Pavement Crack Research Articles

GMDNet: An Irregular Pavement Crack Segmentation Method Based on Multi-Scale Convolutional Attention Aggregation

Pavement cracks are the primary type of distress that cause road damage, and deep-learning-based pavement crack segmentation is a critical technology for current pavement maintenance and management. To address the issues of segmentation discontinuity and poor performance in the segmentation of irregular cracks faced by current semantic segmentation models, this paper proposes an irregular pavement crack segmentation method based on multi-scale convolutional attention aggregation. In this approach, GhostNet is first introduced as the model backbone network for reducing parameter count, with dynamic convolution enhancing GhostNet’s feature extraction capability. Next, a multi-scale convolutional attention aggregation module is proposed to cause the model to focus more on crack features and thus improve the segmentation effect on irregular cracks. Finally, a progressive up-sampling structure is used to enrich the feature information by gradually fusing feature maps of different depths to enhance the continuity of segmentation results. The experimental results on the HGCrack dataset show that GMDNet has a lighter model structure and higher segmentation accuracy than the mainstream semantic segmentation algorithms, achieving 75.16% of MIoU and 84.43% of F1 score, with only 7.67 M parameters. Therefore, the GMDNet proposed in this paper can accurately and efficiently segment irregular cracks on pavements that are more suitable for pavement crack segmentation scenarios in practical applications.

Deep Learning based Pavement Crack Detection System

The pavement crack causes the highway service life to shorten, the safety hidden danger to increase. The low efficiency and high cost of manual inspection makes it difficult to detect pavement cracks. This paper proposes a fast and efficient deep learning pavement crack detection system. CRACK2000, an image segmentation dataset with complex interference background and multiple crack types, is constructed based on perspective transformation and image cropping. The scheme corrects the pavement crack images by perspective transformation. The extraction of pavement crack depth features is completed by applying the U-Net network. Finally, the pavement condition index PCI (pavement condition index) is calculated by quantifying the different types of crack information based on the segmentation results. The experimental results show that the Precision, Recall, F1-score and AUC of the U-Net network are 76.67%, 72.32%, 74.43% and 99.46% respectively. The AUC values reflect that the method is more capable of filtering out complex background interference from cracked images. The automatic pavement crack detection system designed in this paper can accurately locate and classify the location and category of pavement cracks, and perform quantitative pavement evaluation to obtain the pavement deterioration of the road section and the corresponding repair recommendations, enhancing the practicality of pavement crack detection.

Proposed severity ranking for ASTM E3303-21 protocol to quantify asphalt pavement cracking from automated 3D surveys

Cracking is one of the essential indicators to evaluate pavement surface conditions; however, it is challenging to rate pavement cracking automatically from 3D digital images. Recently, a two-level automated crack rating system was proposed for pavement management in Singapore where Level 1 provides detailed crack information including cracking extent, types, and severity. Level 2 is a macro-indicator ranging from 0 to 5 based on crack extent over a 10-m length pavement section, with 0 being excellent condition and 5 being very bad condition. On the other hand, the new ASTM E3303-21 standard has introduced the Pavement Surface Cracking Metric which is a dimensionless measure equivalent to crack density and the Pavement Surface Cracking Index which provides ratings of pavement cracking ranging from 0 to 100, with 0 being the worst possible condition and 100 being the best possible condition. This study was conducted to compare and potentially bridge the gaps between the two mentioned cracking rating methods. Cracking data were collected from the Singapore road network using the Laser Crack Measurement System-2 (LCMS-2). Based on the study findings, three severity ranks (low, medium, and high) were proposed to facilitate the inclusion of the ASTM E3303-21 cracking protocol into Pavement Condition Index calculations.

Utilization of rubber aggregates in cement-treated bases: A review

Cement-treated bases are commonly used in high-classified road pavements due to their stable strength and improved durability. However, this type of pavement base is sensitive to shrinkage caused by early water loss or temperature change after construction, resulting in shrinkage cracks and subsequent reflective cracking in semi-rigid pavements. To address this issue, various waste or by-products, such as oil, steel slag, end-of-life tires etc., have been investigated for use as an additive or a replacement for aggregates in cement-treated base. This review focuses specifically on the impact of rubber aggregates on the properties of rubberized cement-stabilized aggregates. In general, rubber aggregates are detrimental to the mechanical properties of cement-treated aggregates. However, the rubberized cement-stabilized aggregates exhibited reduced shrinkage and improved shrinkage cracking resistance, as confirmed through actual field applications. Furthermore, internal microstructure analyses indicate that rubberized cement-stabilized aggregates possess excellent anti-cracking capacity, particularly when subjected to compressive loadings. The review ends with recommendations and future studies to promote the applicability of rubberised cement-stabilized bases in semi-rigid pavements.

