Pavement Distress Research Articles

Pavement crack detection based on depth-supervision FRRN model

Timely detection of early pavement distress is crucial for effective pavement maintenance. A promising approach involves the segmenting the road surface cracks area from its images. However, the previously employed full-resolution residual network (FRRN), when employing a large number of full-resolution residual units (FRRUs), is susceptible to challenges such as exploding and vanishing gradients. Addressing these issues, this paper has introduced a depth supervision mechanism into FRRN, leading to the development of the Depth Supervision FRRN (DSFRRN) model, designed specifically for accurate segmentation of cracks in road images. This model enhances nonlinear expressiveness while retaining essential features, achieving high precision in pavement crack segmentation. Through experimental validation using a dataset comprising 1565 images of damaged pavements, the result demonstrate that DSFRRN outperforms the original FRRN in terms of both F1 score and Jaccard index, accurately delineating cracks. Consequently, the automated pavement damage detection method presented herein facilitates cost-effective and efficient pavement inspection, contributing to the safeguarding of roadway surfaces and transportation infrastructure.

Utility Cut Impact Assessment and Fee Development Using Pavement Management System

The purpose of this study is to estimate any damage caused by utility cuts to pavements and develop a fee schedule to recover the costs associated with such damage using the City of Pacifica, CA, U.S.’s pavement management system (PMS). To accomplish this, the study looked at the functional deterioration of pavements resulting from utility cuts. The City of Pacifica’s PMS containing a vast dataset of pavement distress spanning 15 years, comprising thousands of data points encompassing various pavement ages and conditions, was analyzed to assess the impact of utility cuts. The findings from this study include: 1) Pavements with cuts of any size deteriorate more than pavements without cuts across all age groups with one exception for older residentials with small cuts; 2) On average, pavement condition index drops by 30% if the cut area is greater than 10% of the section area; and 3) Utility cuts inflict greater damage on newer pavements (<10 years old) compared with older ones (≥10 years old). Consequently, new pavements experience an average percent reduction of approximately 33% in their remaining service life, while older pavements suffer a reduction of about 17%. The statistical analyses indicate that utility cuts caused significant damage, and supported a tiered utility cut fee structure based on functional classification, age of pavement, and size of the cut. The resulting fee schedule is based on 2021 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Example fee calculations are provided.

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.

Innovative Pavement Solutions: A Comprehensive Review from Conventional Asphalt to Sustainable Colored Alternatives

Climate change significantly impacts transportation infrastructure, particularly asphalt pavements. Similarly, the heat absorption of paved surfaces, especially conventional black pavements, significantly intensifies the urban microclimate. Paved surfaces, including asphalt pavements, account for over 30% of the covered surfaces and are vulnerable to rising temperatures, which cause not only pavement distress, such as rutting and cracking, but also urban heat islands (UHI). Sustainable pavement solutions, specifically colored pavements, have been investigated for their potential to mitigate these effects. This review presents an extensive overview of current pavement technologies, emphasizing conventional asphalt’s economic, environmental, and functional characteristics. A discussion of the benefits and challenges of colored pavements is also provided, including their ability to reduce UHI, enhance safety, and contribute to sustainable urban growth. This paper discusses advancements in pavement material science, the use of recycled materials, and the application of reflective coatings, providing insights into sustainable infrastructure development. Transitioning from conventional black pavements to sustainable colored alternatives is not merely a matter of material choice but a strategic transition toward resilient urban planning. Increasing demand for environmentally friendly infrastructure could prompt the construction industry to adopt colored pavements as a tool to promote environmental stewardship.

Impact of Nanocarbon-Coated Calcium Carbonate on Asphalt Rutting: Experimental and Numerical Analyses

Rutting is a significant form of pavement distress that arises from irreversible strains accumulating along wheel paths, directly impacting pavement safety. This research investigates the effectiveness of nanocarbon-coated micronized calcium carbonate powder as a modified filler to mitigate rutting, utilizing numerical methods via finite element software. The study specifically examines the addition of 5% by weight of this modified filler to the asphalt mix. To validate the numerical results, laboratory wheel-tracking tests were conducted on samples incorporating both conventional and modified fillers. The findings reveal that the modified calcium carbonate filler enhances the asphalt’s resistance to rutting, with the 5% inclusion demonstrating a marked improvement in durability and performance. The study also underscores the necessity of characterizing the elastic and visco-plastic properties of materials through rigorous testing methods, such as elastic modulus and dynamic creep tests, to better understand their behavior under load. Numerical analysis based on linear elastic conditions was prioritized over viscous conditions to effectively compare the results of these specialized materials. The strong correlation between the numerical simulations and laboratory results reinforces the effectiveness of finite element methods in predicting pavement behavior and optimizing asphalt mixtures.

