Cracking In Asphalt Pavements Research Articles

Macroscopic properties and microscopic characterisation of an optimally designed anti-cracking stone base course filled with cement stabilised macadam

Stone base course filled with cement stabilised macadam (SFC) is a promising solution for addressing reflection cracks in semi-rigid base asphalt pavement due to its satisfactory crack resistance. However, its limited strength and construction challenges have impeded its widespread application. To overcome these challenges, an optimised mix proportion design method of SFC was proposed. Natural crushed limestones with specific particle sizes were selected to serve as skeletal coarse aggregates; fine aggregates and their proportions were step-by-step determined using aggregate and unconfined compressive strength tests. The multi-scale analysis integrating the performance tests and the microscopic detections was performed to evaluate the effectiveness of the proposed SFC, and compared with common cement stabilised macadam (CSM). The results indicate that the material exhibits satisfactory mechanical strength through a tightly interlocked skeleton structure and excellent crack resistance by dissipating strain energy and retarding cracking of SFC through the weak interface transition zone (ITZ).

MFAFNet: An innovative crack intelligent segmentation method based on multi-layer feature association fusion network

The regular and timely detection and segmentation of cracks in asphalt pavements is very important for road evaluation. However, certain problems remain to be solved, such as the difficulty of crack detection under interference from zebra stripes and dark light. A novel intelligent crack segmentation method based on a multi-layer feature association fusion network (MFAFNet) is therefore proposed in this study. Firstly, a fusion convolution with a Transformer called a feature coupling encoder (FC-Encoder) is developed in MFAFNet to enhance the model's global feature awareness and ability to capture local details. Secondly, a two-branch feature association module (TFBA-module) is proposed to obtain strong correlation information for global and local features. Next, a hybrid MLP architecture for lightweight feature decoder (H-MLP Decoder) is constructed to achieve feature recombination of the multi-level fusion information. The accuracy of crack segmentation under interference from zebra stripes and dark light is improved through the use of these three modules. Our model is compared with some well-known existing segmentation networks such as U-Net, DeepLabv3+, PSPNet, HRNet and SegFormer, and the segmentation accuracy, F1-score and IoU of the proposed MFAFNet are found to be 94.39 %, 93.26 % and 89.46 %, respectively. In addition, the segmentation efficiency reaches 21.87FPS. Our experimental results show that the proposed MFAFNet yields more effective performance in the field of asphalt pavement crack segmentation than the alternatives.

Microscale characterization of an anti-cracking stone base course filled with cement stabilized macadam

Large stone base course filled with cement stabilized macadam (LSFC) is a promising solution for addressing reflection cracks in semi-rigid base asphalt pavements due to its superior crack resistance. However, challenges associated with balancing strength and crack resistance, and mechanizing high-efficiency construction have hindered broader adoption. To address these issues, Wang et al. [1] proposed an optimized mix proportion design for an anti-cracking stone base course filled with cement stabilized macadam (SFC). In this study, various microscopic techniques were employed to explore the anti-cracking mechanism of SFC on a microscopic scale, and compared with the conventional cement stabilized macadam (CSM). These techniques included examining the structural morphology and pore distribution using polarizing microscope (PM) and nuclear magnetic resonance (NMR), analysing the elemental composition and hydration reaction in non-aggregate zones using energy dispersive spectroscopy (EDS), and testing the mechanical properties using atomic force microscope (AFM). The findings revealed, compared with CSM, SFC exhibits a more tightly interlocked coarse aggregate skeleton, which, in turns, resulting in weak interfacial transition zones (ITZs) with a high concentration of pores and defects, weak bonding, and a loose structure, potentially exceeding 20 µm in thickness. The tightly interlocked coarse aggregate skeleton in SFC effectively limits material shrinkage but also leads to localized high stress and weak interfacial damage within the vulnerable ITZ region, converting shrinkage strain energy into microcrack surface energy, thereby delaying the onset of cracking and ultimately enhancing the cracking resistance of the base.

