Accurate Crack Research Articles

Accurate evaluation of fatigue life and study of initial crack effects in diesel engine cylinder blocks based on an accurate crack propagation model

Accurate evaluation of fatigue life and study of initial crack effects in diesel engine cylinder blocks based on an accurate crack propagation model

Perspective Correction and Deep Learning‐Based Crack Detection for Concrete Structures

ABSTRACTThis study introduces a practical and easy‐to‐implement method for detecting cracks in concrete structures that meets engineering application requirements. The approach integrates a distance sensor with an image perspective distortion correction algorithm and employs deep learning techniques to automatically correct image distortions, extract crack information, and quantify the crack condition. This method addresses the inadequacies of previous studies in considering perspective distortion, enabling automated and efficient image capture and accurate crack identification. First, we fine‐tuned a lightweight object detection model based on the pre‐trained YOLOv8x model within the YOLOv8 framework, using a custom dataset to enhance its performance. Next, we trained a semantic segmentation deep learning model using several public datasets containing 9584 crack images and their corresponding pixel‐level annotations for precise crack detection. Additionally, a distance sensor combined with a calibration and image processing algorithm was used to remove perspective distortion and convert the size of structural defects from pixels to millimeters. This process includes capturing component edges, calculating the aspect ratio, and performing perspective correction, ensuring high accuracy in images of cracked structures taken from various angles. Field tests showed that this method improves crack detection accuracy and measurement precision under correct conditions. It simplifies the detection process and offers a reliable automated solution, enhancing the efficiency and accuracy of crack monitoring in concrete structures.

Investigation into fatigue micro-crack identification of steel bridge decks based on acoustic emission detection technology.

With increasing traffic loads and extended bridge service life, fatigue damage in steel bridge decks has become a significant concern. Traditional detection methods often lack the accuracy and responsiveness needed for practical engineering applications. To address the non-stationary nature of acoustic emission (AE) signals during crack initiation and propagation, this study combines the K-singular value decomposition (K-SVD) dictionary learning algorithm with convolutional neural networks (CNN) to enhance AE signal processing and fatigue crack detection. The K-SVD algorithm functions as an adaptive filter, learning from AE signals in various damage states to remove background noise and retain critical structural characteristics. This processed AE data is then input into a CNN, where the improved signal clarity enables higher classification accuracy. Specifically, the integration of K-SVD with CNN achieved recognition accuracies of 93.64% and 92.56% for AE signals from damaged areas, and 95.32% and 94.27% for undamaged signals, on training and test sets, respectively. This approach demonstrates strong engineering potential by providing a scalable solution for real-time, accurate crack detection in bridge inspections. Though computationally intensive, K-SVD's adaptive dictionary learning enhances CNN performance, making the combination viable with optimization strategies in practical settings. These results provide a theoretical foundation and practical guidance for improving fatigue crack detection in steel bridge decks, supporting future applications in automated bridge inspection.

An Accurate Tunnel Crack Identification Method Integrating Local Segmentation and Global Fusion Detection

An Accurate Tunnel Crack Identification Method Integrating Local Segmentation and Global Fusion Detection

Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network

Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection method for bridge surface damage based on Unmanned Aerial Vehicles (UAVs) is proposed for these challenges, incorporating a three-stage detection, quantification, and visualization process. This method enables automatic crack detection, quantification, and localization in a 3D model, generating a bridge model that includes crack details and distribution. The key contributions of this method are as follows: (1) The DCN-BiFPN-EMA-YOLO (DBE-YOLO) crack detection network is introduced, which improves the model’s ability to extract crack features from complex backgrounds and enhances its multi-scale detection capability for accurate detection; (2) a more comprehensive crack quantification method is proposed, integrating the crack automation detection system for accurate crack quantification and efficient processing; (3) crack information is mapped onto the 3D model by computing the camera pose for each image in the 3D model for intuitive crack visualization. Experimental results from tests on a concrete beam and an urban bridge demonstrate that the proposed method accurately identifies and quantifies crack images captured by UAVs. The DBE-YOLO network achieves an accuracy of 96.79% and an F1 score of 88.51%, improving accuracy by 3.19% and the F1 score by 3.8% compared to the original model. The quantification accuracy is within 10% of the error margin of traditional manual inspection. A 3D bridge model was also constructed and integrated with crack information.

ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks

Cracks are a common early road defect that tends to worsen with the aging of roads, potentially leading to severe structural damage. Timely and accurate crack detection plays a crucial role in mitigating such risks and holds significant importance for infrastructure maintenance. Deep learning techniques have demonstrated excellent performance in image-based crack extraction tasks. However, challenges persist due to the presence of numerous noisy pixels in the image background and the diverse and intricate morphologies of cracks, leading to issues such as misclassification and omission. To address these issues, this paper proposes a refined pixel-level segmentation network (ANF-Net) suitable for complex crack detection scenarios with high noise levels and diverse crack morphologies. When extracting crack features, on one hand, the network introduces an attention module tailored for crack scenes to learn pixel-wise feature weights, enabling the network to focus on crack regions and thereby reducing the impact of similar background features, mitigating false positives caused by noise misclassification. On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. This design enhances the network’s ability to extract cracks in morphologically diverse, narrow, and densely populated regions, effectively preventing issues such as crack extraction interruptions and omissions. Additionally, a multi-scale discrete wavelet transform enhancement module is designed to assist the network in considering frequency domain information that contains crack features, further improving its feature extraction capability. Simulations are conducted using three publicly available crack datasets, and the proposed method is compared with mainstream segmentation models. The results demonstrate that the proposed method achieves F1 scores of 87.9%, 82.5%, and 71.5% on the three datasets, respectively, all of which surpass the performance of current mainstream segmentation models. The proposed network accurately extracts road cracks and exhibits robust performance.

Experimental investigation of dry and water injection sandstone under biaxial compression loading based on the infrared and DIC technology

ABSTRACT To investigate the mechanisms of rock crack initiation, propagation and coalescence under stress-hydraulic coupling conditions, the infrared thermography and Digital Image Correlation (DIC) technology were employed to simultaneously monitor the fracture process of dry and water injection sandstone samples under a biaxial compression loading. Subsequently, the thermal effects and deformation evolution during crack development and propagation were analysed. Furthermore, to enhance the visibility of surface cracks on the rock samples, various algorithms were applied to improve the quality of infrared thermograms. The experimental results indicate that the Non-Uniform Correction (NUC) algorithm significantly improves the clarity of infrared thermograms, facilitating more accurate crack identification. Then, by introducing the temperature gradient index ( T gi ), the abnormal thermal effects on the surface of rock samples during crack growth were illustrated. It was found that the T gi exhibits sharp fluctuations prior to rock sample failure, attributed to thermoelastic and frictional heat effects and thermal effects of crack propagation. Moreover, the thermodynamic characteristics of crack propagation were elucidated through the integration of stress gradients and temperature gradients. The findings provide valuable insights into the dynamic behaviour of rock fracture and seepage under coupled stress-hydraulic conditions, contributing to the prediction and prevention of water-related accidents.

A Novel Fuzzy C-Means Clustering Framework for Accurate Road Crack Detection: Incorporating Pixel Augmentation and Intensity Difference Features

A Novel Fuzzy C-Means Clustering Framework for Accurate Road Crack Detection: Incorporating Pixel Augmentation and Intensity Difference Features

Hybrid neural network method for damage localization in structural health monitoring

The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. In this study, however, we explore an innovative approach based on numerical data, which is characterized by greater cost efficiency and offers intriguing research implications. This study emphasizes the evaluation of hybrid RNN-CNN models in comparison to the pure CNN models previously utilized in related research. Our proposed model incorporates a single RNN layer, complemented by essential supporting layers, which contributes to a reduction in complexity and a decrease in the number of parameters. This design choice results in a more streamlined and efficient architecture. Our experimental results reveal an accuracy of 78.9%, which, while slightly lower than the performance of conventional CNN models, underscores the potential of RNN layers in crack detection tasks. Importantly, this work demonstrates that integrating additional RNN layers can effectively enhance crack detection capabilities, particularly given the significance of preserving spatial information for accurate crack segmentation. These findings open avenues for further exploration and optimization of RNN-based methods in structural damage analysis, suggesting that the strategic use of RNNs can complement CNN models to achieve robust performance in this domain.

