Crack Detection Research Articles

Dual-Modal Fusion PRI-SWT Model for Eddy Current Detection of Cracks, Delamination, and Impact Damage in Carbon Fiber-Reinforced Plastic Materials

Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a Parallel Real–Imaginary/Swin Transformer (PRI-SWT) model. Built using the Transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision Transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors—including the real–imaginary fusion layer, the number of layers, the window shift size, and the model’s scale—on the classification performance of the PRI-SWT model was systematically evaluated.

Detection of Cracks in Cultural Heritage Buildings Using UAV Photogrammetry-based Digital Twin

Detection of Cracks in Cultural Heritage Buildings Using UAV Photogrammetry-based Digital Twin

Research on the generation and evaluation of bridge defect datasets for underwater environments utilizing CycleGAN networks

The surface cracks on the underwater structures critically damages the overall reliability of the structures and reduces their strength. It is significant to monitor these cracks in timely manner. Recently, deep learning algorithms have been used for large scale data study and predictions. However, deep supervised learning algorithms need to get training on large scale data set which is time consuming and difficult to apply on the underwater structures. Therefore, it is highly needed to address these issues. Current research proposes an improved cycle-constraint generative adversarial algorithm for the timely detection of surface cracks in underwater structures. It utilizes an enhanced cycle-consistent generative adversarial network (CycleGAN). The proposed algorithm uses image processing techniques including DeblurGAN and Dark channel prior methods to get quality of dataset from underwater structures. The proposed Algorithm introduces a novel cross-domain VGG-cosine similarity assessment to precisely evaluate the performance of proposed algorithm to retain crack information etc. Moreover, performance of proposed algorithm is evaluated through both qualitative and quantitative methods. The quantitative results are directly obtained from the visual results are presented which are generated by the proposed Algorithm. Whereas, the performance of proposed algorithm based on quantitative results is obtained from metrics including PSNR, SSIM, and FID. Experimental results indicates that the proposed algorithm outperforms the original CycleGAN. End results indicate that the proposed algorithm decreased the value of FID by 20 % and increased the values of PSNR and SSIM by 2.37 % and 3.33 % respectively. Quantitative and qualitative results of the proposed algorithm give significant advantages during creating of surface crack images.

Bicrack: a bilateral network for real-time crack detection

Bicrack: a bilateral network for real-time crack detection

Local–Global Feature Adaptive Fusion Network for Building Crack Detection

Cracks represent one of the most common types of damage in building structures and it is crucial to detect cracks in a timely manner to maintain the safety of the buildings. In general, tiny cracks require focusing on local detail information while complex long cracks and cracks similar to the background require more global features for detection. Therefore, it is necessary for crack detection to effectively integrate local and global information. Focusing on this, a local–global feature adaptive fusion network (LGFAF-Net) is proposed. Specifically, we introduce the VMamba encoder as the global feature extraction branch to capture global long-range dependencies. To enhance the ability of the network to acquire detailed information, the residual network is added as another local feature extraction branch, forming a dual-encoding network to enhance the performance of crack detection. In addition, a multi-feature adaptive fusion (MFAF) module is proposed to integrate local and global features from different branches and facilitate representative feature learning. Furthermore, we propose a building exterior wall crack dataset (BEWC) captured by unmanned aerial vehicles (UAVs) to evaluate the performance of the proposed method used to identify wall cracks. Other widely used public crack datasets are also utilized to verify the generalization of the method. Extensive experiments performed on three crack datasets demonstrate the effectiveness and superiority of the proposed method.

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.

Testing the strength of thin feather-edge monolithic multilayered zirconia crowns: A pilot study with a novel acoustic test