An Improved Crack Identification Method for Asphalt Concrete Pavement

The results of high-precision asphalt concrete pavement crack identification can provide help for pavement maintenance. Therefore, methods of image feature enhancement and crack identification of asphalt concrete pavement cracks are proposed. First of all, we used an industrial CCD camera mounted on a vehicle to collect an asphalt concrete pavement crack image. Then, after using the NeighShrink algorithm to denoise the acquired image, a fractional differential image enhancement algorithm was designed based on image feature blocks to enhance the image features. On this basis, crack characteristics were segmented and processed by watershed algorithm. Through crack direction identification and crack parameter extraction, crack distribution direction, crack length and width and other parameters of asphalt concrete pavement were obtained in order to achieve accurate identification of asphalt concrete pavement cracks. The experiment found that this method can effectively remove noise information from asphalt concrete crack images; after applying this method, the image entropy value of each image was improved, with a minimum improvement of 0.38 and a maximum improvement of 1.98. The time consumed by this method in identifying cracks in asphalt concrete pavement varied between 1.4 s and 2.4 s. When identifying the length of cracks in asphalt concrete pavement, the maximum deviation value was only 0.47 mm; when identifying the width of cracks in asphalt concrete pavement, the maximum deviation value was only 0.31 mm. The above results indicate that by enhancing the image features of asphalt concrete cracks, this method achieves more accurate identification results for crack distribution direction, length and width values, with high identification efficiency and good application effect.

Research on Real-Time Detection Algorithm for Pavement Cracks Based on SparseInst-CDSM

This paper proposes a road crack detection algorithm based on an improved SparseInst network, called the SparseInst-CDSM algorithm, aimed at solving the problems of low recognition accuracy and poor real-time detection of existing algorithms. The algorithm introduces the CBAM module, DCNv2 convolution, SPM strip pooling module, MPM mixed pooling module, etc., effectively improving the integrity and accuracy of crack recognition. At the same time, the central axis skeleton of the crack is extracted using the central axis method, and the length and maximum width of the crack are calculated. In the experimental comparison under the self-built crack dataset, SparseInst-CDSM has an accuracy of 93.66%, a precision of 67.35%, a recall of 66.72%, and an IoU of 84.74%, all higher than mainstream segmentation models such as Mask-RCNN and SOLO that were compared, reflecting the superiority of the algorithm proposed in this paper. The comparison results of actual measurements show that the algorithm error is within 10%, indicating that it has high effectiveness and practicality.

Automatic quantitative recognition method for vertical concealed cracks in asphalt pavement based on feature pixel points and 3D reconstructions

Non-destructive detection and characterisation of concealed cracks in asphalt pavements is a key aspect of ground-penetrating radar (GPR) road maintenance. This paper proposes a method based on feature pixel points to quantify and calculate the vertical height of cracks. Firstly, the team used gprMax to conduct numerical simulations to study the GPR image characteristics of concealed cracks in asphalt pavement with varying lengths and widths. Subsequently, the relationship between the pixel value of the crack area and the two-way travel time was established to obtain the relationship between the vertical height of the crack and the pixel. This study combined with the deep learning model (YOLOv5) allows for the calculation of the vertical height of a crack while simultaneously recognizing it, with the minimum error being only 1.3 %. Finally, concealed cracks were visualized in three-dimensional(3D) by slicing the results of vertical crack simulations of asphalt pavement and observing cloud images, and the principle of computed tomography(CT) was employed to reconstruct 3D models of cracked asphalt pavement and estimate the vertical height of the cracks. This method achieved a minimum error of only 2.9 %. The research presents a theoretical framework for recognizing and more intuitive characterizing concealed cracks in asphalt pavement accurately, enabling it to be used in practical engineering applications.

