Crack Quantification Research Articles

Binocular Video-Based Automatic Pixel-Level Crack Detection and Quantification Using Deep Convolutional Neural Networks for Concrete Structures

Crack detection and quantification play crucial roles in assessing the condition of concrete structures. Herein, a novel real-time crack detection and quantification method that leverages binocular vision and a lightweight deep learning model is proposed. In this methodology, the proposed method based on the following four modules is adopted: a lightweight classification algorithm, a high-precision segmentation algorithm, a semi-global block matching algorithm (SGBM), and a crack quantification technique. Based on the crack segmentation results, a framework is developed for quantitative analysis of the major geometric parameters, including crack length, crack width, and crack angle of orientation at the pixel level. Results indicate that, by incorporating channel attention and spatial attention mechanisms in the MBConv module, the detection accuracy of the improved EfficientNetV2 increased by 1.6% compared with the original EfficientNetV2. Results indicate that using the proposed quantification method can achieve low quantification errors of 2%, 4.5%, and 4% for the crack length, width, and angle of orientation, respectively. The proposed method can contribute to crack detection and quantification in practical use by being deployed on smart devices.

High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+

High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+

Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes

At present, researchers in the field of pipeline inspection focus on pipe wall defects while neglecting pipeline defects in special situations such as welds. This poses a threat to the safe operation of projects. In this paper, a multi-node fusion and modal projection algorithm of steel pipes based on guided wave technology is proposed. Through an ANSYS numerical simulation, research is conducted to achieve the identification, localization, and quantification of axial cracks on the surface of straight pipelines and internal cracks in circumferential welds. The propagation characteristics and vibration law of ultrasonic guided waves are theoretically solved by the semi-analytical finite element method in the pipeline. The model section is discretized in one-dimensional polar coordinates to obtain the dispersion curve of the steel pipe. The T(0,1) mode, which is modulated by the Hanning window, is selected to simulate the axial crack of the pipeline and the L(0,2) mode to simulate the crack in the weld, and the correctness of the dispersion curve is verified. The results show that the T(0,1) and L(0,2) modes are successfully excited, and they are sensitive to axial and circumferential cracks. The time–frequency diagram of wavelet transform and the time domain diagram of the crack signal of Hilbert transform are used to identify the echo signal. The first wave packet peak point and group velocity are used to locate the crack. The pure signal of the crack is extracted from the simulation data, and the variation law between the reflection coefficient and the circumferential and radial dimensions of the defect is calculated to evaluate the size of the defect. This provides a new and feasible method for steel pipe defect detection.

A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification

Abstract As a promising structural health monitoring technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1 mm to 1.6 mm, marking a significant improvement.

Automated quantification of crack length and width in asphalt pavements

AbstractRapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1‐fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices.

Deep learning-based method for detection and feature quantification of microscopic cracks on the surface of concrete dams

Efficient detection of surface cracks in concrete dams is crucial for maintaining hydraulic engineering and infrastructure. Accurately identifying and diagnosing microscopic cracks in practical engineering remains challenging. An intelligent method based on an improved YOLOv8s-YOLO-DEW combined with Unet is proposed for microscopic crack detection and information quantification. In YOLO-DEW, designing the CSP Bottleneck with Dynamic Snake Convolutions (C2f_DSC) enhances the fusion of features at different scales, Efficient Multi-Scale Attention (EMA) is introduced to focus on the key information of fracture features, and employing WIoUv3 loss function improves crack localization capability. Subsequently, the Unet is constructed to extract morphological features of micro-fractures. The results indicate that the comprehensive performance of the proposed method is optimal, with 83.5 % mAP for YOLO-DEW, while Unet achieving mIoU and mPA of 80.73 % and 86.49 %. Results from UAV field experiments further confirm the method’s effectiveness, which efficiently and accurately detects microscopic cracks on concrete dam faces.

