Concrete Surface Crack Research Articles

Construction of Concrete Surface Crack Recognition Model Based on Digital Image Processing Technology

In this paper, the bridge crack detection method based on digital images is studied. In-depth analysis and evaluation are performed on the image processing algorithms such as image graying, resolution of checkerboard corner pixel rate, filtering denoising, and edge detection, etc. The calculation and software system for bridge crack width based on videos (or images) is implemented, and 15 bridge crack images are used to verify its crack detection accuracy. The results suggest that the proposed crack identification method in this paper can be used for the crack detection of reinforced concrete bridges and class B prestressed concrete bridges properly. When the crack width is greater than 0.3 mm, the calculated crack width value based on images is very close to the measured value.

A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

Huge number of images are acquired and analysed every day for a range of applications in civil infrastructure. One such application is the identification of cracks in concrete surface images, which is a challenge owing to their low contrast and resolution, blurriness, noise and information loss. Existing image enhancement algorithms improve either contrast or resolution to a rather limited extent. This paper proposes a Hybrid Image Enhancement (HIE) algorithm to improve both the contrast and resolution of concrete surface images using the Wavelet transform and Singular Value Decomposition (SVM). The enhanced concrete surface crack images are classified into specific crack types. The classification comprises preprocessing, crack detection, feature extraction and crack classification. The images are initially preprocessed using the Wiener filter to remove blurriness, following which cracks are detected using morphological operations and discontinuities in the segmented crack regions eliminated using the K-Dimensional Tree algorithm. Features are extracted from the segmented regions using statistical and geometric features. The image is classified thereafter into specific crack types using algorithms from three different neural network, kernel and tree based categories. The proposed HIE algorithm is validated using quantitative metrics and the results obtained are compared with those from State-of-the-Art methods and datasets. The results have shown that the HIE algorithm offers significantly improved accuracy of between 6% and 10% in the classification of concrete surface images.

A Real-Time Detection Method for Concrete Surface Cracks Based on Improved YOLOv4

Many structures in civil engineering are symmetrical. Crack detection is a critical task in the monitoring and inspection of civil engineering structures. This study implements a lightweight neural network based on the YOLOv4 algorithm to detect concrete surface cracks. In the extraction of backbone and the design of neck and head, the symmetry concept is adopted. The model modules are improved to reduce the depth and complexity of the overall network structure. Meanwhile, the separable convolution is used to realize spatial convolution, and the SPP and PANet modules are improved to reduce the model parameters. The convolutional layer and batch normalization layer are merged to improve the model inference speed. In addition, using the focal loss function for reference, the loss function of object detection network is improved to balance the proportion of the cracks and the background samples. To comprehensively evaluate the performance of the improved method, 10,000 images (256 × 256 pixels in size) of cracks on concrete surfaces are collected to build the database. The improved YOLOv4 model achieves an mAP of 94.09% with 8.04 M and 0.64 GMacs. The results show that the improved model is satisfactory in mAP, and the model size and calculation amount are greatly reduced. This performs better in terms of real-time detection on concrete surface cracks.

Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.

Investigation of the effects of corrosion on bond strength of steel in concrete using neural network

Corrosion of steel reinforcement due to hostile environments is regarded as one vital structural health concerns in concrete structures. Specifically, the development of corrosion affects the necessary bond strength of rebar in concrete contributing to the loss of resilience and possible structural failures. It is thus essential to understand the effects of corrosion on bond strength so that remedial measures can be done on existing and deteriorating RC structures. Hence, this study investigated through laboratory experiments and Artificial Neural Network (ANN) modeling the effects of corrosion on bond strength. Experimental results showed that at small amounts of corrosion less than 0.27%, the bond strength was observed to increase. At these levels, the amounts of corrosion products were sufficient enough to expand freely through the permeable structure of concrete and occupy the pore spaces. Beyond this level, however, the bond strength of concrete deteriorated significantly. There was an observed average decrease of 1.391 MPa in the bond strength values for every percent increase in the amount of corrosion. The expansive and progressive internal radial stress due to corrosion resulted to the development of internal and surface cracks in concrete. In the parametric investigation of the derived ANN model, the bond strength was also observed to decline continuously with the growth of corrosion derivatives as represented by the relative magnitudes of the ultrasonic pulse velocity (UPV). The prediction results of the model can be utilized as basis for design and select appropriate mitigating measures to prolong the service life of concrete structures.

