Distresses In Asphalt Pavements Research Articles

Behavior of Concrete Mixtures in Ultra-Thin Whitetopping Composite Pavements

Ultra-thin whitetopping (UTW) is an alternative pavement rehabilitation option to asphalt concrete overlays. UTW is typically a concrete overlay less than 5 inches thick on an existing distressed asphalt pavement. A research project was recently finished to refine the current UTW design methods and develop new recommendations for improved UTW performance. As part of this project, a laboratory study investigated various concrete mixture effects on UTW performance. Seven mixture designs containing various w/cm ratios, cementitious content, discrete fiber-reinforcement content, and two coarse aggregate types were studied. A composite beam test (concrete placed on an existing asphalt beam) was developed to predict the composite section behavior in the laboratory. For this small-scale 2D testing, the results were not as dramatic as expected between the different concrete mixtures. However, the fiber-reinforcement, which increased the total fracture energy of concrete, demonstrated improvement in the load carrying capacity after initial cracking. The concrete mixture design alone was not the sole indicator of the UTW performance. Higher strength concrete mixtures can fracture just as rapidly as normal strength concrete if the UTW exhibits debonding. It is recommended that structural fibers be added to the concrete to extend the performance life of the rehabilitated pavement, especially in the presence of existing asphalt cracks and potential interface debonding.

Long-term skid resistance evolution and influence mechanism of asphalt pavement based on self-developed wear equipment

Skid resistance decay is the critical functional distress of asphalt pavement, directly restricting its safety and service life. In order to grasp the characteristics and mechanism of long-term skid resistance evolution, this study developed a laboratory wheel-accelerated wear equipment (WAWE) and its testing methodology for asphalt pavement and coarse aggregate. Five coarse aggregates and five gradations combinations were selected to investigate the evolution. The accuracy of the evolution model and the loading conversion relationship were analyzed based on the Research Institute of Highway (RIOH) Track. Finally, the pavement anti-skid texture tester (PATT) was used to investigate the evolution characteristics of coarse aggregate microtexture. Results showed that the Asymptotic evolution model could characterize the skid resistance under the influence of coarse aggregate and gradation. RIOH Track verified the model's accuracy and showed that a single WAWE wear was equivalent to 265 loading times on actual pavement. The long-term skid resistance could be improved by increasing the nominal maximum particle size and the gradation fractal dimension indexes D and Df. The microtexture roughness of the coarse aggregate decreased after wear, then stabilized and fluctuated, which showed "regeneration" characteristics. The study can guide asphalt mixture design and maintenance timing decisions based on long-term skid resistance.

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex background in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone’s C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving Pmacro and mAP50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, YOLOv8n, and SSD models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model, when applied to image data collected from projects, achieves a Pmacro of 80.5% and an Rmacro of 86.2%. This result demonstrates its excellent generalization capability and practicality. The model provides significant technical support for the intelligent detection of pavement distress.

ARDs-YOLO: Intelligent detection of asphalt road damages and evaluation of pavement condition in complex scenarios

Pavement distress detection is the key to the development of road maintenance programs. However, traditional methods with long detection period and strong subjectivity are difficult to meet the rapid and large-scale automatic detection and evaluation of pavement technical condition. In this paper, we propose a method based on YOLOv5 combined with the initialized anchor frame selection strategy of intersection and merger ratio metrics, the multidimensional information interaction attention mechanism module ARDs, and the lightweight up-sampling operator CARAFE. An asphalt pavement distress dataset ARDs-5 containing 21,797 pavement images is constructed using a vehicle-mounted CCD camera. The proposed method outperforms the comparison frontier baseline on the self-constructed dataset with a mAP of 75.8%. Finally, ARD-YOLO was applied to evaluate the pavement condition of asphalt highways in Liaoning Province, China, and achieved excellent performance. The experiments show that ARD-YOLO has high practical value for pavement distress detection and evaluation in complex environments.

Effective optimization method of asphalt binders and asphalt mixtures based on the two-parameter of contraction and relaxation

Thermal cracking is one of the primary distresses of asphalt pavement which is a complex process. Evaluating the Low-temperature Performance (LTP) of asphalt mixtures accurately and conducting material optimization analysis is not only crucial for understanding the thermal cracking mechanism of asphalt mixtures but also serve as the basis for enhancing their thermal cracking resistance performance. However, current research has not sufficiently interpreted the mechanism of thermal cracking. It is segmentary that optimizes asphalt mixtures solely based on their ultimate strength, strain, and so on at certain low-temperature points. Therefore, this study closely integrated the thermal cracking mechanism and proposed a two-parameter optimization method. Firstly, clarifying the decisive role of the contraction and relaxation relationship in determining LTP through the Thermal Stress Restrained Specimen Test (TSRST), thermal contraction test, and low-temperature relaxation test. Secondly, quantizing the impact of the aggregates skeleton and then using a two-parameter method to analyze the comprehensive effects on LTP of asphalt mixtures. Finally, summarizing the material optimization method of asphalt binder-first, aggregates-second based on the multiple linear regression method. This paper simplified the evaluation method and provided a basis for material selection and design for improving the thermal cracking resistance of asphalt mixtures.