Automated Geometric Quantification of Building Exterior Wall Cracks Based on Computer Vision

Automated Geometric Quantification of Building Exterior Wall Cracks Based on Computer Vision

A Three-Step Computer Vision-Based Framework for Concrete Crack Detection and Dimensions Identification

Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area in damage images. In step one, a region-based convolutional neural network (YOLOv8) is applied to train the crack localizing model. In step two, Gaussian filtering, Canny, and FindContours are integrated to extract the reference contour (a pre-designed seal) to obtain the conversion scale between pixels and millimeter-wise sizes. In step three, the recognized crack bounding box is cropped, and the ApproxPolyDP function and Hough transform are performed to quantify crack dimensions based on the conversion ratio. The developed framework was validated on a dataset of 4630 crack images, and the model training took 150 epochs. Results show that the average crack detection accuracy reaches 95.7%, and the precision of quantified dimensions is over 90%, while the error increases as the crack size grows smaller (increasing to 8% when the crack width is within 1 mm). The proposed method can help engineers to efficiently achieve crack information at building inspection sites, while the reference frame must be pre-marked near the crack, which may limit the scope of application scenarios. In addition, the robustness and accuracy of the developed image processing techniques-based crack quantification algorithm need to be further improved to meet the requirements in real cases when the crack is located within a complex background.

Intelligent segmentation and quantification of tunnel lining cracks via computer vision

Aiming to automatically, precisely, and rapidly detect tunnel lining cracks from images and extract geometric information for structural condition assessment, this study proposes a novel tunnel lining crack segmentation network (TCSegNet) and establishes a framework for calculating key geometric parameters of cracks. A tunnel lining crack segmentation dataset is first built by conducting on-site inspections of metro tunnels and collecting open-sourced tunnel images. Afterward, the TCSegNet, conforming to the encoder–decoder architectural paradigm, is designed to separate cracks from lining images pixel-to-pixel. An improved ConvNeXt and developed efficient atrous spatial pyramid pooling module constitute the encoder. The skip connections, upsampling modules, and tailored segmentation head form the decoder. Upon the segmentation results of TCSegNet, a computing framework integrating multiple digital image processing techniques is proposed to obtain the length, average width, and maximum width of cracks. The experimental results show that the TCSegNet achieves leading results among several dominant models, with 70.78% mean intersection over union (mIoU) and 57.43% F1score. Furthermore, the TCSegNet has 32.01 million parameters, requires 55.13 billion floating point operations, and gets 107.28 frames per second, proving that it has low time and space complexities and implements real-time segmentation. Also, the rationality and effectiveness of TCSegNet in alleviating the crack disjoint problem and preserving crack edge details are verified through comparative experiments. In addition, the TCSegNet achieves 71.99%, 70.45%, and 70.23% mIoU in high-resolution image segmentation, robustness, and generalization tests, respectively, demonstrating that it is competent for detecting high-resolution lining images, has a solid resistance to illumination variations, and can be well generalized to other tunnel lining image datasets. Finally, the applicability of the crack quantification framework is validated by practical application examples. The developed approaches in this study provide pixel-level segmentation results and detailed measurements of concrete lining cracks to assess tunnel structural safety status.

Decoding nonlinear ultrasonic time-frequency characteristics for fatigue crack quantification and localisation via CNN

ABSTRACT Early detection of fatigue cracks is crucial to guarantee the safe operation of engineering structures. Nonlinear ultrasonic technique (NUT) is widely used for closed crack characterisation as it breaks through the detection sensitivity limit upon closed defects that are usually invisible to linear ultrasonic techniques. The nonlinearity parameter is generally defined as the damage index (DI) to qualitatively detect these cracks. However, DI-based methods are underdetermined for quantifying and localising cracks in most circumstances. In this study, a deep learning method for intelligently decoding nonlinear ultrasonic time-frequency characteristics is developed to quantify and localise fatigue cracks. The nonlinear ultrasonic time-frequency spectrogram, which includes features characterising crack severity and location, is obtained by cleverly selecting the excitation frequency. The convolutional neural networks (CNNs) establish a mapping relationship between the nonlinear ultrasonic time-frequency characteristics and the crack length and location. It is worth mentioning that frequency-dependent convolutional kernels are proposed to more competitively decode nonlinear ultrasonic time-frequency characteristics. The results indicate that the CNNs with the frequency-correlation kernels are particularly robust and effective in both noiseless and noisy environments. The study demonstrated the potential of the proposed nonlinear ultrasonic time-frequency characteristic decoding method for accurate and efficient characterisation of damage.

A Novel Model for Instance Segmentation and Quantification of Bridge Surface Cracks-The YOLOv8-AFPN-MPD-IoU.

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures' performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks.

