Concrete Surface Crack Research Articles

RC Bridge Concrete Surface Cracks and Bug-Holes Detection Using Smartphone Images Based on Flood-Filling Noise Reduction Algorithm

Noise reduction is a key process in digital image detection technology for concrete cracks and bug-holes. In this study, the threshold range of the flood-filling noise reduction algorithm was investigated experimentally. Surface cracks and bug-holes in RC bridge concrete were detected using mobile terminal images based on the flood-filling noise reduction algorithm. The results showed that the error range was within 10% when threshold range Θ was confined in [60, 80] as the crack width was from 0.1 mm to 2 mm. It is suitable that the threshold range Θ was selected as 70 while the measured crack width range was 0.2 mm to 2 mm. However, by reducing the values of the threshold range Θ to 50, the miscalculation was obviously eliminated. The influences of reducing values of the threshold range on bug-holes of the equivalent diameter and area were not significant. It is suitable that the threshold range Θ was elected on 50 to detect bug-holes in the concrete surface. The threshold range can be selected as a suitable value for the detection of cracks and bug-holes in order to reduce noise.

Experimental study on damage of steel fiber reinforced concrete- and wood-filled GFRP tubes under axial compression

In order to reduce the self-weight of concrete structures and improve its corrosion resistance, it is an effective way to fill GFRP tubes with wood columns instead of part of concrete. However, concrete structures are prone to cracks, which will reduce the bearing capacity of the structure and affect the safety and applicability of the structure. Incorporating the steel fibers (SF) in concrete seems to be an excellent solution to limit crack propagation and improve the ductility of concrete structures. Based on the premise, in this paper, a new type of structure composite material of steel fiber reinforced concrete- and wood-filled GFRP tubes was developed. The mechanical properties, damage evolution process and crack fractal characteristics of steel fiber reinforced concrete- and wood-filled GFRP tubes under axial compression with different SF volume fractions (0 %, 1 %, 1.5 % and 2 %) were studied by acoustic emission (AE) technology. The finite element method (FEM) was established to simulate the test work and expand the analysis. The results showed that the bearing capacity and ductility of the specimens increased first and then decrease with the increase of SF content, and the specimens with 1.5 % volume fraction of SF showed the best mechanical properties. According to the characteristics of AE ringing counts, the damage process of the specimen was described by three marker events, which were concrete cracking, wood cracking and GFRP tube fracture. The incorporation of SF increased the intensity of AE signal and the proportion of shear cracks, and reduced the damage scale. The fractal dimension of concrete surface cracks had a good correlation with the degree of local damage and the complexity of crack propagation. The damage evolution model based on AE cumulative events and stress obeyed the two-parameter Weibull distribution. The incorporation of SF reduced the degree of damage, and the parameter m had a negative correlation with brittleness. In addition, the damage process of the specimens revealed by the FEM matched well with the experimental results. Additionally, the incorporation of SF made the stress distribution of each part of the specimen more uniform and avoided the phenomenon of stress concentration.

Non-uniform distribution model of rust layer around steel bar circumference and its effect on corrosion-induced concrete cracking

This paper presents a critical review of non-uniform distribution models of rust layer around a reinforcing bar’s circumference and a numerical investigation into the effect of non-uniform distribution of rust on (a) crack patterns and (b) development of concrete surface crack width. Different shapes of rust layer simulated by mathematical models were established based on statistical distribution functions which were compared with the transport-electrochemical model and published testing results. In addition, an advanced 2D finite element model was implemented to simulate crack propagation in concrete caused by rebar corrosion. It is found that non-uniform rust distribution shape around the rebar not only affected concrete surface crack width evolution, but also crack length and crack pattern. Transport-electrochemical model and Gaussian distribution-based model could reasonably simulate rust distribution and associated corrosion-induced cracks. In addition, the authors proposed a simplified semi-empirical model that incorporated an analytical chloride transport model and a Gaussian model to simulate time-dependent advancement of non-uniform rust layer. Furthermore, a parametric study of the effect of elastic stiffness of the rust layer on crack patterns and surface crack width development was also performed.

Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.