Optimizing epoxy asphalt tack coat formulations for improved adhesion and working performance of ultrathin wearing course

Insufficient adhesion between ultrathin wearing courses (UTWCs) and substrate layers can result in pavement distress. Epoxy asphalt exhibits potential as a tack coat due to its high bonding strength and thermal stability. However, its rapid curing may pose challenges to construction schedules. This study aimed to optimize the formulations of epoxy asphalt tack coats (EATCs) to enhance the interfacial adhesion and working performance of UTWCs. The adhesion of EATCs was evaluated through rotational viscosity and tensile tests. Design parameters were optimized using analysis of variance and microscopic tests. Road performance was assessed via interlayer shear and tensile testing on three pavement structures. The results indicated that the curing agent strongly influenced the properties and curing kinetics of EATCs. Formulations containing complex polysebacic polyanhydride (CPSPA-EA) exhibited superior bonding while meeting construction schedules. Microscopic analysis of CPSPA-EA revealed a stable crosslinked network formed at 30% binder dosage. The amount of spraying significantly impacted the adhesion in UTWC structures. The optimal spraying amounts were 0.60 kg/m2 for asphalt concrete (AC-13), 0.55 kg/m2 for stone matrix asphalt (SMA-13), and 0.40 kg/m2 for cement concrete substrate layers. These findings presented an optimization framework for designing EATCs to enhance UTWCs working and long-term performance through balanced tack formulations and application parameters.

Pavement Distress Detection Based on the Staged Deep Learning Classifier Approach

Pavement crack detection is a critical task that is essential to ensure the safety of roads and highways. Accurate detection and classification of pavement cracks can help transportation agencies to identify potential maintenance needs and plan appropriate interventions. In this project, we propose a staged deep learning classifier approach for pavement crack detection to provide accurate and comprehensive information about the pavement condition. In this proposed pavement crack detection model, there are four main phases: pre-processing, feature extraction, feature selection, and crack detection. Firstly, raw images are pre-processed through median filtering and histogram equalization for noise removal and contrast enhancement. Next, features such as the improved local binary pattern, gray-level co-occurrence matrix, and gray-level run length matrix are extracted from the pre-processed images. The optimal features are selected using a hybrid optimization model, the tuna customized honey badger optimization algorithm, which combines the honey badger algorithm and tuna swarm optimization. This new hybrid optimization model will enhance the search strategy for solving optimization problems. The loss function of an augmented convolutional neural network (A-CNN) is modified to compute the root mean square error instead of the entropy-based loss function. The final detected outcomes (presence or absence of cracks) are acquired from the A-CNN.

Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet

With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively.

Impacts of the replacement of distressed pavements by low noise pavements - a case study in Portugal

BRISA is employing efforts to evaluate the pavement influence as a factor of source noise reduction, while studying forms of adjusting the applicability of the CNOSSOS noise prediction method to the reality of the concession road network, in order to comply with European directives about Environmental Noise evaluation and management. This Project evaluates the environmental noise effects of replacing a wearing course of Porous and Betuminous Asphalt at end-of-life for a course of SMA12 and new Betuminous Asphalt. Two methodologies were applied to evaluate Tire-Pavement noise, (i) through Statistical Pass-By and (ii) Close Proximity methods. Using these methodologies, it was possible to evaluate the effect of the intervention and the evolution of the noise in the first few months of the exploration of the layers and to monitor noise performance of the pavements during its entire lifetime. Brisa is challenged to have better pavements knowledge to extend CNOSSOS pavement library and update noise models.

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.

An Ensemble Learning Approach With Attention Mechanism for Detecting Pavement Distress and Disaster-Induced Road Damage

An Ensemble Learning Approach With Attention Mechanism for Detecting Pavement Distress and Disaster-Induced Road Damage

Predicting pavement surface conditions through artificial neural networks

In this study, six distinct types of pavement distress were evaluated using the pavement condition index (PCI) rating: ravelling, rutting, cracking (including alligator, transverse and longitudinal cracks), shoving, patching and potholes. The severity levels of these distress types are utilised for classification, and these levels are subsequently converted into PCI values. The proposed methodology incorporates a thorough analysis, considering various variables such as average daily traffic, land-use classification, number of lanes, road width and length, pavement age, road alignment, vehicle type and weather conditions in Berzhite, Tirana, Albania. To predict PCI values, an artificial neural network model was employed due to its versatility in handling diverse inputs. Key performance metrics, including R2, mean squared error and mean absolute error, are used for model assessment. Notably, model 2, featuring two hidden layers, exhibits superior performance over model 1. However, due to size constraints in testing and validation datasets in this study, the accuracy of the model is somewhat limited. Future directions for research involve expanding the dataset to enhance model accuracy and incorporating advanced features to capture a more complex comprehension of pavement conditions.

A Method for Surveying Road Pavement Distress Based on Front-View Image Data Using a Lightweight Segmentation Approach

A Method for Surveying Road Pavement Distress Based on Front-View Image Data Using a Lightweight Segmentation Approach

Experimental Study to Investigate the Performance-Related Properties of Modified Asphalt Concrete Using Nanomaterials Al2O3, SiO2, and TiO2.