Unsupervised PG-DDPM-augmented mixed dataset for training an accurate concrete bridge crack detection model under small samples

Unsupervised PG-DDPM-augmented mixed dataset for training an accurate concrete bridge crack detection model under small samples

Optimized VGG Network with Dilated Residual Convolution and Path Enhancement for Crack Image Segmentation

Image crack segmentation is a critical task in infrastructure maintenance, as accurate crack detection is essential for structural health monitoring and preventing potential risks. Although traditional Convolutional Neural Networks (CNN) have achieved certain successes in image crack detection, they still exhibit limitations in handling noisy images and detecting fine cracks. This study proposes a VGG network based on dilated residual convolution and path-enhanced optimization to improve the accuracy and efficiency of image crack segmentation. The study utilizes fuzzy morphological filtering for preprocessing, introduces dilated convolution to expand the receptive field, employs residual structures to enhance feature transmission, and incorporates path enhancement modules to boost network performance. The experimental evaluation was conducted using the SDNET2018, METU Dataset, and CFD datasets. The results show that the proposed method achieves a mean intersection over union (MIoU) of 94.60% and a mean accuracy of 96.18%. The improved algorithm demonstrates significant advantages in noise interference handling and detail processing.

An optimized and precise road crack segmentation network in complex scenarios

AbstractRoad cracks pose a serious threat to the stability of road structures and traffic safety. Therefore, this paper proposes an optimized accurate road crack segmentation network called MBGBNet, which can solve the problems of complex background, tiny cracks, and irregular edges in road segmentation. First, multi‐scale domain feature aggregation is proposed to address the interference of complex background. Second, bidirectional embedding fusion adaptive attention is proposed to capture the features of tiny cracks, and finally, Gaussian weighted edge segmentation algorithm is proposed to ensure the accuracy of crack edge segmentation. In addition, this paper uses the preheated bat optimization algorithm, which can quickly determine the optimal learning rate to converge the equilibrium. In the validation experiments on the self‐built dataset, mean intersection over union reaches 80.54% and precision of 86.38%. MBGBNet outperforms the other seven state‐of‐the‐art crack segmentation networks on the three classical crack datasets, highlighting its advanced segmentation capabilities. Therefore, MBGBNet is an effective auxiliary method for solving road safety problems.

Super-resolution crack image generation for fixed PTZ camera–based highway pavement crack recognition

Abstract Among the various defects that inevitably occur on expressways, cracks are the most common and significant indicators of highway pavement damage. Timely and accurate crack recognition is urgently required for highway maintenance. Highway pavement crack recognition depends primarily on human visual inspection and expressway maintenance vehicles. These approaches are very time-consuming, labor-intensive, and difficult to implement in civil engineering. Cost-effective fixed pan/tilt/zoom (PTZ) camera–based crack recognition was investigated in our previous work. However, for pavement cracks captured at long camera distances, the limited resolution of the PTZ vision generates low-resolution crack images. In addition, the cracks exhibit weak features, such that the crack pixels density and distribution are significantly affected by the background noise, making it challenging to recognize these cracks. Aiming to solve these problems, a high-order kernel-based modified bicubic interpolation is proposed to typically reveal and characterize discrete pixel variations, obtain high-quality super-resolution crack images, and improve the recognition performance of cracks. Extensive experiments with respect to the crack datasets captured by PTZ cameras on G4/highways in China are conducted to verify the performance of the proposed method. Two measurement parameters, namely Just Noticeable Blur (JNB) and Structural Similarity Index, confirm the high quality of the super-resolution crack images. Experimental comparisons demonstrate that super-resolution crack image-based crack recognition achieves out-performance, such that the mAP, precision (P), recall (R), and F 1 -score are increased to 95.3 % , 97.3 % , 96.1 % , and 97.4%, respectively. This method proves the feasibility of high-efficiency crack recognition using modified bicubic interpolation for fixed PTZ vision-based expressways maintenance engineering.

Efficient and accurate road crack detection technology based on YOLOv8-ES

Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.

Distributed Backface Strain Sensing of Composite Adhesively Bonded Joints under Mode II Fatigue Loading

Distributed Backface Strain Sensing of Composite Adhesively Bonded Joints under Mode II Fatigue Loading

SnakeConv and SFC boosting precise segmentation on the crack of tunnel lining surface: based on DeepLabV3+ with improved Swin transformer V2