Intoduction: Limited research has explored the mechanical characteristics of recently introduced multilayered monolithic zirconia (MZC) crowns with feather-edge margins design. Moreover, most of the conventional techniques for evaluating the mechanical behavior of brittle dental ceramics rely on destructive tests, making it impossible to precisely evaluate real strength values due to premature crack initiation detection failure. Objectives: The main objective of the study was to assess the load capacity of translucent multilayered monolithic zirconia crowns with feather-edge margin thicknesses of 0.4 mm and 0.5 mm. Additionally, the research introduced a novel non-destructive acoustic emission testing (AET) technique in the laboratory to identify early cracks in dental ceramics. Methods: Forty multilayered zirconia crowns produced using computer-aided design and computer-aided manufacturing (CAD/CAM) technology were evenly divided into two groups, each with distinct feather-edge margin thicknesses: 0.5 mm (group 1) and 0.4 mm (group 2). These crowns were securely cemented onto customized polymethyl methacrylate (PMMA) dies using a universal restorative glass ionomer. Subsequently, the crowns underwent a compressive axial loading test, controlled by an innovative AET crack detection system rooted in the principles of non-destructive laboratory testing. Fractographic analysis was conducted to determine the location of crack or eventual fracture. Results: The proportion of MZCs with a crack was statistically higher than the proportion with a fracture in the whole sample (p<0.001). In addition, the results showed that the crowns in group 1 exhibited higher loading values than those in group 2. The mean loading capacity in group was 911.2 N ± 614.7 N. Two locations of crack and fracture were registered: occlusal and marginal. No statistical association was observed between the two groups for the location of cracks or fractures. Conclusions: The feather-edge crowns with a 0.5 mm margin thickness had a higher loading capacity than those with a 0.4 mm. However, the latter supports loads that exceed occlusal force in the oral cavity suggesting that 0.4 mm feathered edge MZCs may be a valid and less invasive alternative to thicker margins. The novel load-to-fracture AET setup proved effective in detecting early crack initiation, suggesting it as a promising method for assessing the mechanical properties of brittle materials prior to failure, potentially impacting the field of dentistry. However more advanced research is needed to enhance the accuracy of the proposed technique. Clinical implications: The examined MZC demonstrates the ability to withstand occlusal forces in a patient’s mouth, offering clinical practitioners the option of embracing minimally invasive dentistry through the use of 0.4 mm feather-edge MZC in their treatment options. Furthermore, the positive results from the innovative acoustic emission testing technique, facilitating early crack detection, indicate a promising future for studying the behavior of brittle materials in dental laboratory testing.

Automated surface crack detection with inductive thermography

In recent years, inductive thermography has become established as a non-destructive inspection technique for detecting surface cracks in metallic parts. A short inductive heating pulse (0.1 – 1 s) is applied to the sample and an infrared camera records the surface temperature during and after the heating pulse. Since a surface crack obstructs both the eddy current flow and the heat flow, the defect becomes well visible in the infrared images. The technique has the advantage of being contact-free, fast and it can be fully automated. Additionally, it also provides information about the crack depth, because the deeper the crack, the greater the obstacle to the eddy current and to the heat flow. In order to detect the cracks in the infrared images of castings and forgings with complex geometry, a comparison method has been developed. The images of the inspected parts are compared with images of reference parts, and in this way artefacts caused by edges and corners of the parts are filtered out and not marked as defects. This greatly increases the robustness of the fully automated inspection, which can test parts 24 hours a day, and 7 days a week, without any human interaction, and the results of each part are well documented and stored.

An iterative up-sampling convolutional neural network for glass curtain crack detection using unmanned aerial vehicles

It is inefficient, subjective, and potentially life-threatening for both inspectors and pedestrians to manually inspect glass curtain cracks of high-rise buildings. Due to the development of image instance segmentation techniques for deep learning and the operation of compact unmanned aerial vehicles (UAVs), automatic, accurate, and safe glass curtain crack inspection can be achieved. A novel approach proposes to segment glass curtain cracks efficiently and accurately, combining a convolutional neural network (CNN), the region of interest alignment (RoIAlign), and an iterative up-sampling method. The approach proposed achieves excellent performance with an accuracy=99.89%, precision=91.1%, recall=72.1%, F1-score=79.7%, and mIoU=78.7%. Comparative studies have been conducted to examine the performance of the proposed approach relative to Solo, Swin, Mask RCNN, and ConvNext V2. The results show that the proposed network outperforms the networks used for comparison and that it can more accurately detect and segment a variety of crack types in complex reflective environments of glass curtains.

