Distresses In Asphalt Pavements Research Articles

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.

Engineering properties-based parameters used for moisture damage evaluation of asphalt mixtures: a review

Moisture-induced damage has been known as a major premature distresses in asphalt pavement. Numerous review papers have been made to provide the progression in study on moisture damage of asphalt pavement. However, mechanical properties of asphalt mixtures used as a parameter for moisture susceptibility evaluation were not reviewed in detail. To make the review of moisture-induced damage in asphalt pavement more comprehensive, this paper reviews research studies on moisture damage of asphalt pavement in view of mechanical properties as a parameter during the past 50 years. These parameters include tensile strength ratio (TSR), cohesion ratio (CR), dynamic modulus ratio (DMR), resilient modulus ratio (RMR), and shear strength ratio (SSR). TSR and CR are only relevant at bottom of asphalt concrete (AC), while SSR can be used to describe moisture damage at different AC depths. DMR and RMR are concentrated on the impact of moisture damage via performance characteristics of asphalt mixtures.

Regional variations of climate change impacts on asphalt pavement rutting distress

The global climate is currently changing remarkably. Rising temperatures intensify the likelihood of rutting in asphalt pavements. This research, covering seven Chinese provinces from 2021 to 2100, predicts asphalt pavement rutting depth on freeways and secondary roadways for the next 80 years. The study introduced the Rutting Climate Change Index to gauge climate change impact on pavement rutting. It examined regional variations in climate's effect on pavement rutting and associated factors. The results indicate that both longitude and latitude significantly influence the Rutting Climate Change Index for asphalt pavement rutting distress. With increasing longitude, the impact of climate change on rutting distress in asphalt pavement exhibits diverse patterns across different regions. The influence of climate change on rutting distress in asphalt pavement increases with higher latitudes. The regional variations in the impact of climate change on rutting distress in asphalt pavements are more pronounced along the latitude.

Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach

Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R2) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved.

Evaluation of cracking resistance of asphalt concrete under long-term aging condition

Cracking is one of the primary distresses in asphalt pavements under traffic loading and climate. Asphalt cracking can be caused by aging asphalt binders in production, transportation, construction, and exploitation. Therefore, it is very necessary to study the influence of aging on cracking resistance for asphalt concrete. This paper initially evaluated the cracking resistance of asphalt concrete with a nominal maximum aggregate size of 12.5mm (AC 12.5) under long-term aging conditions. Based on the laboratory experiments, the study evaluated the required technical parameters of 60/70 binder, PMBIII binder, and AC 12.5 considering long-term aging conditions. Test results indicated that the aging of asphalt binder can significantly affect the cracking resistance of asphalt concrete.

Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)

Pavements function as protective layers for roads and require frequent inspection and maintenance throughout their service life. This paper describes an intelligent pavement distress inspection system that uses an enhanced version of the ‘you only look once’ (YOLO) model and an omni-scale network (OSNet) to instantly capture road surface distress images and their precise locations. The YOLO model was evaluated on a dataset comprising 9749 pavement distress images, with the detected distress serving as an input for feature extraction and instance-level recognition through OSNet. The OSNet model achieved a mean average precision (mAP) of 99.4% for a dataset containing 398 individual distress instances. The proposed methods were successfully integrated into a pavement distress inspection vehicle. Field experiments demonstrated the real-time capability and high efficiency of the system, with significant improvement in road maintenance inspection efficiency

Experimental study on the interface bonding characteristic of polyurethane and pavement materials

As a new grouting material, polyurethane has been used in the repair engineering of longitudinal joint distress of semi-rigid base asphalt pavement. It is necessary to study the bonding characteristics of the interface between polyurethane and road materials. In this paper, the interface characteristics of composite materials were considered from the two materials about pavement material and base material. Shear test and split tensile test were carried out on the specimens with different interface roughness and polyurethane density. Based on the test data, the strength models of shear strength and splitting tensile strength with roughness and density were established. The micro-characteristics of the composite interface were analyzed. Polyurethane formed a dense film at the interface, and the film thickness increases with the increase of density. The failure mode of the specimen was affected by the polyurethane film. In addition, the influence of normal stress is considered in the shear test, and the friction angle of the interface was analyzed. The stress-strain curve characteristics of shear test and split tensile test of two kinds of composite materials were summarized. The failure modes were generally three types: failure along the boundary of polyurethane material, failure along the boundary of base material, and joint failure of polyurethane material and base material along the interface. The research results provide a basis for better understanding the influencing factors and failure modes of the bonding characteristic between asphalt mixture, cement stabilized macadam and polyurethane materials.