Nondestructive Approach for Complex-Shaped Cracks in Concrete Structures by Electromagnetic Waves with FDTD Technique

Concrete cracks have no specific shape and do not show linearity. Since the natural occurrences of concrete cracks make simulation identification difficult, rectangular step function and a dynamic geometry are used to define a concrete surface crack in the natural process. A novel interior crack expression is obtained by accepting the area between two curves as a crack filled by air in concrete and modeling this area like a Riemann integral domain. Taking the partition of this integral domain, the most realistic definition of the crack is made. Electromagnetic (EM) waves are utilized for numerical simulation after identifying the defects, cracks, rebars, and geometry of concrete. Three different simulation setups with complex geometries with two different surface cracks and one internal crack are simulated using a finite-difference time-domain (FDTD) method with Gaussian pulse wave excitation. Simulations are obtained using both transverse electric (TEz) waves and transverse magnetic (TMz) waves and the results are compared with each other. Air-dried concrete specimens are molded following simulation setups with surface cracks and measurements are made nondestructively with a Vivaldi antenna array in the frequency range of 0.4–4.0 GHz. The reflection and transmission coefficients are validated by comparing the data obtained using the measurement with the results obtained from numerical simulation.

A deep learning-based vision enhancement method for UAV assisted visual inspection of concrete cracks

Implementing unmanned aerial vehicles (UAVs) on concrete surface-crack inspection leads to a promising visual crack detection approach. One of the challenges for automated field visual cracking inspection is image degradation caused by the rain or fog and motion blur during data acquisition. The present study combines two deep neural networks to address the image degradation problem. By using the Variance of Laplacian algorithm for quantifying image clarity, the proposed deep neural networks can remarkably enhance the sharpness of the degraded images. After vision enhancement process, Mask Region Convolutional Neutral Network (Mask R-CNN) was developed to perform automated crack identification and segmentation. Results show a 8~13% enhancement in prediction accuracy compared to the degraded images, indicating that the proposed deep learning-based vision enhancement method can effectivey identify and segment concrete surface cracks from photos captured by UAVs.

Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques

This study proposed a semi-automated system for crack detection and quantification, based on the combination of a trained convolutional neural network (CNN) and a developed application. Specifically, we tested four commonly used CNNs and determined GoogLeNet for this study. Then, the transfer learning and fully training of GoogLeNet were further tested on our testing dataset and a public dataset. The results show that the transfer learning GoogLeNet has relatively balanced performances on these two datasets, with accuracy of 96.69% and 88.39%, respectively. A new sliding window technique (neighborhood scanning) was proposed and shown almost equivalent performance to the previous dual scanning method. A method for calculating crack width was presented. The average relative error of this method is 14.58% (0.05 mm), i.e., much smaller than the 36.37% (i.e., 0.14 mm) of the previous method. An application was then developed to integrate the proposed methods and other techniques such as edge detectors, boundary tracking, and threshold segmentation to segment, quantify, and analyze cracks. Verifications on 23 untrained raw images (eleven with 10240 × 2048 pixels, twelve with 2592 × 4608 pixels) show that: (1) the developed system and a previous pixel-level segmentation system require an average of 9.48 s and 10.35 s; (2) these two systems show an 80.40% and a 78.64% average Intersection over Union (IoU). Therefore, the proposed system is a cost-effective solution for detecting and analyzing cracks on concrete surfaces considering its practical performance and time cost. Practically, the proposed system could be used to analyze the images collected from onsite inspection or from experiment.