Effect of different types of fine aggregates on fatigue resistance and self-healing of fine aggregate asphalt matrices

Fatigue cracking is one of the most common types of distress in asphalt pavements. Each asphalt concrete (AC) constituent and its interactions are relevant to characterize the resistance to fatigue cracking of the ACs. Also, for the selection of materials, it is essential to consider not only the capacity of the material resist to fatigue cracking but also its ability to heal. Materials owning self-heal properties can enlarge the ACs fatigue life. Many former studies investigated the self-healing of bituminous materials at the binder level. However, this material property can be dependent on the binder-aggregate interactions. Thus, the current work aims to evaluate the influence of using different fine aggregates on the fatigue cracking resistance and self-healing capacity behavior of fine aggregate matrices (FAMs). First, granite, basalt, and mica schist aggregates were subjected to physical, morphological, and mineralogical characterization. Then, three FAMs were fabricated with the same asphalt binder and these different fine aggregates. To evaluate the fatigue cracking resistance and self-healing capacity of the FAMs, frequency sweep and time sweep tests were conducted. The simplified viscoelastic continuum damage (S-VECD) theory was used to interpret the results of those tests. The mineral composition of the aggregates impacted the stiffness and the fatigue life of the FAMs. However, there was no significant influence of the aggregates on the self-healing capacity of the FAMs, since there was no significant increase in the fatigue life of the materials after the resting periods in the time sweep tests.

The influence of interlayer bonding conditions on the propagation laws of reflective cracks in semi-rigid base pavement based on the DEM and GPR

Reflection cracks represent the predominant internal distress in semi-rigid base asphalt pavements and often accompanied by additional secondary issues that detrimentally affect the pavement structural bearing capacity. The propagation and deterioration of reflective cracks are affected by many factors, for example the interlayer bonding conditions. It is crucial to identify the propagation and deterioration of reflective cracks to prolong the life of pavement structure. The purpose of this article is to explore the influence of interlayer bonding conditions on the propagation laws of reflective cracks. This work employs the discrete element method(DEM) to explore the propagation patterns of reflection cracks accompanied different bonding conditions between the surface layer and base layer subjected to moving load. The results of discrete element simulation reveal a three-phase progression laws when the reflective cracks expand from the base layer to the pavement surface，involving rapid propagation, stable propagation, and failure process. With the interlayer bond strength decreases, expansion of reflective crack is significantly accelerated. Meanwhile, once the critical bond strength threshold is reached, complete debonding will occur between the surface and base layers. The results are ultimately verified through the core sampling from practical engineering using three-dimensional (3D) ground penetrating radar(GPR) detection, along with the radar image recognition of reflective cracks and poor interlayer bonding. The results will serve as a foundation for researching the mechanism and treatment measures for two internal pavement distress, namely reflection cracks and poor interlayer bonding.

Evaluation of Fatigue Behavior of Asphalt Field Cores Using Discrete Element Modeling.

Fatigue cracking is one of the primary distresses of asphalt pavements, which significantly affects the asphalt pavement performance. The fatigue behavior of the asphalt mixture observed in the laboratory test can vary depending on the type of fatigue test and the dimension and shape of the test specimen. The variations can make it difficult to accurately evaluate the fatigue properties of the field asphalt concrete. Accordingly, this study proposed a reliable method to evaluate the fatigue behavior of the asphalt field cores based on discrete element modeling (DEM). The mesoscopic geometric model was built using discrete element software PFC (Particle Flow Code) and CT scan images of the asphalt field cores. The virtual fatigue test was simulated in accordance with the semi-circular bending (SCB) test. The mesoscopic parameters of the contacting model in the virtual test were determined through the uniaxial compression dynamic modulus test and SCB test. Based on the virtual SCB test, the displacement, contact forces, and crack growth were analyzed. The test results show that the fatigue life simulated in the virtual test was consistent with that of the SCB fatigue test. The fatigue cracks in the asphalt mixture were observed in three stages, i.e., crack initiation, crack propagation, and failure. It was found that the crack propagation stage consumes a significant portion of the fatigue life since the tensile contact forces mainly increase in this stage.

