Cracking In Concrete Pavements Research Articles

A Multi-Stage Feature Aggregation and Structure Awareness Network for Concrete Bridge Crack Detection.

One of the most significant problems affecting a concrete bridge's safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolutional methods with square kernels struggle to capture crack features effectively, fail to perceive the long-range dependencies between crack regions, and have weak suppression ability for background noises, leading to low detection precision of bridge cracks. To address this problem, a multi-stage feature aggregation and structure awareness network (MFSA-Net) for pixel-level concrete bridge crack detection is proposed in this paper. Specifically, in the coding stage, a structure-aware convolution block is proposed by combining square convolution with strip convolution to perceive the linear structure of concrete bridge cracks. Square convolution is used to capture detailed local information. In contrast, strip convolution is employed to interact with the local features to establish the long-range dependence relationship between discrete crack regions. Unlike the self-attention mechanism, strip convolution also suppresses background interference near crack regions. Meanwhile, the feature attention fusion block is presented for fusing features from the encoder and decoder at the same stage, which can sharpen the edges of concrete bridge cracks. In order to fully utilize the shallow detail features and deep semantic features, the features from different stages are aggregated to obtain fine-grained segmentation results. The proposed MFSA-Net was trained and evaluated on the publicly available concrete bridge crack dataset and achieved average results of 73.74%, 77.04%, 75.30%, and 60.48% for precision, recall, F1 score, and IoU, respectively, on three typical sub-datasets, thus showing optimal performance in comparison with other existing methods. MFSA-Net also gained optimal performance on two publicly available concrete pavement crack datasets, thereby indicating its adaptability to crack detection across diverse scenarios.

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.

A dynamic semantic segmentation algorithm with encoder-crossor-decoder structure for pixel-level building cracks

A large number of newly built infrastructures as well as those constructed in the early stage are faced with the problems of detection and maintenance. However, it is difficult to detect building cracks because of its small size and complex background noise. In this study, a crack segmentation network based on Encoder-Crossor-Decoder structure is innovatively proposed to solve the problems of small cracks and easy to be disturbed by background. Then, a loss function is proposed to address the problem of large differences in the ratio of cracks to background pixels in architectural crack segmentation. The experiments show that the loss function can effectively improve the training effect of the model and make the model obtain better semantic segmentation ability. Finally, according to the requirements of building crack detection, a large dataset of concrete pavement cracks is produced, which fills the gap of large dataset of semantic segmentation of cracks. The excellent effect of the model and loss function is verified with three datasets containing most of the major material and structural scenes. In addition, we compare the model with other deep learning segmentation models to validate its effectiveness. The results show that the mIoU of the model of this study reaches 84.04%, 77.56% and 87.38% in the bridge non-steel crack dataset, steel surface crack dataset and our concrete crack dataset, respectively. The accuracy reaches 99.14%, 98.62% and 99.37%. F1 reaches 0.911, 0.873 and 0.963 respectively. It outperforms other deep learning based segmentation methods.

USE OF STEEL FIBER AND COPPER SLAG IN CONCRETE PAVEMENT

This study explores the utilization of steel fiber as a reinforcement material in concrete pavement construction. The investigation delves into the mechanical and structural properties of steel fiber-reinforced concrete (SFRC) and its impact on the performance of pavement structures. Various proportions of steel fibers are incorporated into the concrete mix, and the resulting mixtures are subjected to comprehensive testing, including compressive strength, flexural strength, and fatigue resistance. The study aims to assess the potential enhancement in the durability and load-bearing capacity of concrete pavements through the incorporation of steel fibers. The findings indicate that the addition of steel fibers contributes to increased tensile strength and crack resistance, addressing common issues such as reflective cracking and fatigue failure in concrete pavements. Moreover, the study investigates the economic viability and sustainability aspects associated with the use of steel fiber in concrete pavements, considering its influence on lifecycle costs and environmental impact. The outcomes provide valuable insights for engineers and practitioners seeking optimized solutions for durable and resilient concrete pavement construction. Keywords: Steel fibers, Concrete pavement, Reinforcement, Mechanical Properties, Durability,Structural Performance, Compressive strength, Flexural Strength,Fatigue resistance, Reflective Cracking,Sustainability.