Abstract Tunnel cracks pose a significant threat to structural integrity, potentially leading to localized collapse of the infrastructure. Traditional manual crack detection methods are prohibitively expensive, highlighting the need for an efficient and accurate automatic crack segmentation model. To address this challenge, we propose a novel crack segmentation model for subway tunnel lining surface based on the DeepLabV3+ architecture. In this model, we design an improved Swin transformer V2 Base (SwinV2*) as the backbone to enhance crack segmentation performance. Considering the tubular morphology of tunnel cracks, we introduce a snake convolution module to better capture their unique features. To prevent performance degradation when fusing shallow and deep features, we incorporate a spatial feature calibration module that facilitates feature alignment and grouping along the channel dimension. We assess our model’s effectiveness using thousands of crack images captured by the image acquisition system designed for subway tunnel surfaces. Experimental results show that our model achieves strong performance metrics: 68.96% IoU, 84.33% mIoU, 87.57% PA. Compared to the original DeepLabV3+, our approach demonstrates superior performance, with a 2.89% improvement in IoU, a 1.45% increase in mIoU and notably, a significant 10.39% improvement in PA.

High‐temperature subcritical crack growth in a 3D woven SiC‐based composite

AbstractThis study investigates the subcritical crack growth behavior of a 3D woven SiC‐based composite under cyclic loading at 1100°C in air. The composite was subjected to monotonic tension and tension–tension fatigue tests using both unnotched dumbbell‐shaped and edge‐notched specimens. Detailed examinations of crack growth were conducted through optical microscopy and X‐ray computed tomography (XCT). While the monotonic tensile strength was found to be insensitive to millimeter‐sized notches, the fatigue strength was reduced by 20%–30% in the presence of such notches. Crack growth was found to be irregular and chaotic at the mesoscale, influenced by the complex interplay of chemical and mechanical processes and spatial variability in the composite microstructure. The study emphasizes the importance of XCT imaging for accurate internal crack tracking. It also proposes a phenomenological fracture mechanics model to describe crack growth and lifetime prediction, highlighting the nonmonotonic nature of crack growth rates.

Optimizing concrete crack detection an echo state network approach with improved fish migration optimization

There are numerous reasons for concrete buildings cracks, like stress loads, material flaws, and environmental impacts. It is important to find and investigate the concrete cracks during analyzing the safety and structural soundness of buildings, bridges, and other infrastructure. However, there are many models available for concrete crack detection, an efficient approach is needed because the existing methods often have flaws like overfitting, high computational complexity, and noise sensitivity, which can lead to accurate crack detection and classification. This paper proposes an enhanced version of the fish migration optimization (IFMO) method combined with an optimized echo state network (ESN) model for concrete fracture detection using the combination form is established for improving the detection accuracy of the model by optimal arrangement of the ESN. The suggested ESN/IFMO model was tested on the SDNET2018 dataset, which comprises concrete photos with diverse fracture patterns, and the results were compared to several other state-of-the-art approaches. The suggested ESN/IFMO model shows potential as a more effective solution for concrete crack identification, increasing accuracy by 3.75–8.19% over current models such as DL, DINN, AlexNet, CNN, and LSTM, as well as increasing F1 score by 5.14–12.55%.

Detection and Classification of Surface Cracks Using Deep Learning Based Autoencoders in Eddy Current Testing

AbstractIndustrial equipment subjected to rigorous conditions of high speed and pressure leads to the development of cracks on metal surfaces. These cracks reduce the service life and threaten the safety of parts, and the deeper the crack, the greater the resulting damage. Crack detection and crack depth evaluation continue to take center stage in quantitative non‐destructive testing and evaluation (NDT&E 4.0). The accuracy of the rotating uniform eddy current (RUEC) probe in achieving fast and efficient detection of surface cracks is corroborated by a comparison with previous experimental results. Next, accurate crack depth classification is achieved by building deep learning model based on a sparse autoencoder (SAE) and a multi‐layer perceptron (MLP) model. These classifiers are combined with eddy current testing (ECT) data, including the normal magnetic component Bz. As a result, evaluation metrics such as accuracy increased by up to 100% with both precision and recall scores of 1 for the deep sparse autoencoder classifier compared to MLP performance. The originality of our approach is evident in the application of deep SAE, which achieves high classification accuracy. Furthermore, the integration of our high‐resolution NDT&E RUEC probe with advanced machine learning models for depth classification is both novel and impactful. This unique combination offers a comprehensive framework for crack analysis, from precise detection to detailed characterization. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Thermal effects on mode I fracture of sandstone: Accurate crack identification in thermal-mechanical coupled peridynamic simulations

Thermal effects on mode I fracture of sandstone: Accurate crack identification in thermal-mechanical coupled peridynamic simulations