Pavement crack detection based on point cloud data and data fusion.

The three-dimensional detection in point cloud data for pavement cracks has drawn the attention of many researchers recently. In the field of pavement surface point cloud detection, the key tasks include the identification of pavement cracks and the extraction of the location and size information of pavement cracks. Based on the point cloud data of pavement surface, we developed two methods to directly extract and detect cracks, respectively. The first method is based on the improved sliding window algorithm by combining the random sample consensus (RANSAC) technique to directly extract the crack information from point clouds. The second method is developed based on YOLOv5 to process the two-dimensional images transformed from point cloud data for automatic pavement crack detection. We also attempted to fuse the point cloud images with greyscale images as input for the YOLOv5. Analysis results show that the improved sliding window algorithm efficiently extracts pavement cracks with less noise, and the YOLOv5-based method obtains a good detection of pavement cracks. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model.

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

Crack inspection is important to monitor the structural health of pavement structures and make the repair process easier. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. To solve the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates the pavement and collects visual images from the surface. A deep-learning-based semantic segmentation framework named RCDNet was proposed. The RCDNet was implemented on the onboard computer of the robot to identify cracks from the visual images. The encoder-decoder architecture was utilized as the base framework of the proposed RCDNet. The RCDNet comprises a dual-channel encoder and a decoder module. The encoder and decoder parts contain a context-embedded channel attention (CECA) module and a global attention module (GAM), respectively. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested in both indoor and outdoor environments. The robot was observed to complete the inspection of a 3 m × 2 m grid within 10 min and a 2.5 m × 1 m grid within 6 min. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map was generated using the visual image results. This map highlights areas that require greater attention for repair in the test grid.

Improved YOLOV5-Based UAV Pavement Crack Detection

In terms of highway crack detection, the combination of UAV and deep learning network has become a powerful detection means. However, in the actual detection, in order to take into account the detection efficiency, it is necessary to ensure that the detection area is large enough, which makes the crack occupy few pixels in the image, and the image background is complex. Therefore, in this paper, DJI Mavic3 is used to establish the image data set of highway pavement cracks under complex background. And, the YOLOV5 deep learning model is improved by adding swin transformer structure and BIFPN feature pyramid. The improved YOLOV5 model achieved real-time pixel-level detection with detection accuracy of 90% and detection speed of 43.5 FPS. In terms of crack detection ability, the accuracy of the improved YOLOV5 reaches 4 pixels, and cracks of 1.2 mm can be detected in the experiment. Compared with the YOLOV7 model, the detection accuracy of the improved YOLOV5 model is increased by 15.4%. Compared with the YOLOV6 model, the calculated parameters of the improved YOLOV5 model are reduced by 59.25%. The proposed model is superior to other advanced models in crack detection.

Method and Research of Concrete Pavement Crack Detection Based on Convolution Neural Network

Method and Research of Concrete Pavement Crack Detection Based on Convolution Neural Network

Mechanical Characteristics and Correlation Analysis of Crack Propagation and Slip Cracking in Asphalt Pavement Surface Layer

With the rapid development in the field of road traffic, the problems of slip cracking and U-shaped cracks on the surface of asphalt pavement are becoming more and more prominent, which seriously affect the safety and service life of roads. In order to solve this problem, a reliable asphalt pavement model is established in this study by combining the joint simulation and secondary development of experimental test parameters. The model can accurately simulate the slip cracking of asphalt pavements under different conditions and provide detailed parametric analysis of the factors affecting crack development. The results show that the inter-ply bond damage no longer deteriorates when the inter-ply strength is 0.3 MPa, indicating that the form of inter-ply contact plays a role in controlling the development of slip cracks. In addition, the maximum interlayer shear stress increased by 21.1% during the increase of vehicle axle load from 100 kN to 200 kN on the asphalt pavement, which led to an increase in the length of slip cracks. Meanwhile, the maximum longitudinal tensile stress in the asphalt pavement surface layer increased by 10.2% and the maximum shear stress increased by 18.3% when the elastic modulus of the asphalt pavement surface layer was increased from 7500 MPa to 14500 MPa.