Recognition and Classification of Concrete Surface Cracks with an Inception Quantum Convolutional Neural Network Algorithm

Recognition and Classification of Concrete Surface Cracks with an Inception Quantum Convolutional Neural Network Algorithm

Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network

To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResNet-18 residual network was developed. This method was implemented by training a model with images to extract the cracks, where the image processing algorithms and deep learning were combined. The results show that the computational accuracy can meet the requirements by utilizing the established image dataset and appropriate model hyperparameters. The trained model had high recognition accuracy when the 256 × 256 resolution images were adopted, and the worst accuracy of crack recognition in the test set was over 90%. The average accuracy in the test set was 91.3% when considering environmental interference generated by processing the images with a brightness adjustment, salt-and-pepper noise, and localized interference. Then, it was demonstrated that the present model possesses good robustness for crack identification in different environments. The average recognition accuracy when dealing with images of a real bridge, which are outside the training dataset, was 99.7%. The residual network model developed in this study has the advantages of low cost, high efficiency, and practicality compared to traditional detection methods. Compared to the existing deep learning methods, the model created in this study requires less computational resources and storage space, and shows a faster training speed and higher accuracy.

An image processing mechanism for aerial inspection robots to detect submillimeter-width concrete cracks in social infrastructures

Inspection robots for early detection of potential risks in severe environments require a high accuracy like human experts. A fine mechanism is crucial for extracting target components from noisy signals. We have proposed a detection system for submillimeter-width cracks in concrete surfaces of social infrastructures, such as bridges, by using morphological component analysis (MCA), for aerial image-inspection robots. Traditional schemes like PCA have relied on linear decomposition for the separation of target signal and noise components. Recent advancement in signal decomposition focuses on the enhancement of linearity in the separation by introducing a set of nonlinear basis functions to represent the raw signal even when multiple factors are mixed in a nonlinear manner. In this sense, MCA is a core technique to be able to isolate target components to represent submillimeter-width cracks from others. We proposed a proper pre- and post-processing operations to attach MCA, which demonstrated a high accuracy yet coarse and fine image components have to be integrated redundantly. In the present study, we successfully found a simpler mechanism to set the single basis function to extract the target by introducing a new thresholding mechanism. It suggests a high potential of MCA for inspection robots for various purposes.

An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and Otsu method

This study introduces a pioneering system for real-time classification and measurement of concrete surface cracks, a crucial aspect of Structural Health Monitoring (SHM). We harness the power of transfer learning (TL) in Convolutional Neural Networks (CNNs), including renowned models such as MobileNetV2, EfficientNetV2, InceptionV3, and ResNet50. Notably, our model excels, particularly with TL MobileNetV2, achieving remarkable results – a 99.87% accuracy rate, 99.74% recall, 100% precision, and an impressive 99.87% F1-score. Incorporating the Otsu method for image segmentation, our system accurately assesses individual crack sizes. To refine measurements, Euclidean distance calculations and a 'pixel per inch' technique, accounting for video resolution, ensure millimeter-level width estimations. Precision is validated through manual experiments using a vernier caliper, specifically the Mitutoyo Absolute Digital Caliper. This tool ensures high accuracy, with an error margin of ±0.2 mm to ±0.3 mm, making it efficient for detailed measurements. Despite these promising outcomes, it is crucial to acknowledge inherent limitations. These include dependence on image quality, challenges in generalization, sensitivity to training data, assumption of linear crack width calculation, resolution dependency, and other factors. These limitations underscore the need for further refinement in our proposed classification model and measurement technique. This research represents a significant advancement in the SHM field, catering to early detection and timely maintenance requirements essential for infrastructure safety and longevity. As the field increasingly prioritizes rapid detection, our model presents a versatile solution that enhances the potential of Structural Health Monitoring.

An Integrated Framework for Image Acquisition, Processing, and Analysis Procedures for Automated Damage Evaluation of Concrete Surfaces.

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82.

Post-Earthquake Serviceability Assessment Using the Convolutional Neural Networks and Deep Neural Networks

There have been studies using image processing techniques to assess damage of structure especially columns without sending individual to onsite. Convolutional neural networks (CNNs) and the entire Deep neural network (DNN) have shown state-of-art results in object detection and image classification tasks. This study proposed cascaded deep learning network for post-earthquake structural serviceability assessment. Major target deficiency components (crack, spalled area, transverse bar, and longitudinal bar) were used to determine the proposed damage states to assess serviceability of structure. Concrete surface cracks are major defect in civil structures. Structural Inspection which is done for the evaluation of rigidity and tensile strength of the building. Crack detection plays a major role in the building inspection, finding the cracks and determining the Structural health in the case of post-earthquake. The research paper is all about finding the serviceability results of the structure.