A novel approach for automatic asphalt pavement crack length estimation

Estimation of the actual size of an object from its digital image can help to automate processes and increase productivity. One example of object-size measurement is evaluation of the pavement condition by estimating crack length. As input images taken by different cameras are often inconsistent, a robust method of automatically measuring objects is necessary. Here, the crack size has been estimated by combining the proposed ‘asphalt crack length extractor’ (ACLE) method and an object-size conversion method called ‘pixel to actual size conversion engine’ (PACE). In ACLE, image segmentation is performed to detect the object and extract its pixel-wise length from the image. In PACE, the pixel-wise measurements are converted to metric units. Several experiments were conducted to evaluate the proposed methods. The combined PACE/ACLE module showed an average error of less than 3%. Thus, it is proposed for use in crack length determination and condition assessment of asphalt pavements.

Impact of composition and knitting methods of fiberglass grid on the progression of reflective crack in asphalt pavement

In the asphalt pavement with semi-rigid base, the shrinkage cracks initiating from base course often propagate upward to surface course to develops into reflective cracks. The fiberglass grid, as a woven geotextile, is widely used to retard the occurrence of reflective cracks in China, but the impacts of material composition and knitting methods of it on reflective cracks are still not clear. In this paper, two kinds of models, including a two-scale model of fiber bundle-fiberglass grids layer and a coupled thermo-mechanical model of pavement structure, are established. In the two-scale model, the elastic constants (ECs) of fiber bundle and fiberglass grids layer are calculated under different compositions and knitting methods and then assigned to the coupled thermo-mechanical model to estimate the fatigue lives at initiation phase (FL-IP) and propagation phase (FL-PP) of reflective cracks based on an estimation theory. Moreover, this study designs the laboratory tests to validate this estimation method of fatigue life. Then, based on the predicted fatigue life at different phases of crack progression, the sensitivity analysis is implemented to analyze the impacts of composition and knitting methods on the fatigue life. As a result, the recommendations on design and preparation of fiberglass grid are proposed. The results show that, in material composition, the glass fibers with high elastic modulus and volume fraction and the coating materials with high elastic modulus are beneficial to retarding the initiation and propagation of reflective cracks and in knitting methods, increasing density of fiber bundles parallel to the traffic direction and reducing braided angle also has positive effect. These results can be used to optimizing design and preparation of fiberglass grid to reduce the occurrence of reflective cracks in asphalt pavement.

U-Net-based integrated framework for pavement crack detection and zone-based scoring

ABSTRACT Pavement scoring systems are cost-effective means of integrating the pavement management to prevent the progression of failure. The current research developed a pavement scoring system in which the presence and severity of longitudinal and transverse cracks in asphalt pavements are detected through digital images. The pavement cracks were identified using U-Net-based network architecture optimised by the grasshopper optimisation algorithm. The quantum geographic information system was used to reveal the cracks on the study zones. These zones then were evaluated, compared and scored according to their mean crack length, mean crack width and the number of cracks. The analytic hierarchy process based on expert opinion was employed to determine the importance and weight of these factors. Each area received a score based on the condition of the pavement that enabled comparison and identification of the critical areas. The proposed method can allow transportation agencies to identify and evaluate critical areas and determine the proper maintenance and repair priorities.

Thermoreversible aging in asphalt binders

It is an undisputed fact that aging increases the risk of low-temperature cracking in asphalt pavement. Previous studies on aging have focused on oxidative aging, but few studies on thermoreversible aging have been reported. The existing literature has shown that thermoreversible aging has the same effect as oxidative aging on cracking of asphalt pavement in cold region, so it is obvious that the study of thermoreversible aging is of great practical significance to improve cracking of asphalt pavement. This paper introduces the current status of thermoreversible aging research from the concept, mechanisms, characterisation methods and effects, presents the problems of existing research and indicates the direction of further research, which is important for accurately characterising the mechanical properties of asphalt binders, controlling the waste addition ratio and improving the durability of asphalt pavement.

GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine for accurate detection of asphalt pavement cracks