Automatic pavement crack segmentation using a generative adversarial network (GAN)-based convolutional neural network

Due to the increasing demand on road maintenance around the whole world, advanced techniques have been developed to automatically detect and segment pavement cracks. However, most of methods suffer from background noise or fail in fine crack segmentation. This paper proposes a generative adversarial network (GAN)-based neural network named CrackSegAN to segment pavement cracks automatically. The generator of CrackSegAN generates segmentation results, while the discriminator trains the generator adversarially. A joint loss function is proposed to optimize the generator with sufficient gradients and mitigate the high class imbalance in pavement crack images. Elastic deformation data augmentation method is applied to force CrackSegAN to learn the transformation invariance. The proposed CrackSegAN reaches an average F1 score of 0.9780 on CrackForest dataset and 0.8412 on Crack500 dataset. Ablation study shows that the most prominent difference is made by the proposed joint loss function which increases the average F1 score by 8.98% on CrackForest dataset. Besides, the comparison between using different data augmentation strategies validates the effectiveness of elastic deformation. Overall, the proposed CrackSegAN increases the F1 score by 1.91% on CrackForest dataset and 1.01% on Crack500 compared with state-of-the-art methods. Qualitatively, CrackSegAN is more robust to background noises and segments cracks with more details. Moreover, the test on field data proves a better generalizability of CrackSegAN on unseen background noises.

Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety. The task is challenging because the shadows on the pavement may have similar intensity with the crack, which interfere with the crack detection performance. Till to the present, there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows. To fill in the gap, we made several contributions as follows. First, we proposed a new pavement shadow and crack dataset, which contains a variety of shadow and pavement pixel size combinations. It also covers all common cracks (linear cracks and network cracks), placing higher demands on crack detection methods. Second, we designed a two-step shadow-removal-oriented crack detection approach: SROCD, which improves the performance of the algorithm by first removing the shadow and then detecting it. In addition to shadows, the method can cope with other noise disturbances. Third, we explored the mechanism of how shadows affect crack detection. Based on this mechanism, we propose a data augmentation method based on the difference in brightness values, which can adapt to brightness changes caused by seasonal and weather changes. Finally, we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters, and the algorithm improves the performance of the model overall. We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset, and the experimental results demonstrate the superiority of our method.

Pavement Crack Detection Based on 3D Edge Representation and Data Communication With Digital Twins

With digital information applied in intelligent transportation system, pavement crack detection with digital twins has drawn widely attention since the past several years. However, it is still a challenge task to accomplish crack detection satisfactory results due to complex pavement background. This paper presents a pavement crack detection method based on 3D edge representation and data communication with digital twins. Firstly, pavement data is achieved by multiple sensors including video data and 3D scanning technology, and the digital twins based model is constructed based on data communication. Secondly, to distinguish between crack feature and pavement background in depth level, the point cloud data enhancement method based on fractional differential is proposed to strengthen crack boundary and maintain detail simultaneously. Thirdly, a 3D crack segmentation algorithm is proposed based on edge feature representation. After improved filter function with plane proximity and depth similarity, the gradient magnitude values of every point are estimated by the neighborhood and direction. Two thresholds are computed automatically using the iteration method to extract the crack from pavement. Finally, the dataset is established by 2600 crack data, and extensive experiments verify the proposed method could conduct better result compared with other existing methods using F-measure evaluation system.

Study of the Bonding Performance and Application of an Epoxy Resin Pavement Sealant

An epoxy resin pavement sealant (ERPS) was prepared to solve the interface bonding failure problem of traditional road sealants. The fluidity, bonding performance, and low temperature performance of sealants repaired struture were studied based on surface energy theory, fluidity tests, tensile tests, beam bending tests, and oblique shear tests. The results show that ERPS has good excellent fluidity and can meet the construction time. Under the most unfavorable condition of water, mineral powder, and low temperature, the tensile bond strength, flexural bond strength, and oblique shear bond strength of ERPS repair specimens were 1.84, 7.21, and 8.02 MPa, respectively, and the strength at each temperature was better than styrene-butadiene-styrene (SBS)-modified asphalt, silicone sealant, and polyurethane sealant. Under the most unfavorable conditions, the ERPS-repaired cracks recovered 68.1% of the tensile strength, 67.8% of the flexural strength, and 70.5% of the oblique shear strength of the undamaged specimens. The ERPS-repaired pavement crack wall structure has excellent low-temperature bonding performance and good high-temperature shear performance, which shows that ERPS can effectively solve the problem of adhesive failure sealants at low temperatures.