A multi-scale residual encoding network for concrete crack segmentation

Concrete surface crack detection plays a crucial role in ensuring concrete safety. However, manual crack detection is time-consuming, necessitating the development of an automatic method to streamline the process. Nonetheless, detecting concrete cracks automatically remains challenging due to the heterogeneous strength of cracks and the complex background. To address this issue, we propose a multi-scale residual encoding network for concrete crack segmentation. This network leverages the U-NET basic network structure to merge feature maps from different levels into low-level features, thus enhancing the utilization of predicted feature maps. The primary contribution of this research is the enhancement of the U-NET coding network through the incorporation of a residual structure. This modification improves the coding network’s ability to extract features related to small cracks. Furthermore, an attention mechanism is utilized within the network to enhance the perceptual field information of the crack feature map. The integration of this mechanism enhances the accuracy of crack detection across various scales. Furthermore, we introduce a specially designed loss function tailored to crack datasets to tackle the problem of imbalanced positive and negative samples in concrete crack images caused by data imbalance. This loss function helps improve the prediction accuracy of crack pixels. To demonstrate the superiority and universality of our proposed method, we conducted a comparative evaluation against state-of-the-art edge detection and semantic segmentation methods using a standardized evaluation approach. Experimental results on the SDNET2018 dataset demonstrate the effectiveness of our method, achieving mIOU, F1-score, Precision, and Recall scores of 0.862, 0.941, 0.945, and 0.9394, respectively.

Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction

The crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton is proposed in this paper. The main processing steps include image preprocessing, crack skeleton extraction, and inflection point recognition. In extracting the crack skeleton, the skeleton is initially extracted using morphological operations. After identifying the endpoints based on the neighborhood distribution of the skeleton points, the burrs are tracked until the branching point using the endpoints as the starting point, and the skeleton burrs are judged and removed by setting a threshold with a burr removal rate of 100%. For the identification of inflection points based on chain code calculation, the algorithm is optimized by the concentration of the inflection points and the distance and angle between the inflection points to remove false inflection points. The test data show that the algorithm has good adaptability and the accuracy is higher than 90%. After obtaining the crack skeleton image with real inflection points, the structural features of the skeleton can be calculated, including the distance ratio before and after the inflection points and the angle formed between the inflection points. This lays the foundation for future research to realize the recognition of the same crack at different time points.

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm

Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm

Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection

To solve the problems of high training cost and low time efficiency when detecting concrete surface crack defects, this paper proposes a concrete surface crack detection method based on shallow CNNs. First, an image dataset of concrete surface cracks is constructed and preprocessing operations are performed on the dataset. Then, a shallow CNNs model for concrete surface crack detection is constructed, hyperparameters of the network model are set, spatial attention mechanism is introduced, and the dataset is input to the model for training. Finally, the results are analyzed and evaluated, and compared with the mainstream deep learning models InceptionV3 and Resnet50. Experimental results demonstrate that our proposed method achieves a 99.56% accuracy rate for detecting cracks on concrete surfaces, while also offering higher time efficiency and lower training costs. Given the significantly higher training costs associated with mainstream deep learning models such as InceptionV3 and ResNet50, our proposed method holds promise for future applications in industrial production processes and the intelligent field of construction quality inspection. By effectively, automatically, and accurately identifying cracks on concrete surfaces, it can reduce the risk of catastrophic failures.

Research on the Repair Quality of Crack in Concrete Structures by Stress Waves

The main purpose of this study is to develop non-destructive testing techniques for assessing the repair condition of concrete structures. Concrete is a building material commonly used in engineering, such as dams, bridges, retaining walls, houses, etc.. Concrete structures frequently exist various types of cracks due to inferior environments and excessive loading. There are several crack-repairing techniques used in concrete construction. The most common method is to use epoxy. The proposed research will perform a series of numerical and experimental studies to investigate the feasibility of developing a new technique for quantitative evaluation of the concrete surface crack repairing status degree. The results of the studies will form the experimental study for establishing the experimental procedure and signal analysis criteria for further field application in the nondestructive evaluation of concrete surface crack repairing status.

Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network

Automated quality control of pavement and concrete surfaces is essential for maintaining structural integrity and consistency in the construction and infrastructure industries. This paper presents a novel deep learning model designed for automated quality control of these surfaces during both construction and maintenance phases. The model employs per-pixel segmentation and per-image classification, integrating both local and broader context information. Additionally, we utilize the classification results to improve segmentation during both training and inference stages. We evaluated the proposed model on a publicly available dataset containing more than 7,000 images of pavement and concrete cracks. The model achieved a Dice score of 81% and an intersection-over-union of 71%, surpassing publicly available state-of-the-art methods by at least 6–7 percentage points. An ablation study confirms that leveraging classification information enhances overall segmentation performance. Furthermore, our model is computationally efficient, processing over 30 FPS for 512 × 512 images, making it suitable for real-time applications on medium-resolution images. Code and the corrected dataset ground truths are publicly available: https://github.com/vicoslab/segdec-net-plusplus-conbuildmat2023.git.