Advanced Parallel Laser Line-Camera System for Automatic and Precise Crack Width Measurement

Accurate quantification of cracks is crucial in evaluating building conditions. However, current non-contact measurement technologies encounter challenges in measuring crack widths, particularly in hard-to-reach areas. This paper presents a novel portable parallel laser line-camera system that is specifically designed for automatic and precise measurement of crack widths from different angles and over long distances. The system is composed of a digital camera, parallel line laser diodes, and rigid positioning arms, forming a triangulation-based configuration. A newly developed image processing algorithm is at the core of this innovation, allowing for the extraction of accurate pixel scale information from laser strips, thus overcoming the challenge of pixel distortion in non-perpendicular photography. The U-Net algorithm is utilized to efficiently identify crack regions, followed by the Canny algorithm for meticulous extraction of crack features. By utilizing the pixel scale data obtained from the laser strips, the actual width of cracks can be accurately determined. The system's efficacy and precision have been validated through laboratory and field tests. This automatic, precise, and portable solution significantly advances the field of on-site crack measurement, offering substantial potential for practical applications in building condition assessments.

Guided-wave Dynamic Probability Model-based Crack Quantification Method for Metal Structures

In the process of applying guided-wave-based aircraft online structural health monitoring, the characteristics of guided wave signals used to represent structural status exhibit random uncertainty due to the interference of loads on guided wave propagation. The trend of signal feature changes caused by damage is often obscured, making the quantification of cracks under loads one of the challenges in online structural health monitoring for aircraft. To address this challenge, this paper proposes a crack quantification method using an adaptive Gaussian mixture model (GMM) and optimized probability migration distance measurement. This method effectively mitigates the uncertainty caused by loads by constructing an adaptive Gaussian mixture model to track the probability distribution changes of guided wave signal features under different structural health conditions. Subsequently, an optimized probability migration distance measurement method is employed to characterize the structural state, further enhancing the correlation between features and the extent of structural damage. Finally, an optimized probability migration distance relationship with crack length is established to estimate the crack length of new structural specimens. The effectiveness of this method is validated using load-induced crack propagation experiments on metal structures.

Investigation of Mixed-Mode Crack Quantification with Distributed Fiber Optic Sensors

The development of high-resolution distributed fiber optic sensing (DFOS) technologies, particularly optical frequency domain reflectometry (OFDR), has proven effective in measuring crack widths. However, most existing research assumes that the crack opening direction is perpendicular to the cable direction, with limited studies addressing mixed-mode cracks using distributed fiber optic sensors. This research aims to create a novel method for quantifying mixed-mode cracks using DFOS, combining experimental investigations and numerical simulations. A calibration rig was designed and fabricated to simulate mixed-mode crack conditions in a 2D plane, allowing for the reproduction of various crack opening scenarios. Multiple fiber optic cable types with distinct strain transfer mechanisms were used, crossing the artificial crack surface at varying angles. The mechanical behavior of fiber optic cables in mixed-mode crack situations was further examined through numerical simulations. Based on findings from both experimental and numerical studies, an algorithm was developed to determine crack width and crack opening direction using DFOS data. As mixed-mode cracks frequently occur in reinforced concrete structures under complex stress conditions, this research enhances the understanding of fiber optic sensor performance in realistic cracking situations and offers valuable insights for more precise damage assessment using DFOS.

Automated intelligent measurement of cracks on bridge piers using a ring-climbing vision scanning operation robot

Detecting cracks on the surface of bridge piers is critical to the health condition of bridges. Aiming at the limitations of existing unmanned aerial vehicles (UAVs) and climbing robots, this research proposes a ring-climbing visual scanning operation robotic disease acquisition system, which achieves entire region, high-definition, and fast acquisition of cracks on bridge piers surfaces. In addition, a crack segmentation network based on an efficient self-attention mechanism and a refined segmentation algorithm are proposed to achieve accurate detection and quantification of cracks on bridge piers. Finally, an experimental prototype was built, and experiments were conducted. The results show that the maximum error of crack quantification is within 0.1 mm. The designed ring-climbing visual scanning operation robotic disease acquisition system can be applied to the surface of any pier with a 1.2 to 1.5 m diameter, which is of great significance for the automatic detection of the health condition of bridge piers.

