the test data of vehicle load test on asphalt pavement, the significance of dynamic response index of different asphalt pavement to axle load change of single and double rear axle trucks is analyzed. The changes of horizontal strain at the bottom of asphalt layer, vertical compressive stress at the top of subgrade, and vertical displacement of transition layer and bottom compressive stress before and after laying of middle and upper layers are revealed. The test results show that reducing the thickness of asphalt layer or increasing the vehicle axle load can lead to the more unfavorable stress-strain distribution in asphalt pavement under driving load. The influence of the fluctuation of truck axle load or asphalt layer thickness on the stress-strain distribution in asphalt pavement varies with the type of pavement structure, pavement response index, vehicle driving speed, vehicle type, and the horizon of the measuring point. The transverse strain at the bottom of the asphalt layer is more affected by the axle load and the thickness of the asphalt layer than the longitudinal strain at the bottom of the asphalt layer. The measured value of the dynamic response index of the semirigid asphalt pavement is more affected by the axle load than that of the inverted asphalt pavement. However, the measured value of the dynamic response index of the semi-rigid asphalt pavement is less affected by the thickness of the asphalt layer when the thickness of the asphalt layer changes within a certain range. The tensile strain part of the longitudinal strain at the bottom of the asphalt layer and the compressive strain part of the transverse strain at the bottom of the asphalt layer are greatly affected by the thickness of the asphalt layer. The sensitivity of fatigue life of asphalt pavement of inverted structure to driving speed and axle load is less than that of semirigid structure. The research results can provide reference for the analysis of the asphalt pavement disease mechanism and provide guidance for asphalt pavement structural design and service life analysis.

Disease Characteristics and Maintenance Technology of Highway Subgrade and Pavement

In recent years, the subgrade and pavement diseases account for the vast majority of the expressway maintenance diseases, which puzzles many expressway maintenance departments and units. To sustain the continuous development of the expressway, the service level of the expressway is need to be improved, by ensuring the safety of the people, and by providing more convenient services for the people during travel. The technical measures for scientific maintenance of expressway subgrade and pavement diseases were discussed in this paper. Further, this paper discusses the main subgrade and pavement diseases types, and proposed the targeted maintenance methods, which can be as a reference for the expressway maintenance departments.