Asphalt cracks are the initial indication and major pavement structural distress in the field of civil engineering because it might potentially intimidate road traffic and highway safety. Managing and maintaining pavement structures are the two crucial issues faced eventually by road administrators. It is essential to take immediate action regarding pavement structural damages to prevent its severity. In recent times, there established numerous deep learning-based image processing methods to detect pavement cracks, but due to the presence of interference factors such as noises and road markings in the images, those methods failed to provide high accuracy and efficiency. Therefore, with the aim to yield high accuracy of crack detection, this article designs a novel asphalt pavement crack identification system “GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine” (GNet TL with IGT-KELM). The road crack images used for perusal are acquired from NHA12D dataset, in which it consists of 40 asphalt pavement images with two different viewpoints. For the purpose of data balancing, some non-crack images are gathered by field survey. The images with large noise distortions and unwanted background information influence crack detection performance so that the preprocessing steps are executed before applying it to the prediction system. The most significant crack and non-crack features from the images are extracted using GNet TL model. With the extracted features, the KELM is well-trained to detect cracks and non-cracks separately. To increase crack detection performance of the classifier, an improved gorilla troop optimizer is introduced to optimize the KELM parameters. The experimental finding reveals that the proposed detection mechanism achieves crack recognition accuracy of about 98.93%, which is considerably greater than compared baseline approaches.

Research on the response of bitumen binder and bitumen mastic to physical hardening effect based on rheological properties

Bitumen materials will occur a physical hardening effect when stored isothermally in a low-temperature environment, and ignoring the physical hardening factor has become one of the important reasons for premature cracking of asphalt pavement during the service period. This paper investigates the response of bitumen binder and bitumen mastic to physical hardening effects at different storage temperatures and times using the bending beam rheometer and dynamic shear rheometer. The response of the material to physical hardening effects is analyzed through temperature index system changes, mathematical model fitting, and stiffness master curve fitting. Results indicate that the response of the material to physical hardening effects can be classified into two stages: the rapid growth and the gradual stabilization periods. The response time of these two stages is related to factors such as aging degree and storage temperature. The physical hardening effect will cause the temperature loss of the material at low temperature, and its influence law on TS60=300MPa and Tm60=0.300 is different. It is recommended to extend the isothermal storage time specified in the test from 1 h to 9 h, considering the time cost and the change in temperature index. Moreover, the stiffness master curve study revealed the occurrence of the "tail drift" phenomenon.

Evaluating the low-temperature performance of asphalt mixtures based on the contraction and relaxation properties

The thermal cracking of asphalt pavement has been the focus of research in cold regions of the world. The contraction and relaxation properties of asphalt mixtures were crucial for investigating the thermal stress of asphalt pavement based on the thermal cracking mechanism. This paper proposed a novel evaluation index for characterizing the low-temperature performance of asphalt mixtures by considering their contraction and relaxation properties. Firstly, the rank during the evaluation indexes for low-temperature performance was determined by comparative analysis of method. Secondly, the thermal contraction coefficient was used to characterize contraction properties and cumulative stress at per unit strain rate (CSSR) was used to characterize relaxation properties in this paper. According to the cumulation process of thermal stress, a novel index was constructed based on the contraction and relaxation properties. Finally, considering the correlation between the failure temperature and the novel indexes, the prediction model of low-temperature performance consistent with the order of failure temperature was established based on the multiple linear regression method. Through the standardized analysis of the prediction model, the influence of novel indexes for the low-temperature performance of asphalt mixtures under different temperature conditions was clarified. A novel evaluation index provided a theoretical basis for unifying the evaluation indexes of asphalt mixtures.

The Classification of Asphalt Pavement Crack Images Based on Beamlet Transform

Pavement cracking is a common road infrastructure issue which significantly affects road performance, safety and longevity. This article employed a Beamlet Transform algorithm to detect and classify different types of flexible asphalt concrete pavement cracks. Additionally, a dedicated crack segmentation network was employed for precise segmentation of pavement crack. This approach incorporates advancements that has improve precision in crack classification and segmentation. Based on the results of the beamlet transform, significant improvements in the gray scale representation of crack, enhanced crack detection, reduced noise in crack images and a more precise measurement of cracks length were achieved. Computations were performed to determine the length of linear cracks and the area of block cracks. A total of 1000 pavement images were used for training and testing the accuracy of asphalt pavement crack detection and classification models. The research results showed that block cracking, alligator cracking, transverse cracking, and longitudinal cracking can all be recognized with a remarkable accuracy. Alligator cracks and block cracks achieved detection rates more than 90%, while detection rates for the longitudinal and transverse cracks reached more than 95% accuracy.