The dual nature of asphalt binder necessitates improvements to mitigate rutting and fatigue since it performs as an elastic material under the regime of rapid loading or cold temperatures and as a viscous fluid at elevated temperatures. The present investigation assesses the effectiveness of Nano Alumina (NA), Nano Silica (NS), and Nano Titanium Dioxide (NT) at weight percentages of 0, 2, 4, 6, and 8% in asphalt cement to enhance both asphalt binder and mixture performance. Binder evaluations include tests for consistency, thermal susceptibility, aging, and workability, while mixture assessments focus on Marshall properties, moisture susceptibility, resilient modulus, permanent deformation, and fatigue characteristics. NS notably improves binder viscosity by about 138% and reduces penetration by approximately 40.8% at 8% nanomaterial (NM) content, significantly boosting hardness and consistency. NS also enhances Marshall stability and decreases air voids, increasing the mix's durability. For moisture resistance, NS at 8% NM content elevates the Tensile Strength Ratio (TSR) to 91.0%, substantially surpassing the 80% standard. Similarly, NA and NT also show improved TSR values at 8% NM content, with 88.0% and 84.1%, respectively. Additionally, NS, NA, and NT reduce permanent deformation by 82%, 69%, and 64% at 10,000 cycles at 8% NM content, illustrating their effectiveness in mitigating pavement distress. Notably, while higher NM content generally results in better performance across most tests, the optimal NM content for fatigue resistance is 4% for NS and 6% for both NA and NT, reflecting their peak performance against various types of pavement distresses. These results highlight the significant advantages of nanoparticles in improving asphalt's mechanical properties, workability, stability, and durability. The study recommends further field validation to confirm these laboratory findings and ensure that enhancements translate into tangible improvements in real-world pavement performance and longevity.

A novel model for the pavement distress segmentation based on multi-level attention DeepLabV3+

The identification and recognition of pavement distress are vital for automatic pavement evaluation. The computation efficiency and accuracy are the two factors that determine the evaluation model. Our study utilizes the state-of-the-art image segmentation method of DeepLabV3+ with the attention mechanism. This study aims to conduct the pavement distress segmentation on the Crack500 and GAPs384 datasets. Critical results are comprehensively provided with a comparison of different backbones and architectures. In this study, an adaptive probabilistic sampling method is proposed and adopted to compare with the random crop and resized images. The test result of the adaptive prob-sampling method on DeepLabV3-attention architecture outperforms other model results on the Crack500 dataset. On the other way, the test result of the adaptive prob-sampling method on DeepLabV3 without attention architecture works better in the GAPs384 dataset. The different results relied on the diverse characteristics of datasets, since GAPs384 datasets have different asphalt surface types and a wide variety of distress classes rather than simple crack information which Crack500 included. In addition, some performance improvement methods, such as batch normalization of image input, revising dice loss, and hyperparameters search, have been implemented in this work. The results are solid and reliable for concluding the critical analysis of methods and datasets. This study demonstrates the considerable potential of deep learning in the application of intelligent pavement evaluation. To advance the practical application of artificial intelligence on distress segmentation, we will explore more domain adaptive methods in future studies.

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.

Cost-Effective Pavement Condition Survey for Municipal Road Networks

One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.

UPDAPS-Flood: a mechanistic-empirical flexible pavement analysis tool to evaluate the effect of flooding events on flexible pavement performance

ABSTRACT Flooding can cause considerable damage to flexible pavements and negatively affects their performance over time. This study aims to evaluate the flooding effects on the flexible pavement performance and the potential resiliency of the pavement network through a mechanistic-empirical (ME) pavement analysis approach. The Unified Pavement Distress Analysis and Prediction System (UPDAPS) programme, which includes ME analysis models, was used to develop the UPDAPS-Flood programme. A total of 7,655 flexible pavement sections were extracted from the Highway Pavement Management System (HPMS) database in three United States regions. Finally, the resiliency of the pavement network to flood events was quantified using the loss of pavement service life based on the international roughness index (IRI). The results showed that flooding has the highest impact on rutting performance, followed by IRI and fatigue cracking. It has been found that a proper drainage system can reduce the flooding impact by a factor of two. The predicted pavement performance was found to be very sensitive to the coefficients of the rutting model, which brings the need for further studies to re-calibrate the rutting prediction model at higher moisture content levels.

Image-based severity analysis of Asphalt pavement bleeding using a metaheuristic-boosted fuzzy classifier

Pavement management systems play a vital role in maintaining transportation infrastructures by evaluating pavement distress to perform maintenance tasks efficiently. Severity analysis is an important step in this process. With an increasing focus on automating the pavement distress inspection, challenges persist, including limited attention to severity analysis of texture-based distresses and lack of applying fuzzy systems in this analysis despite the linguistic and qualitative description of severity levels. Accordingly, this paper presents a methodology leveraging computational intelligence frameworks such as fuzzy logic and metaheuristic optimization to develop a reliable system for severity analysis, particularly focusing on asphalt pavement bleeding. Employing GLSM and statistical feature extraction in conjunction with a fuzzy classifier, optimized with metaheuristic-based algorithms like GA, HBA, GWA, ARA, and SSA, the proposed boosted fuzzy classifier achieves an impressive accuracy of 93% and notable improvements in performance metrics, underscoring its superiority over classic fuzzy classifiers.