A reliability-base method for thermal cracking prediction in asphalt concrete

This study focuses on understanding the impact of environmental factors that contribute to the deterioration and damage of Hot Mix Asphalt (HMA) pavements. One significant problem faced by HMA pavements is transverse (thermal) cracking, which refers to the formation of cracks perpendicular to the centerline of the pavement. Over the past few decades, researchers have put significant effort into analyzing and understanding how thermal cracking affects HMA pavements. Mechanistic-empirical models play a crucial role in this process, but they require a substantial amount of input data related to the pavement's structural and material properties. Obtaining this data can be done through various methods with different levels of quality, which affect the reliability of the pavement performance responses. The probabilistic base designs and specifications can consider the variation of input data and the impact of material and structural together with environmental parameters on pavement deterioration. In this project, transverse cracking, as one of the major asphalt concrete pavement cracking, particularly in the freezing areas, will be studied, and a reliability-based method will be introduced. To reach this goal, the long-term pavement performance (LTPP) database was used. A regression model from the key parameters of pavement material and structural properties and environmental and traffic indicators was generated. Then through the structural reliability method, the probability of overpassing of transverse cracking amount from the threshold for a pavement design was assessed.

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.

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

Study Effects of Gradation and Material Thermal Property of Chip Seal Aggregates in Roller Concrete Pavement Crack Healing by Image Processing and RMS.

One of the most roller cement concrete pavement failures of pavement is the formation of first cracks. The roughness of its completed surface after the installation has restricted the usage of this pavement. Therefore, engineers increase the quality of service of this pavement by placing a layer of asphalt coating; The primary goal of this study is to evaluate the impact of particle size and type of chip seal aggregates on filling cracks in rolled concrete pavement. Accordingly, rolled concrete samples with chip seal covering were prepared with various aggregates (limestone, steel slag, and copper slag). Then, the influence of temperature on its self-healing ability was tested by putting the samples in the microwave device for cracking improvements. With the aid of Design Expert Software and image processing, the Response Surface Method reviewed the data analysis. Even though due to the study's limitations, a constant mixing design was applied, the results of this study indicate that the amount of crack filling and repair in specimens slag is higher than that of aggregate materials. With the increase of steel and copper slag, 50% of repair and crack repair at 30 °C, the temperature is 27.13% and 28.79%, respectively, and at 60 °C, the temperature is 58.7% and 59.4%, respectively.

Investigations of the optimal requirements for curing of calcium sulfoaluminate cement systems

Wet curing improves ordinary portland cement (OPC) concrete durability and strength by increasing total hydration, densifying microstructure, and decreasing concrete permeability. In general, wet curing is recommended for OPC until it gains >70% of the designed compressive strength, typically for at least 7 days. Calcium sulfoaluminate (CSA) cement may allow for decreased curing time requirements due to its different phase composition from that of OPC, rapid hydration, and strength gain. Rapid hydration may also prevent some disadvantages associated with OPC curing requirements, such as long curing times, costs associated with the use of high-water quantities, and supervision needs for curing processes. In addition, it is important to understand the effect of use of various curing regimes that are currently specified for use with OPC when applied in CSA systems. This study investigated a variety of curing durations and curing solution compositions to understand their effects on CSA hydration, strength development, and shrinkage. The results demonstrate that 3-day moist curing promotes adequate strength gain and completion of hydration reactions. Additionally, wet curing CSA samples even for 1 day led to lower shrinkage than 7-day cured OPC samples and may result in reduced cracking in concrete pavements. Curing through ponding of samples in deionized water or calcium sulfate-saturated solution resulted in strength reductions of 18% or greater relative to fog-curing.

A deeper generative adversarial network for grooved cement concrete pavement crack detection

Periodic grooved cement concrete pavement crack detection is of great importance for pavement condition monitoring and maintenance. The current state-of-the-art (SOTA) detection solutions highly depend on datasets. However, due to the limited access to crack images, more efficient methods are urgently needed to advance the detection of cracking on grooved cement concrete pavement. This study proposes an improved deeper Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to generate datasets of pavement images with a size of 512 × 512 pixels 2. Poisson bleeding is adopted to create the synthesized grooved cement concrete pavement crack images based on the generated crack images and groove images. The robustness of the proposed improved deeper WGAN-GP model is validated by Faster R-CNN, YOLOv3, and YOLOv4 models trained on original crack images and generated crack images for region-level detection. U-Net and W-segnet are used to achieve pixel-level crack detection to evaluate the effectiveness of proposed model. Results show that the improved deeper WGAN-GP could generate more realistic transverse, longitudinal and oblique crack images. In addition, the Poisson bleeding algorithm contributes to synthesizing grooved cement concrete pavement crack images. Moreover, it is observed that YOLOv3 trained by the augmented dataset could achieve a mean average precision (MAP) of 81.98%, 6% MAP higher than the non-augmented dataset. U-Net and W-segnet benefit from augmented dataset with a better pixel-level segmentation result. Based on the results, it can be concluded that the improved deeper WAGN-GP image generation method can provide a straightforward way to fill the data shortage gap of grooved cement concrete pavement cracks, thus increasing the problem-solving capability of the SOTA crack detection models.