Pavement raveling inspection using a new image texture-based feature set and artificial intelligence

Enhancing pavement distress inspection is crucial within a pavement management system. Recognizing the advantages of automated pavement condition inspection, considerable efforts have been invested in developing more efficient systems for this purpose. Raveling, a common asphalt pavement distress, significantly impacts both passenger comfort and pavement durability. Given its connection to pavement texture and the limited effectiveness of earlier inspection systems, improving automatic raveling inspection has become a challenging endeavor. This study proposes an image-based system for raveling detection, employing a new image processing-based feature set. This approach generates enhanced inputs for artificial intelligence models, thereby improving the efficiency of raveling inspection while optimizing resource allocation. To accomplish this, an image dataset is created by combining newly collected images with an existing collection. Novel combinations of features are then engineered to extract meaningful texture-based image features. Subsequently, three feature selection algorithms are employed to identify the most pivotal features. Machine Learning (ML) models are then trained for effective raveling detection based on the proposed feature set, and their performance is evaluated across various performance indices. The achieved performance metrics, reaching nearly 96 % on the test datasets, underscore the efficacy of the proposed method in automating the inspection of texture-based pavement distresses.

Hybrid Transfer Learning and Support Vector Machine Models for Asphalt Pavement Distress Classification

Pavement condition evaluation plays a crucial role in assisting with the management of the highway infrastructure. However, the current methods used for assessing pavement conditions are costly, time-consuming, and subjective. There is a growing need to automate these assessment tactics and leverage low-cost technologies to enable widespread deployment. This study aims to develop robust and highly accurate models for classifying asphalt pavement distresses using transfer learning (TL) techniques based on pretrained deep learning (DL) networks. This topic has gained considerable attention in the field since 2015 when DL became the mainstream choice for various computer vision tasks. While progress has been made in TL model development, challenges persist in areas of accuracy, repeatability, and training cost. To tackle these challenges, this study proposes hybrid models that combine DL networks with support vector machines (SVMs). Three strategies were evaluated: single DL models using transfer learning (TLDL), hybrid models combining DL and SVM (DL+SVM), and hybrid models combining TLDL and SVM (TLDL+SVM). The performance of each strategy was assessed using statistical metrics based on the confusion matrix. Results consistently showed that the TLDL+SVM strategy outperformed the other approaches in accuracy and F1 scores, regardless of the DL network type. On average, the hybrid models achieved an accuracy of 95%, surpassing the 80% accuracy of the best single model and the 55% accuracy for DL+SVM without TL. The results clearly indicate that employing transfer-learned models as feature extractors, in combination with SVM as the classifier, consistently achieves exceptional performance.

Evaluation of moisture‐induced damage on the fatigue life of asphalt mixtures using failure tests of asphalt binders, interfaces, and mixtures

AbstractFatigue cracking, a major distress in asphalt pavements, is worsened by traffic and climate conditions such as moisture effects on constituents and interfacial properties. However, investigation on fatigue's evolution in different scales under moisture effect is still scarce in the open literature. This work aims to assess moisture‐induced damage effects on asphaltic materials focusing on the failure of asphalt binders, the aggregate‐binder interface, and mixture. The methodology included tests under dry and wet conditions: linear amplitude sweep (LAS); asphalt bond strength (ABS) in different temperatures and pull rates for interfaces; indirect tensile strength (ITS), and tension‐compression fatigue on asphalt mixtures. Key findings were: (i) moisture damage presents growing effects from binders to mixtures; (ii) LAS test results were not sensitive to moisture; (iii) ABS tests require proper field temperatures for accuracy; (iv) ITS ratio provide reliable conditioned fatigue expectations; and (v) fatigue testing without conditioning to hydrodynamic effects or real moisture susceptibility might overestimate service‐life.

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R2 = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.

Water Stability of Fibers-Enhanced Asphalt Mixtures under Static and Dynamic Damage Conditions.