Bitumen stress relaxation modulus and its slope as performance-based criteria to control low temperature cracking

ABSTRACT This paper aims to determine the performance-based criteria for bitumen to control low temperature cracking in asphalt pavements. For this purpose, the performance of 26 recovered bitumen samples was analysed using the dynamic shear rheometer with 4-mm parallel plates (4-mm DSR). The stress relaxation modulus and its slope at 60 s were determined by performing a stress relaxation test at specific critical temperatures. The changes in these properties depending on the test temperature were analysed. The stress relaxation modulus and its slope at 60 s of 26 recovered bitumen samples were compared with the actual performance of the road sections from which these bitumen samples were recovered. On the basis of that, the limiting values of the stress relaxation modulus and its slope at 60 s were determined. The research showed that the formation and development of low temperature cracks on asphalt pavements are limited, when the stress relaxation modulus and its slope at 60 s and at temperature 10°C higher than the lowest asphalt surface temperature are not higher than 52 MPa and not lower than 0.370, respectively. These performance-based criteria are incorporated into the algorithm developed to assess bitumen resistance to low temperature cracking.

Asphalt pavement crack detection based on infrared thermography and deep learning

ABSTRACT The current intelligent detection of asphalt pavement cracks relies on visible images, which are ineffective in handling temporary shadow and reflection. In contrast, infrared images reflect thermal radiation intensity, making them suitable for crack detection by mitigating environmental interference. The GSkYOLOv5 method is proposed in this study for accurate crack detection in infrared images of asphalt pavement. Firstly, an infrared dataset consisting of 2400 images of asphalt pavement cracks was captured and produced, including three types of cracks – strip cracks, cross cracks and reticular cracks. And the imbalanced samples were balanced using generative adversarial networks. Secondly, GhostNet and Selective Kernel Networks were added to enhance crack information perception, and the SPPF module was replaced to improve feature learning for different scale cracks. Lastly, GSkYOLOv5 was tested with a self-built infrared dataset of asphalt pavement cracks. GSkYOLOv5 improved detection accuracy by 4.7% and recall rate by 1.3% compared to YOLOv5s, outperforming the other tested algorithms. This method can be combined with visible image-based crack detection for comprehensive intelligent detection of asphalt pavement cracks using dual lenses for visible and infrared images.

Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation

A lightweight PCSNet-based segmentation model is developed to efficiently overcome insufficient performance in feature extraction and boundary loss information resulting from sampling operations. The proposed approach incorporates two key components: an enhanced shuffle unit and an improved inverted residual architecture to effectively reduce model parameters and enhance inference time. Additionally, introducing the generalized Dice loss (GDL) aims to address the prediction accuracy issue caused by category imbalance. Finally, this study employs gradient-based class activation mapping to visualize and interpret the learned features. To enhance model interpretability, experiments were conducted on three publicly available datasets and compared with existing popular segmentation models. The findings demonstrate that including the GDL in the lightweight PCSNet model leads to a significant improvement in prediction performance, with the highest mIoU reaching 78.93%. Additionally, the visualization of class activation mapping (CAM) further enhances PCSNet interpretability in terms of feature extraction.

Correlation analysis of longitudinal cracks and vertical deformation within asphalt pavement of cold regions

The asphalt pavement longitudinal crack is a common distress in cold regions, resulting from uneven deformation of the subgrade. Analysis of the correlation law between uneven deformation and crack distress is of positive significance for understanding the mechanism of crack initiation, and putting forward treatment measures. In view of the complexity of longitudinal crack inducement and road surface deformation, the grey relational method was used to analyze this relationship. Through long-term monitoring of the vertical deformation data of typical road sections, the vertical deformation law of the pavement surface and its deformation characteristics under the action of temperature field are analyzed. Parameters such as vertical relative deformation, vertical relative deformation rate and vertical differential deformation VDSr were constructed to describe vertical deformation characteristics. Typical distribution characteristics of longitudinal fractures and their length and distribution characteristics are also described. The grey correlation analysis theory was utilized to analyze the relationship between deformation characteristics of sections, cross sections and monitoring points and longitudinal crack characteristics (length and location). The analysis reveals a linear positive correlation or a high correlation between several indicators. This study can provide a deeper understanding of the occurrence and development mechanism of longitudinal cracks in asphalt pavement of cold areas, and give references for the research of road engineering structure, materials and distress prevention.

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing.

Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang-Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang-Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

Detection Method of Cracks in Expressway Asphalt Pavement Based on Digital Image Processing Technology

Considering the limitations of the current pavement crack damage detection methods, this study proposes a method based on digital image processing technology for detecting highway asphalt pavement crack damage. Firstly, a non-subsampled contourlet transform is used to enhance the image of highway asphalt pavement. Secondly, the non-crack regions in the image are screened, and the crack extraction is completed by obtaining and enhancing the crack intensity map. Finally, the features of cracks are extracted and input into the support vector machine for classification and recognition to complete the detection of cracks in highway asphalt pavement. The experimental results show that the proposed method can effectively enhance the quality of a pavement image and precisely extract a crack area from the image with a high level of damage detection accuracy.

Peanut Shell Powder as a Sustainable Modifier and Its Influence on Self-Healing Properties of Asphalt.

This paper investigated, for the first time, the feasibility of using peanut shell powder, a plant waste residue, as a modifier for asphalt, particularly its self-healing ability. Modified asphalt samples were prepared using varying particle size ranges and concentrations of peanut shell powder. Various tests, including fatigue-healing-fatigue tests, high- and low-temperature rheological property tests, penetration tests for conventional performance, and atomic force microscopy scans, were conducted to investigate the effects of peanut shell powder on the self-healing performance and other properties of asphalt. The results showed that the porous structure of peanut shell powder was able to absorb light components within the asphalt and release them under load, thus improving the self-healing and fatigue resistance properties of the modified asphalt. Experimental conditions such as temperature, healing time, and fatigue damage level also influenced the self-healing performance of asphalt. Additionally, peanut shell powder could increase the dynamic viscosity and high-temperature rheological property of modified asphalt while reducing its temperature susceptibility. However, it had a negative impact on the low-temperature ductility and creep rate, which could potentially lead to premature cracking of asphalt pavement in colder regions. Increasing the content of peanut shell powder and reducing its particle size within a certain range had positive effects. When the content of peanut shell powder was 4% and the particle size range was 80-100 mesh, the overall performance of modified asphalt was satisfactory.

Optimization of crack healing in steel bridge deck asphalt pavement based on the microwave heating technology

Microwave technology can heal cracks in asphalt pavements by heating the pavement. When it is used to repair cracks in steel deck asphalt pavement (SBDAP), the temperature of the SBDAP is greatly increased because the steel deck reflects the microwaves, allowing the microwaves to reheat the SBDAP. This study established a numerical model for microwave-heated SBDAP, and analyzed the effects of the waveguide height above the pavement surface, the thickness of the SBDAP, microwave power and heating time on the average of the temperature gradient (Tavg) and the standard deviation of the temperature gradient (Tstd) of the SBDAP. The feasibility of the numerical model was validated through the laboratory microwave heating tests. The prediction models of Tstd and Tavg under the effect of four influencing factors were developed by response surface methodology, and the validity of the prediction models was verified based on the analysis of R2 values and analysis of variance. With the prediction models of Tavg and Tstd as optimization functions, the optimal combination of the aforementioned influencing factors was determined using the genetic algorithm. In summary, the results of this study can effectively guide the field construction of microwave heating technology to repair SBDAP cracks.

Characterization of Self-healing Properties of Asphalt Pavement Materials Containing Carbon Nanotubes: from the Binder and Mix Level based on Grey Relational Analysis

Asphalt pavement cracks not only reduce road life and affect traffic safety but also increase maintenance costs. These issues can be solved significantly if microwave heating (MH) technology is applied based on the self-healing mechanism. This work aims to adopt carbon nanotubes (CNTs) to modify asphalt binders to accelerate their self-healing properties. Thermal conductivity and diffusivity, high-temperature rheological characteristics, and fatigue resistance were conducted to investigate healing potential from binders level; semicircular bending (SCB) tests and MH test were used to assess the fracture and self-healing properties from mix level; Grey relational analysis (GRA) was employed to study the relevance of asphalt binder properties on the healing properties of mixes. Results indicated that CNTs could improve asphalt binders' thermal conductivity, thermal diffusivity, high-temperature deformation resistance, and anti-fatigue properties. However, the Jnr-diff results suggest that the content of CNTs needs to be controlled within 1.0%. From the mixtures perspective, samples with 1.0% content of CNTs can achieve the best fracture performance and healing efficiency with excellent heating uniformity and no overheating problems. Furthermore, thermal performance strongly correlates with mixtures' healing indexes. This research addresses the maintenance of asphalt pavement cracks, which is essential for long-life pavements and sustainable road development.