Investigation of the Fatigue Life of Bottom-Up Cracking in Asphalt Concrete Pavements

Traditionally, fatigue cracking in asphalt pavement means fatigue failure, which is the basis for controlling the design thickness of asphalt pavements. In fact, the fatigue failure of asphalt pavements includes three stages: fatigue cracking, crack expansion, and structural failure. Therefore, this paper aims to investigate the fatigue life of the bottom-up cracking of asphalt concrete (AC) pavements considering the different stages of fatigue failure. The dynamic modulus of AC of different grades was experimentally determined. The tensile stresses at the bottom of the AC layer were evaluated by embedding the tested dynamic modulus into a numerical simulation, which can be used to calculate the fatigue cracking life. Then, overlay tests (OTs) at different temperatures were conducted to obtain the fracture parameters A and n from the asphalt mixture. The crack propagation life was calculated via the Paris formula based on the fracture parameters A and n. The analysis results showed that an increase in AC thickness could effectively improve the fatigue crack life of the pavement structure, and the proportion of crack propagation life to fatigue crack life at different temperatures varied significantly. Therefore, when analyzing and calculating the fatigue life of pavement structures, besides the fatigue cracking life, the crack propagation life after cracking should also be considered, which is very important for accurately calculating the entire fatigue life of asphalt pavement structures. This will offer guidance for asphalt pavement thickness design.

Modelling negative thermomechanical effects in reinforced road structures with thermoelastic incompatibility of coating and reinforcement materials

The phenomena of the formation of local defects and cracks in asphalt concrete pavements of roads and bridges are most often observed in climatic zones with large temperature differences during their seasonal and daily changes. To a large extent, this is due to the heterogeneity of the thermomechanical properties of the materials of the coating layers and the base. To prevent these phenomena, reinforcing rods and meshes are introduced into the coating structure. In this work, using the theory of thermoelasticity, it is shown by the method of mathematical modelling that in cases of incompatibility of the thermomechanical characteristics of asphalt concrete materials and reinforcement, additional localized thermal stresses arise in its small vicinity, which, even at moderate temperatures, can reach critical values and lead to local defects and cracks. Since these defects are latent, they cannot always be detected in practice. The presented results of analytic calculation validated these conclusions. They can be used in both road building and composite design.

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

Crack is a common concrete pavement distress that will deteriorate into severe problems without timely repair, which means the automated detection of pavement crack is essential for pavement maintenance. However, automatic crack detection and segmentation remain challenging due to the complex pavement condition. Recent research on pavement crack detection based on deep learning has laid a good foundation for automated crack segmentation, but there can still be improvements. This paper proposes an automatic concrete pavement crack segmentation framework with enhanced graph network branch. First, the nodes of the graph and nodes’ attributions are generated based on the image dividing. The edges of the graph are determined based on Gaussian distribution. Then, the graph from the image is input into the graph branch. The graph feature map of the graph branch output is fused with the image feature map of the encoder and then enters the decoder to recover the image resolution to obtain the crack segmentation result. Finally, the method is tested on a self-built 3D concrete pavement crack dataset. The proposed method achieves the highest F1 and IoU (Intersection over Union) in the comparison experiments. And the graph branch addition improves 0.08 on F1 and 0.06 on IoU compared with U-Net.

Investigation of Effects of Different Environmental Conditions on Balance Time of Bleeding and Evaporation in Plastic Shrinkage of Concrete Pavements

One of the causes of early-age cracking in concrete pavements is plastic shrinkage. This shrinkage occurs after the balance of bleeding and evaporation and the formation of negative capillary pressures at the pavement surface. Different environmental conditions cause the rate of evaporation to change, resulting in a change in time of balance and subsequent cracking. This study, using the standard ASTM C 1579 method, examined the relationship between time of balance of bleeding and evaporation, and cracking area in concrete in 27 different environmental conditions, including a combination of three ambient temperatures, three wind speeds, and three relative humidities with using a continuous video recording system and digital image analysis. The results showed that there was a significant relationship between time of balance and severity of plastic shrinkage cracking. By reducing the time of balance from the maximum value to its minimum value, the cracking area increased by more than four times. It was seen that the combination of environmental conditions has a significant effect on increasing or decreasing the severity of cracking, and the fact that only one factor is critical cannot necessarily create critical conditions for plastic shrinkage cracking. Time of balance can be an essential factor in controlling and reducing plastic shrinkage cracking and assessing the effects of different environmental conditions on the severity of cracking in concrete pavements. It is necessary to control this factor during the construction of concrete pavements in different environmental conditions.