Inclined Crack Quantification of Plate-Like Structures Based on Circular Sensor Array and Lamb Waves

Inclined Crack Quantification of Plate-Like Structures Based on Circular Sensor Array and Lamb Waves

UAV vision-based crack quantification and visualization of bridges: system design and engineering application

Accurately measuring visible cracks in bridges is crucial for their structural health diagnosis, damage detection, performance evaluation, and maintenance planning. The primary means of visual crack detection still relies heavily on manual visual inspection, an inefficient process that can pose significant safety risks. This article develops a unmanned aerial vehicle (UAV) vision-based surface crack measurement methodology and visualization scheme for the bridges that can detect and measure cracks automatically with improved efficiency. The surface crack measurement methodology is achieved by designing a three-stage crack sensing system including the You Only Look Once-based crack recognition, U-shaped network-based crack segmentation, and deep-vision-based crack width calculation. This workflow is integrated into a comprehensive UAV inspection system, which is intended for operation at the field. The surface crack visualization scheme is accomplished by taking advantage of time-series image fusion, GPS information migration, and three-dimensional (3D) point cloud technique to reconstruct the 3D geometrical model of the tested bridge, which is convenient for unveiling the crack information in the bridge. The proposed methodology was successfully validated by a case study on an arch bridge. The achievement of this article promotes the UAV vision-based bridge’s surface crack inspection technology to a new status that no preparation for pasting calibration marker is needed, and crack identification, segmentation, and width calculation are realized promptly during the UAV flying on-site, as well as damage evaluation for bridges is visually fulfilled based on the reconstructed digital-graphical 3D model. The working environments and influencing factors to the developed system are sufficiently discussed. Certain limitations in the current application are pointed out for future improvements.

Inverse finite element method and support vector regression for automated crack detection with OFDR-Distributed fiber optic sensors

Crack detection is a crucial but challenging task. In this paper, a new method is introduced for automatical detection of the substrate cracks. An anomaly index is defined based on optical frequency domain reflectometry (OFDR) measured strain and inverse finite element method (iFEM) recovered strain. The index is sensitive to crack-induced strain but not sensitive to substrate strain. Leveraging on the self-defined anomaly index, the crack location can be identified with millimetre accuracy. Then, the detection of the crack depth is regarded as a typical multi-regression analysis and solved by support vector regression (SVR) method. Particle swarm optimization (PSO) approach is applied to find optimal parameters of the SVR kernel function. Experimental cases of a simple-supported beams with different crack locations and depths are performed, demonstrating the excellent prediction accuracy of the method. The proposed method has a promising potential in identification and quantification of cracks.

Pixel-level block classification and crack detection from 3D reconstruction models of masonry structures using convolutional neural networks

Inspection and documentation of masonry structures is a time-consuming and expensive process that heavily relies on an engineer's expertise. This paper introduces a computer vision-based method that automates the creation of classified point clouds from 3D reconstruction models obtained through photogrammetry and/or laser scanning. By leveraging Convolutional Neural Networks (CNN) on 3D renders, the proposed methodology can be used to classify structural features (i.e., blocks, mortar, cracks) with greater accuracy and less effort than conventional methods. Moreover, this approach is flexible and can include further classifications by incorporating additional CNN models, allowing broader applications across various materials (i.e., concrete, steel, timber) and defects. Additionally, a precise methodology for accurate crack quantification featuring a manual annotation tool was introduced which was validated using outputs from a full-scale masonry arch bridge test carried out in the laboratory. The results emphasize the robustness of the classification approach and highlight the useful geometric information that can be gained from full-scale masonry structures. The proposed approach has the potential to use image data from UAVs/static cameras and revolutionize the way we document and inspect structures in the future.

Deep learning-assisted automatic quality assessment of concrete surfaces with cracks and bugholes

Cracks and bugholes are common factors affecting the quality of concrete surface, thus automatic detection of apparent defects is essential for concrete surface quality assessment. This study proposes an automatic quality assessment method based on deep learning for concrete surfaces with different scales of cracks and bugholes. In particular, a modified cascade mask region-based convolutional neural network (MC Mask R-CNN) with ResNet and feature pyramid network (FPN) is presented. Furthermore, a novel quantitative analysis algorithm for instance segmentation and size quantification of cracks and bugholes is proposed. Multi-scale features extracted by ResNet can be integrated using FPN. Closed operations and fixed elliptical kernels are adopted to reduce inaccuracies associated with crack discontinuities. Datasets collected at construction sites are used for training and testing of the network after data augmentation. Comparison work was carried out to evaluate the proposed model, and the box mean average precision (APbbox) and the mask mean average precision (APmask) index can reach up to 0.588 and 0.553. The overall quantization errors of cracks and bugholes were within acceptable limits. Validation experiments were conducted to illustrate the validity of the quality assessment method.