Applicability and Cost Implication of Labor-Based Methods for Sustainable Road Maintenance (SRM) in Developing Countries

Regular maintenance keeps roads in a good working condition for users. Road maintenance can be conducted using labor and machine based methods. In this study, applicability and costs of labor-based, namely employment intensive (EI), labor intensive (LI), and labor-based equipment-supported (LBES) road maintenance methods were investigated with a case study in Tigray region, northern Ethiopia. The objectives of this study were therefore to (a) identify road defects and outline the corresponding labor-based maintenance methods, (b) compare relative costs of the three labor-based road maintenance methods, and (c) explore indicators of sustainable road maintenance (SRM) practices for developing countries. The results of this study showed that the labor-based methods are suitable for maintaining a wide range of road defects such as pavement cracks, bleeding, potholes, and rutting. Results of the cost analysis revealed that the LI and EI were 6% and 36% cheaper than the LBES method for repairing shoulder damage in asphalt roads. Roadside bush clearing using LBES resulted in 40% and 147% cost saving compared to EI and LI methods, respectively. Average share of labor cost compared to combined cost of labor and equipment in EI, LI, and LBES was 97%, 79%, and 37%, respectively. Unit rates of maintenance activities for different labor-based methods were also developed for Tigray region. The developed road maintenance unit rates will be useful for estimating cost of labor-based methods in Tigray and other similar regions in Ethiopia. In this study, 45 suitability indicators under 10 categories and three themes (social, economic, and environmental) were developed to improve future road maintenance practices in developing countries. Finally, it is recommended to use SRM practices to increase sustainability of road infrastructure in developing countries.

Investigation of asphalt pavement crack propagation based on micromechanical finite element: A case study

Cracking is a prevalent issue in expressway pavements in Shanxi Province, exhibiting various types. Based on field investigations, the primary types of asphalt pavement cracks observed in Shanxi expressways can be categorized as top-down crack (TDC, surface crack) and reflection crack (bottom-top crack). These two types differ significantly in terms of cracking mechanisms, development modes, treatment methods, and impact severity. This paper aims to simulate the initiation and propagation processes of top-down cracks and reflection cracks using the finite element method, aiming to provide a basis for distinguishing between the two. In this paper, a microstructure numerical model of a three-phase asphalt concrete pavement is established. Finite element software is utilized to simulate the initiation and propagation of top-down cracks and reflection cracks on asphalt pavement, while investigating the influence of various factors on crack propagation. The findings demonstrate that top-down cracks exhibit multi-point cracking on the pavement surface, resulting in a tortuous and discontinuous development process. Conversely, reflection cracks display significant stress concentration at the crack tip, leading to propagation even under loads significantly lower than the material's tensile strength, following a single propagation path. Aggregate distribution form, preset crack length, tensile strength of ITZ, and load distribution form do not have a significant effect on the primary cracking mode. However, they do influence the crack initiation point, cracking load, and propagation path to some extent.

A Highway Pavement Crack Identification Method Based on an Improved U-Net Model

Crack identification plays a vital role in preventive maintenance strategies during highway pavement maintenance. Therefore, accurate identification of cracks in highway pavement images is the key to highway maintenance work. In this paper, an improved U-Net network adopting multi-scale feature prediction fusion and the improved parallel attention module was put forward to better identify concrete cracks. Multiscale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus using global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, which can give more weight to the target region in the image and further capture the global contextual information of the image to improve the recognition accuracy. Improving the bottleneck layer is used to improve the robustness of the model and prevent overfitting. Experiments show that the improved U-Net network in this paper has a significant improvement over the original U-Net network. The performance of the proposed method in this paper was investigated on two publicly available datasets (Crack500 and CFD) and compared with competing methods proposed in the literature. Using the Crack500 dataset, the method in this paper achieved the highest score in precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). Similarly, for the CFD dataset, the method in this paper achieved high values for precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%). Thus, the method has several advantages for identifying cracks in highway pavements and is an ideal tool for practical work. In future work, identifying more crack types and model light-weighting are the key objectives. Meanwhile, this paper provides a new idea for road crack identification.