Improvement of a Technology of Use of a Road Milling Machine for Opening Cracks in Asphalt Concrete Pavements

The article presents improved technology of opening temperature origin cracks in asphalt and concrete pavements. The technology is based on the newly developed original design of a road milling machine with an automatically ranging width of an additional set of disc cutters and a diameter of a center plate. It is proposed to use it as part of an automatic system for milling (grooving) cracks of temperature origin in asphalt concrete pavements of highways. Dominant factors influencing the efficiency of road repairs in the process of milling the road pavement having cracks caused by temperature exposure with their broad unpredictable distribution relative to the axis of the main crack have been identified. The article also includes calculations of the total elongation of a curved part of an external surface of a disc-shaped element required for the identification of controllably changing axial overall dimensions of an assembled road milling machine. The calculations, as well as the efficiency and working ability of the proposed road milling machine, have been proved by experimental studies.

Investigation of climate change impacts on early-age cracking of jointed plain concrete pavements in Canada

Canada’s climate is warming at a rate about double the global average, leading to potential negative impacts on public infrastructures such as jointed plain concrete pavement (JPCP). In light of this reality of changing climate, the work contained in this paper is aimed at evaluating JPCP’s early-age behaviour in response to environmental conditions. HIPERPAV® software and the associated models developed by the U.S. Federal Highway Administration (FHWA) were used to identify cracking potential. A sensitivity analysis was performed to assess the effect of different levels of air temperature, mix temperature, base layer temperature, wind speed, and relative humidity. Additionally, projected extreme temperatures predicted by the Canadian Regional Climate Model (CanRCM) were used to determine the relative impact of climate change on cracking risk. The results demonstrated the increased cracking risk under changing climate in several Canadian cities by mid-century and highlighted the importance of developing a pathway forward for climate adaptation.

Continuously Reinforced Concrete Pavement Cracking Patterns and Properties with Internal Curing and Active Cracking

Transverse cracking in continuously reinforced concrete pavement (CRCP) develops over time and may exhibit irregular patterns such as cluster cracks, Y-shaped cracks, and divided cracks. These undesirable cracking patterns can increase the probability of premature spalling and punchouts. Ideally, a uniform transverse cracking pattern with small crack widths leads to favorable long-term performance in CRCP. An experimental field project was constructed with internally cured concrete and active cracking to control crack initiation timing, patterns, and properties. Prewetted fine lightweight aggregate was used for internal curing, and edge notches of 2 in. depth and 2 ft length every 4 ft along the test section were sawcut for active crack control. The three experimental CRCP sections in Illinois were monitored for crack spacing, crack width, and formation of undesirable cracks over a 4-year period. Internally cured concrete significantly reduced the undesirable crack patterns relative to the control section. Internally cured concrete coupled with active crack concrete produced superior crack patterns and properties, that is, uniform crack spacing of 3.6 ft without cluster cracks, 0.2 mm surface crack width, and only a small number of undesirable cracks. Active crack control produced a higher number of transverse cracks near the terminal joint (last 150 ft) compared with the control section. Moisture and temperature management with active crack control during construction of the CRCP provided a desirable crack pattern and properties that should increase the overall service life of a pavement.

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

A brief review of sealants for cement concrete pavement joints and cracks

Pavement, as an important transportation infrastructure, plays an essential role in modern society. Joints and cracks, which are either designed for or created by internal and external means, must be filled in with proper sealant to stop penetration of water into the surface layer and layers beneath; otherwise, they usually lead to severer premature structural failure. In this paper, we briefly reviewed the various types of sealants available for cement concrete pavement, their performance, and their selection and evaluation methods. In particular, we discussed some new developments in sealant research, that is, using shape memory polymers as sealants. We concluded the brief review with some future perspectives.

Prediction of Premature Cracking in Jointed Plain Concrete Pavements

AbstractThis paper presents a mechanistic analysis of the occurrence and extent of premature cracking in jointed plain concrete pavements (JPCPs) by modeling (1) vehicle–pavement interaction, (2) f...