Water damage is one of the major distresses of asphalt pavements. Existing methods for investigating the water stability of asphalt mixtures rely primarily on static water test methods, the tensile strength ratio (TSR) test, and the retained Marshall stability (RMS) test, which evaluate the strength and stability loss after freeze-thaw damage or hot water immersion, respectively. However, these methods do not accurately replicate the actual dynamic water damage conditions to pavement. Therefore, in this study, a variety of damage conditions, including static water conditions and dynamic water pressure conditions, were used to investigate the effects of lignin fibers (LFs), polyester fibers (PFs), and polypropylene fibers (PPFs) on the water stability of asphalt mixtures. First, three fibers-enhanced SMA gap-gradation asphalt mixtures were designed. Then, TSR and RMS were measured under traditional static water damage conditions and new dynamic water pressure damage conditions to evaluate the effect of fiber types on the water stability of asphalt mixtures. Finally, the void rate of asphalt mixtures and its changes under dynamic water damage conditions were further revealed with the help of CT scanning technique. Results showed that, among these three types of fibers, PFs-enhanced asphalt mixture exhibited excellent stability under both static and dynamic water conditions, and the CT scanning test also indicated that the PFs can significantly reduce the increase rate of voids in asphalt mixtures after dynamic water pressure damage. This study identified the potential of incorporating suitable type of fiber to enhance the performance of asphalt mixture under dynamic water pressure damage.

Study on the change rule of low-temperature properties of asphalt binder with aging time

ABSTRACT Low-temperature cracking is common distress in asphalt pavements. Understanding the change rule of its low-temperature properties is helpful in investigating the optimal maintenance timing of asphalt pavement. In this paper, the change rule of low-temperature properties of asphalt binders with aging time is investigated by various indexes. Four asphalt binders and four anti-aging modified asphalt binders were subjected to the rolling thin film oven (RTFO) test and pressure aging vessel (PAV) test. Dynamic Shear Rheometer (DSR) with the 4 mm parallel plates was used to determine the low-temperature properties of asphalt binders. The results show that aging and low temperature have a negative impact on the low-temperature properties of asphalt binders. The anti-aging agent improves the low-temperature properties and aging resistance of asphalt binders. Compared with other indexes, the critical cracking temperature Tc and the critical temperature difference ΔTc are more suitable for analysing the effect of aging on the low-temperature properties of asphalt binders. Also, the ΔTc shows the three-stage decreasing rule of ‘fast-slow-fast’, with the inflection points. It meant that the low-temperature properties of asphalt binders had been altered at the inflection points, which provided a favourable reference to research on the optimal maintenance timing of asphalt pavement.

Multiple distresses detection for Asphalt Pavement using improved you Only Look Once Algorithm based on convolutional neural network

ABSTRACT Leveraging the YOLOv7 object detection framework, this study introduces YOLOv7-CSP, a refined algorithm tailored for identifying asphalt pavement distress with enhanced precision. Utilizing advanced image processing for dataset preprocessing, including data augmentation and denoising, YOLOv7-CSP integrates the CSPNeXt structure and CA attention mechanism for improved detection accuracy and efficiency. The algorithm optimizes anchor box selection through Kmeans clustering and employs a secondary labeling method to enhance learning efficiency and dataset quality. Comparative tests reveal YOLOv7-CSP's superior performance, with significant improvements in mAP, F1 score, GFLOPS, and FPS metrics, demonstrating its effectiveness in detecting various pavement distresses. This innovative approach marks a significant advancement in automatic pavement distress recognition, offering a robust solution for highway maintenance decision-making.

Intelligent detection of hidden distresses in asphalt pavement based on GPR and deep learning algorithm

Hidden distresses of asphalt pavement are hidden dangers in the service of expressways. Efficient and accurate detection of hidden distresses is of great importance for maintaining and managing the expressways suffering from them. In this study, the intelligent detection technology of asphalt pavement hidden distresses was studied using the object detection algorithm and ground penetrating radar (GPR) image data. A GPR image data set was established to train and test the object detection model. And the distresses samples in the data set include settlement, hidden cracks and loosening. According to the distribution of hidden distresses in GPR image data, a two-way object detection model for targets of different scales was proposed, and each part of the two-way model was optimized and improved based on the basic model. At the same time, a copy-and-paste data enhancement scheme was designed to expand and balance the number of distress data samples. The final model greatly improved the detection accuracy of asphalt pavement hidden distresses. The results of the model test showed that the two-way object detection model proposed in this paper can effectively improve the detection accuracy of small-sized distress objects. Compared to the basic model, the final model had an increase of 17.9% in the average precision (AP) of small objects and a 9.9% increase in the comprehensive index AP (0.5: 0.95). The results of the ablation experiment show that the optimization methods for each part of the model improved the comprehensive detection effect of the model. When compared with other object detection models, the final model was superior to other models in all AP indicators, and had obvious advantages in the detection accuracy of the three types of hidden distresses. The confusion matrix of the test results of the final model showed that the recall rate of the final model for the three types of hidden distresses was above 96%. Thus, applying the final model established in this study to actual road detection can effectively improve the efficiency of distress detection in GPR images.