Nonlinear vibration characteristics and damage detection method of blade with breathing fatigue crack

In aero-engine, blades operate under harsh conditions for extended periods, which makes them highly susceptible to fatigue cracks. The localized and nonlinear structural changes caused by blade crack lead to complex vibration characteristics that are not well understood, posing challenges to the identification of these cracks. This paper proposes an approach for identifying breathing fatigue crack in compressor blade, leveraging the nonlinear features induced by the cracks. Crucial sensitive characteristics are extracted and a crack indicator for accurate crack detection is defined. Initially addressing the typical morphology of fatigue breathing cracks, a dynamic model of blade with cracks is developed to analyze their impact on the natural frequencies. Nonlinear contact forces are introduced to characterize the "breathing" effect of the fatigue crack, allowing for the determination of the harmonic distribution patterns in the blade's vibration response under various excitation conditions. A finite element model is then established considering the crack morphology, by adopting the contact element. Based on that, the nonlinear response characteristics associated with the blade's primary, sub-harmonic, and super-harmonic resonances are explored. A crack-sensitive parameter is defined and extracted from the nonlinear responses, under different crack propagation stages. At last, effectiveness and reliability of the defined parameter for identifying cracks is validated through a vibration fatigue test.

Quantifying sandstone crack extension and expansion via deep learning methods

This study investigates the damage evolution of sandstone under loading stresses, which can lead to irreversible plastic deformation and reduced project stability service life. To achieve this objective, we identify and quantify the emergence and expansion process of sandstone cracks using deep learning methods. Specifically, our approach employs a crack segmentation model enhanced by an attention mechanism, achieving a remarkable effective crack detection MIoU of 93.38. Through the application of connectivity domain analysis, skeleton extraction, and orthogonal projection techniques, we extract crucial crack parameters, which are subsequently formulated into equations to determine their actual dimensions. These evolving parameter variations serve as the foundation for predicting the sandstone's entire damage progression, encompassing both microscopic crack growth and eventual catastrophic failure preceding macroscopic manifestation of damage. Validation of our method through uniaxial compression tests on sandstone specimens confirm its efficacy and practical applicability. In conclusion, this study presents a novel framework for quantifying sandstone crack evolution, providing invaluable insights into the intricate mechanisms governing damage fracture progression in rocks.

Detection of Crack Location and Crack Depth in Leaf Spring

The objective of this work is to find the crack location and crack depth in leaf spring of an army jeep. The spring is subjected to heavy jerks and vibration during the military operation. Therefore it is necessary to find the crack in a leaf spring. Crack in a leaf spring introduces local flexibility that would affect the vibration response of the leaf spring. Main problem is to detect existence of a crack together with its location and depth in the leaf spring .We used a natural frequency a basic criteria for crack detection. Analytical calculations are done to find out natural frequency of leaf spring by using Euler's beam theory. Ansys 16.0 is used to find out natural frequency along with experimentation on FFT analyzer. Now, We propose a method based on some set of fuzzy rules obtained from the information supplemented by Numerical Analysis. Fuzzy controller use comprises of three input variables (first, second and third natural frequency) and two output variables (relative crack length and relative crack depth) are generated with Triangular MF.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

Lightweight concrete crack detection based on spiking neural networks

PurposeMost existing methods for concrete crack detection are based on deep learning techniques such as convolutional neural networks. However, these models, due to their large memory footprint, high power consumption and insufficient feature extraction capabilities, face challenges in mobile applications. To address these issues, this paper proposes a lightweight spiking neural network detection model.Design/methodology/approachThis model achieves fast and accurate crack detection. Firstly, the Gabor-Spiking (GS) module preprocesses input images, extracting texture features and edge features of crack images through Gabor filter convolution modules and spiking convolution modules, respectively. Next, the multiscale residual (MR) module is designed, composed of convolutional layers and residual modules of various scales, to process the fused features and perform crack detection.FindingsExperimental results demonstrate that the model’s size can be reduced to 4.6 MB, achieving accuracy improvements to 87.3 and 96.4% on the SDNET and OCD datasets, respectively.Originality/valueThis paper proposes a lightweight spiking neural network detection model based on the GS module for edge texture feature fusion and the MR module for crack detection.

Detection of crack on concrete surfaces using image processing techniques

Cracks on the concrete surface are one of the earliest indications of structural disintegration under maintenance; further the continual exposure can cause severe harm to the environment. The manual method of crack detection is time-consuming. Since the manual approach fully depends on the specialist’s data and experience, it lacks perspicacity within quantitative analysis. This study establishes a MATLAB® programme on image process techniques for automatic crack recognition and analyses. An image pre-processing theme combines multiple adaptive filtering and contrast enhancement based on the image that may improve the removal of background noise. Then, for removing isolated noise spots, it tends to develop a local adaptive threshold segmentation formula combined with an image morphological operator. The goal of this Gray intensity adjustment methodology is to improve the accuracy of the crack detection results. Furthermore, the results show that the target crack is identified on the concrete surface, additionally classified as its crack type and its properties are extracted.