A method of lining seam elimination with angle adaptation and rectangular mark for road tunnel concrete lining images

Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to the similarity of brightness and linearity between surface cracks and lining cracks, the existing crack detection algorithms can not extract cracks accurately and quickly. An algorithm of lining seam crack elimination with rectangular mark is proposed here. First, the line segments in the image are detected by the Line Segment Detector algorithm based on the coarse percolation detection of the crack. Second, the distribution directions are calculated, and cracks from the lining seams are distinguished by the adaptive threshold judgment method. Third, by using the distribution characteristics of pixels, the line segments are extended to form rectangular marks perpendicular to the direction of lining seams. Finally, the marking information is used to remove the lining joints and obtain the real surface cracks of tunnel lining. Experimental results show that the algorithm can quickly and effectively remove any shape distribution of lining seam. The algorithm fills in the blank of concrete tunnel lining surface crack detection technology.

Characteristics of Soybean Urease Mineralized Calcium Carbonate and Repair of Concrete Surface Damage

The C60 concrete blocks with surface crack damage under high temperature environment were soaked by adding appropriate amount of soybean urease into the CO(NH2)2-CaCl2 solution, the soybean urease mineralized calcium carbonate were characterized, and the effect of repairing concrete surface crack damage were evaluated by the surface sedimentation of C60 concrete blocks in the study. The experimental results showed that the activity of soybean powder was statistically significant, and its productivity of urease was comparable with that of urease-producing bacteria. After immersion in a soybean solution, a layer of complete and continuous white sediment covered the concrete surface. The cracks on the concrete surface were completely shielded, and the rising temperature on infrared thermal image of the concrete after repair was lower than before. Besides, through analysis by SEM, EDS, and XRD, the products formed after repair were found to be calcite-type CaCO3 with high purity, and the crystals exhibited different morphological features. The above results indicate that soybean urease can regulate and induce the formation of calcium carbonate, and the precipitate is innocuous and harmless, suitable for a new type of concrete crack repair material.

Estimation of Internal Corrosion Degree from Observed Surface Cracking of Concrete Using Meso-scale Simulation

Estimation of Internal Corrosion Degree from Observed Surface Cracking of Concrete Using Meso-scale Simulation

Concrete Surface Crack Recognition in Complex Scenario Based on Deep Learning

A concrete crack detection method suitable for various scene conditions is proposed on the basis of image recognition to complete the classification and the identification of the cracks of the collected crack images in the bridge health monitoring work conveniently and reliably. Moreover, this method can improve the crack recognition effect, which is greatly affected by the selection of the initial clustering center of the extraction algorithm, and has high environmental dependence on the image background. Convolutional neural networks (CNNs) is a representative deep learning algorithm that can characterize learning and classify input information according to its own hierarchical structure. After collecting image data on the spot by relying on the Baoxie River Bridge inspection project, Gaoxin 4th Road, Donghu High-tech Zone, Wuhan City based on the CNN, an image classification model suitable for concrete crack image classification is established. This model realizes the collection of the inspection project image classification of concrete structures in complex scenes while considering that the local cluster density and Euclidean distance of the clustering center in the traditional K-means algorithm are both large. The traditional K-means algorithm is improved by combining the use of statistical principles and morphological methods. Finally, the improved K-means algorithm completes the crack skeleton segmentation extraction and crack width calculation of crack images in complex scenes. The effectiveness of the proposed method under concrete surface peelings, stains, mosses, or other noise conditions was verified according to the successful identification of 600 crack on-site images photographed from a bridge surface. Results also show that the efficiency of the proposed crack detection method is higher than that of traditional methods. The proposed approach can also provide a reference for the in depth research on crack identification on the surface of concrete structures in complex background in the future.

An Improved Nondestructive Semantic Segmentation Method for Concrete Dam Surface Crack Images with High Resolution

To nondestructive semantic segment the crack pixels in the image with high resolution, previous methods often use sliding window and the crack patches to train the FCNs, and then use the trained FCNs for crack recognition. However, the FCNs will produce a higher proportion of false crack predictions with messy distributions in the high-resolution image. A CNN-to-FCN method is proposed to solve this problem. The CNN is trained by all the patches for large-scale crack and background recognition, and the screened crack predictions are then segmented by the FCN. A real-world concrete dam surface crack image database is firstly established to verify the improved method. The results indicated that (1) the improved method can extremely avoid the higher proportion of false crack predictions and their messy distributions in the high-resolution image through the full utilization of background patches and large-scale background recognition; (2) the ResNetv2 backbone and DeepLabv3 architecture recommended by the improved method can be further modified by reducing the bottleneck channels and adding a DUC module to achieve better performance; (3) the improved method can also reduce the prediction time when the image has low proportion of crack patches, which becomes more practicable for the engineering applications.