Correlation analysis of highway asphalt pavement distress based on Spearman correlation coefficient

In order to improve the operational life of highway asphalt pavements, reduce urban traffic safety accidents, and improve pavement travelling comfort, this paper analyze the frequency of different highway asphalt pavement distresses and calculates the percentage of each other based on highway asphalt pavement data sets in Shandong. The results showed that the five types of highway asphalt pavement distresses including alligator cracks, longitudinal cracks, transverse cracks, sealed cracks and patches occur with high frequency and account for 96.2% of the total proportion. For revealing the coupling principle between different highway asphalt pavement distress, this paper analyze the correlation between the five types of highway asphalt pavement distresses by Spearman correlation coefficient. The results showed that alligator cracks have a higher positive correlation with transverse cracks and longitudinal cracks and sealed cracks have a negative correlation with transverse cracks. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize he level of service of asphalt pavements on urban roads.

Review of Multi-dimensional Rut Index Threshold Method Based on Driving Comfort and Safety

Rutting, one of the typical distresses in asphalt pavement, is known to be a potential hazard to driving comfort and safety when considering driving performance. The cost of different treatment plans varies greatly, which directly affects the allocation and use of maintenance funds and the formulation of maintenance investment plans. The safety and comfort evaluation and maintenance threshold of rutting have an important role in determining the timing of rutting maintenance and the allocation of maintenance funds. At present, the research on rutting based on driving safety/comfort only considers the one-dimensional rut depth. It is difficult to carry out in-depth discussion on the influence of traverse and longitudinal features from the three-dimensional perspective, thus cannot establish a vehicle driving safety/comfort theory system based on real multi-dimensional rut morphological features. With the introduction of high-efficiency, high-density and high-precision advanced 3D laser technology, it is possible to break through the bottleneck of effective extraction and construction/reconstruction of high-resolution three-dimensional rutting features. This paper gives a detailed overview of the rut maintenance threshold standard and multi-dimensional rut indexes, rut comfort and safety evaluation, and maintenance threshold methods; and finally discuss the current problems and proposes future research recommendations. Future research will not only construct the multi-dimensional morphological of the rut with high resolution and with the cross and vertical slopes of the road considered, but also establish a multi-dimensional rut evaluation method and maintenance standard considering driving safety/comfort with comprehensive high precision, which can be used for the refining and revising of rut maintenance specification.

Preparation and properties of one-component polyurea composite phase change cooling coating for asphalt pavement

To reduce the high-temperature distress of asphalt pavement and alleviate the urban heat island effect, one-component polyurea composite phase change cooling coating was prepared. Through the tests of storage time, curing time, hardness and tensile property, the performance variation of one-component polyurea under different latent curing agent dosage were analyzed. Through the tests of road performance and thermoregulation performance, the change rule of road performance and cooling effect of one-component polyurea composite phase change cooling coating under different amount of phase change particle admixture and coating were analyzed. The results showed that the phase-change cooling coating prepared in this study can meet the requirements of road performance. When the dosage of phase change particles is 20% and the amount of coating is 1.2 kg/m2, the maximum cooling effect of the coating reached 3.7 °C under the simulated indoor conditions and 3.3 °C under the real outdoor environment.

Automatic detection and location of pavement internal distresses from ground penetrating radar images based on deep learning

Ground penetrating radar (GPR) has been prevailingly applied in nondestructive testing of internal distresses within asphalt pavement. However, the interpretation of abnormal echo signals in massive pavement GPR images is time-consuming and labor-intensive, and prone to misjudgment. To address this issue, a three-step method was proposed for the automatic detection and location of the internal distresses (eg: crack and debonding) echo features from GPR images. The on-site and numerical simulated GPR images of the asphalt pavement together formed the dataset required for the subsequent deep learning models. The first step is that You Only Look Once version 3 (YOLOv3) model predicted rectangular boxes for enclosing the internal distress echo features from GPR images. The second step involves developing the U-net models to segment the internal distress echo feature pixels in the cropped rectangular boxes. The last step is that the median points of the segmentation of the internal distress echo feature were fitted with a theoretical curve equation, to estimate the location of the internal distress. The proposed method has shown that the comprehensive detection accuracy of the internal distress echo features can reach 96.99%, the semantic segmentation accuracies of the internal distress echo feature pixels are not less than 0.856, and the average deviation of the estimated depths of the internal distresses is 3.25 cm. The research method makes further advances in accurately and automatically detecting and locating the internal distresses of asphalt pavement.