Pavement crack detection based on depth-supervision FRRN model

Timely detection of early pavement distress is crucial for effective pavement maintenance. A promising approach involves the segmenting the road surface cracks area from its images. However, the previously employed full-resolution residual network (FRRN), when employing a large number of full-resolution residual units (FRRUs), is susceptible to challenges such as exploding and vanishing gradients. Addressing these issues, this paper has introduced a depth supervision mechanism into FRRN, leading to the development of the Depth Supervision FRRN (DSFRRN) model, designed specifically for accurate segmentation of cracks in road images. This model enhances nonlinear expressiveness while retaining essential features, achieving high precision in pavement crack segmentation. Through experimental validation using a dataset comprising 1565 images of damaged pavements, the result demonstrate that DSFRRN outperforms the original FRRN in terms of both F1 score and Jaccard index, accurately delineating cracks. Consequently, the automated pavement damage detection method presented herein facilitates cost-effective and efficient pavement inspection, contributing to the safeguarding of roadway surfaces and transportation infrastructure.

Survey of automated crack detection methods for asphalt and concrete structures

Survey of automated crack detection methods for asphalt and concrete structures

Automated Surface Crack Identification of Reinforced Concrete Members Using an Improved YOLOv4-Tiny-Based Crack Detection Model

In recent times, the deployment of advanced structural health monitoring techniques has increased due to the aging infrastructural elements. This paper employed an enhanced You Only Look Once (YOLO) v4-tiny algorithm, based on the Crack Detection Model (CDM), to accurately identify and classify crack types in reinforced concrete (RC) members. YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. Despite its smaller size, it maintains competitive accuracy, making it ideal for applications requiring high-speed processing on resource-limited devices. First, an extensive experimental program was conducted by testing full-scale RC members under different shear span (a) to depth ratios to achieve flexural and shear dominant failure modes. The digital images captured from the failure of RC beams were analyzed using the CDM of the YOLOv4-tiny algorithm. Results reveal the accurate identification of cracks formed along the depth of the beam at different stages of loading. Moreover, the confidence score attained for all the test samples was more than 95%, which indicates the accuracy of the developed model in capturing the types of cracks in the RC beam. The outcomes of the proposed work encourage the use of a developed CDM algorithm in real-time crack detection analysis of critical infrastructural elements.

Enhanced Fatigue Crack Detection in Complex Structure with Large Cutout Using Nonlinear Lamb Wave

The large cutout structure is a key component in the bottom skin of an airplane wing, and is susceptible to developing fatigue cracks under service loads. Early fatigue crack detection is crucial to ensure structural safety and reduce maintenance costs. Nonlinear Lamb wave techniques show significant potential in microcrack monitoring. However, nonlinear components are often relatively weak. In addition, a large cutout structure introduces complex boundary conditions for Lamb wave propagation, making nonlinear Lamb wave monitoring more challenging. This article proposes an integrated data processing method, combining phase inversion with continuous wavelet transform (CWT) to enhance crack detection in complex structures, with phase-velocity desynchronization adopted to suppress the material nonlinearity. Experiments on a large cutout aluminum alloy plate with thickness variations were conducted to validate the proposed method, and the results demonstrated its effectiveness in detecting fatigue cracks. Furthermore, this study found that nonlinear components are more effective than linear components in monitoring closed cracks.

Integrated Machine Learning and Region Growing Algorithms for Enhanced Concrete Crack Detection: A Novel Approach

In the field of construction engineering, the cracking of concrete structures is a common engineering problem, which has a great impact on the overall stability and service life of the engineered structure. During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. However, due to the changeable environment of the project site and different image specifications, using a single algorithm makes it difficult to balance high efficiency and high accuracy. In this study, we designed a combined recognition method including the region growth algorithm and machine learning regression that can achieve a tradeoff between accuracy and efficiency. Firstly, the regression method learns the image features of the dataset and the specific region growth threshold, and the regression function is trained by using the open-source dataset to determine the region growth threshold using the characteristics of the images included in the tests. The region growth algorithm is used to expand the threshold from the seed points of the image to obtain the crack recognition results. The results show that this method improves the accuracy of SSIM by 7% compared with the traditional region growth algorithm, and does not significantly increase the computational cost, with an increase of 0.78 s per photo process. Compared with the deep learning method, the recognition accuracy of SSIM is decreased by 5.96%, but it takes less resources and has high efficiency.