Concrete Cracks Detection Using Convolutional NeuralNetwork Based on Transfer Learning

Crack plays a critical role in the field of evaluating the quality of concrete structures, which affects the safety, applicability, and durability of the structure. Due to its excellent performance in image processing, the convolutional neural network is becoming the mainstream choice to replace manual crack detection. In this paper, we improve the EfficientNetB0 to realize the detection of concrete surface cracks using the transfer learning method. The model is designed by neural architecture search technology. The weights are pretrained on the ImageNet. Supervised learning uses Adam optimizer to update network parameters. In the testing process, crack images from different locations were used to further test the generalization capability of the model. By comparing the detection results with the MobileNetV2, DenseNet201, and InceptionV3 models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (0.9911) and has good generalization capability. Our model is an efficient detection model, which provides a new option for crack detection in areas with limited computing resources.

Estimation of internal corrosion degree from observed surface cracking of concrete using mesoscale simulation with Model Predictive Control

AbstractCorrosion of the steel reinforcing bars in concrete structures is one of the major maintenance problems. Corrosion results in expansive pressure on the surrounding concrete, which causes internal damage that may become visible as surface cracking. Such damage may degrade structural safety and serviceability. Effective maintenance requires the evaluation of residual performance based on estimates of spatially nonuniform levels of corrosion, which are typically obtained through surface measurements only. In this study, the authors have developed a simulation system for estimating the levels of internal corrosion along the reinforcing bar length from surface crack information. This innovative system is produced by integrating the technique of Model Predictive Control (MPC) with Rigid‐Body‐Spring Models (RBSM) of corrosion‐induced cracking at the concrete mesoscale. In this study, MPC controls the simulated surface cracks such that they match the observed cracks by optimizing the internal expansions of springs representing the steel‐concrete interface within the RBSM. The applicability of the system is verified using both synthetic crack width data and crack data collected from in‐house laboratory testing. In the laboratory testing, corrosion levels were quantified by 3D scanning of the extracted reinforcing bars. The simulation results agree with the corrosion measurements, demonstrating the potential of the MPC‐RBSM system for predicting the corrosion distribution along reinforcing bars using surface crack data.

Intelligent detection of building cracks based on deep learning

In order to solve the damage caused by the concrete structure, which leads to the reduction of the life of infrastructure, endangers the safety of pedestrians, and has a serious impact on the social economy, building crack detection model of FCN (Fully Convolutional Networks), R-CNN (Regions with CNN feature) and RFCN (Richer Fully Convolutional Networks) has been proposed based on the convolutional neural network model to amplify and extract the features of the data and previous studies. Through the training of building surface data such as roads, bridges, houses and dams, the model is analyzed in terms of morphological and geometric indexes. Finally, the model of crack picture detection and segmentation based on deep learning is used for picture performance detection and comprehensive evaluation. The results show that: in the aspect of building gap detection, the RFCN model has the best processing effect, the gap recognition degree is higher, and the detail processing is better. In the aspect of model evaluation index, the correct rate of RFCN model is increased by 10%, the accuracy rate is increased by 12%, the recall rate is increased by 8%, the loss rate is increased by 3%, and the overall stability is higher. In the aspect of comprehensive performance, the picture processing performance is better than the FCN model by 7% and better than the R-CNN model by 15%, and the memory share is 80%. The fusion model based on deep learning and picture processing has been improved in many aspects, which can provide strong theoretical support and practical value for the detection and research of concrete surface cracks such as bridges, dams, highways and houses.