Mobile-DenseNet: Detection of building concrete surface cracks using a new fusion technique based on deep learning

Crack detection is very important during the inspection of building structures to determine whether they are safe. Therefore, to ensure the reliability and longevity of buildings, it is necessary to have experts periodically carry out building inspections. Building inspection has traditionally been conducted using human-based visual inspection methods as well as artificial intelligence methods that have shown great success in computer vision in recent years. In this study, 9 different models (Xception, VGG16, ResNet101, InceptionV3, InceptionResNetV2, MobileNetV2, DenseNet169, NASNetMobile, and EfficientNetB6), which have shown significant success in the field of artificial intelligence, are discussed to detect and classify cracks in building structures. In addition, a new fusion model structure called Mobile-DenseNet has been proposed by making block cutting and adding auxiliary layers to the MobileNetV2 and DenseNet169 model structures. With this proposed model structure, cracks in concrete structures were classified. A dataset consisting of concrete surface images was used to detect and classify cracks occurring in concrete structures, and a 99.87 % success rate was achieved with the proposed Mobile-DenseNet model in classifying cracks occurring on the concrete surface. The proposed model outperformed the traditional pretrained model structures in the study in terms of the number of transactions, density, features, complexity, and success accuracy.

Reinforced concrete surface cracks length detection and length estimation by using digital image processing approach

Surface cracks are a common failure that occurs in reinforced concrete structures (RC). With the help of new technologies, access to crack properties should be easier and help the inspector to provide better results. However, most inspectors still prefer manual visual inspection approach, which leads to inconsistent results when investigating this flaw. Moreover, cracks with inconsistent shapes and irregularities are a difficult task for crack extraction, and inspectors overlook the details of cracks. Therefore, in this study, crack detection and thin crack appearance enhancement using various digital image processing algorithms (DSP) were proposed to improve the accuracy of crack length estimation. By using certain DSP on the captured crack images on RC, several algorithms were created and coded in MATLAB via the morphological approach to produce good quality of the original crack images. At the end of this study, the appearance of the thin crack was improved and helped to improve the estimate of the total length of the crack in pixels. The maximum percentage error between the estimated crack length was calculated and compared to the actual length and was 7.10 %. The surface crack detection algorithm and the has the potential as a helpful structural health monitoring (SHM) tool for crack inspection.

Concrete Cover Cracking and Reinforcement Corrosion Behavior in Concrete with New-to-Old Concrete Interfaces.

In reinforced concrete (RC) structures, new-to-old concrete interfaces are widely present due to precast splices, repairs, and construction joints. In this paper, both monolithic and segmental specimens were fabricated with five kinds of water-cement ratios, including ordinary and high-strength concrete. The impressed current-accelerated corrosion test was used, and the degree of reinforcement corrosion was controlled by Faraday's Law. In the accelerated corrosion process, the concrete surface cracking, steel corrosion, and mechanical properties of the corroded steels in the segmental specimens were investigated and compared with monolithic specimens considering the pouring method, concrete strength, and the strength difference between new and old concrete. The prediction of concrete cracking time was also discussed. The results indicated that, for the monolithic specimens, longitudinal cracks could be observed on the ordinary concrete surface, while no cracks were produced on a high-strength concrete surface; only the rust leaked out at the ends. For the segmental specimens, both longitudinal and transverse cracks were produced on an ordinary concrete surface, while only transverse cracks were produced at the high-strength new-to-old concrete interfaces. The steel embedded in the segmental specimens suffered more sectional loss at the new-to-old concrete interfaces. An influence coefficient based on the section loss of the rebar was proposed to evaluate the influence of interfaces on the rust uniformity of rebars. When there were differences in strength between new and old concrete, the influence of the interface on the uniformity of steel bar cross-section loss slightly increased. Based on available theoretical analysis for uniform corrosion, the concrete cracking time of the monolithic specimens was predicted, which was basically consistent with experimental phenomena. However, further research is needed to predict the service life of segmental specimens with new-to-old concrete interfaces.