Analysis of steel corrosion‐induced surface damage evolution of magnesium oxychloride cement concrete through gray‐level co‐occurrence matrices

AbstractIn order to get the characteristics of crack formation on a concrete surface, a coupled method of industrial cameras and gray‐level co‐occurrence matrices (GLCM) is used to analyze the formation of magnesium oxychloride cement concrete surface cracks. This method requires fewer samples than the previous edge detection method, typing analysis, and artificial neural network method and can provide more damage parameter information without being limited to the geometric information of local features. According to the GLCM analysis of the surface crack formation of concrete under the effect of a rust expansion force, it is concluded that the larger the concrete protective layer, the longer the energization time required. When the concrete surface crack reaches 0.2 mm, the test pieces with protective layers of 14, 24, 34, and 44 mm need to be energized for 270, 465, 673, and 792 hr, respectively. With the increase of cracks on the concrete surface, the contrast keeps increasing, while the correlation, energy, and homogeneity gradually decrease. In color heat maps, the number of matrix peaks gradually increases with the formation of cracks, and the bandwidth of the horizontal projection of matrix peaks gradually decreases, and there is a tendency to extend along the main diagonal of the matrix. The selection of the size of the region of interest region will have a certain impact on the size of the four eigenvalues in GLCM, but it will not change its development law.

A vision-based active learning convolutional neural network model for concrete surface crack detection

Concrete surface crack detection based on computer vision, specifically via a convolutional neural network, has drawn increasing attention for replacing manual visual inspection of bridges and buildings. This article proposes a new framework for this task and a sampling and training method based on active learning to treat class imbalances. In particular, the new framework includes a clear definition of two categories of samples, a relevant sliding window technique, data augmentation and annotation methods. The advantages of this framework are that data integrity can be ensured and a very large amount of annotation work can be saved. Training datasets generated with the proposed sampling and training method not only are representative of the original dataset but also highlight samples that are highly complex, yet informative. Based on the proposed framework and sampling and training strategy, AlexNet is re-tuned, validated, tested and compared with an existing network. The investigation revealed outstanding performances of the proposed framework in terms of the detection accuracy, precision and F1 measure due to its nonlinear learning ability, training dataset integrity and active learning strategy.

Automatic crack distress classification from concrete surface images using a novel deep-width network architecture

The condition monitoring of concrete surface plays a significant role in civil infrastructure management system. Crack is the main threat to concrete surface of buildings, bridges, roads and pavements. This issue has been researched for several decades, however, it is still a challenge to classify crack since there are many inferior factors, e.g., intense inhomogeneity, structure complexity and background noise of concrete surface. In this paper, a novel deep-width network (DWN) architecture is used for binary and multi-label concrete surface crack classification without handcraft feature extraction. It intelligently learns cracking structures from input raw images by linear and nonlinear mapping process, flexible dynamically updates new weights and efficiently constructs the network by adding new incremental samples. The presented crack distress classification method is tested on two concrete surface crack image datasets and compared with many popular classification methods like sparse autoencoder (SAE), convolution neural network (CNN), and broad learn system (BLS). Experimental results demonstrate that it obviously outperforms those methods both in accuracy and efficiency.

Effect of Concrete Surface Crack Filling Conditions on Dominant Frequency of Surface Rayleigh Wave

Elastic Surface Rayleigh Wave (R-wave) based non-destructive test (NDT) techniques have been studied broadly for concrete surface crack assessment in the past few decades. However, the effect of environment factors on the accuracy of R-wave NDT results are often being neglected. Sand and dust are ones of them that easily accumulate within the surface cracks of concrete structures, which potentially affect the characteristics of R-waves propagation when it travels through the concrete medium that containing cracks. This study is aimed to investigate the effect of concrete surface crack filling conditions to the dominant frequency of R-wave. Numerical simulations were conducted to study the changes of R-wave waveform when it propagated through a cracked concrete model with two various filling conditions, namely fine sand and charcoal powder. Fast Fourier Transform (FFT) for post-signal-processing (PSP) of raw R-wave signals to determine its dominant frequency. The results of numerical simulations were then justified by experimental measurements. It was found that the dominant frequency of R-wave is independent to the volume and the type of filling materials, but sensitive to the surface discontinuity of the concrete.