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In view of the low contrast between the edge of concrete cracks and the background, this paper proposes a R-Unet target detection framework combined with edge detection operator. Firstly, the Roberts operator is used to extract the target edge feature (3-band), which is fused with the original data to form a 6-band image as input data. Secondly, based on the Unet model, the residual module is introduced into the encoding path to reduce the network overfitting, and the attention module is added in the decoding path to enhance the learning ability of the pixels in the crack area and improve the accuracy of concrete crack extraction; a combination of the cross entropy loss function and Dice loss function is used for evaluation. The results of experiments on the self-made concrete crack datasets show that R-Unet target detection framework is significantly better than the original Unet model and other relevant models. The overall extraction accuracy reached 95.75% with the recall rate of 94.33%, and the MIou of 90.67%. The R-Unet target detection framework proposed can greatly reduce the crack edge false detection rate and improve the target detection accuracy. [ABSTRACT FROM AUTHOR]

Summary: Cracks can develop in high‐rise buildings because of long‐term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning‐based identification methods can efficiently identify cracks, the accuracy of crack identification in high‐rise buildings needs to be improved due to the lack of crack datasets specifically related to high‐rise structures. Moreover, the number of available images of cracks in high‐rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high‐rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high‐rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full‐scale high‐rise buildings. The trained network can accurately identify different components in images of the full‐scale, high‐rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application‐specific context in crack identification. [ABSTRACT FROM AUTHOR]

It is meaningful to monitor and identify concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper, a new method based on distributed long-gauge optic fiber sensing is proposed to implement crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then, the indicators with different sensors are normalized by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of the damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence of the aircraft, such as taxiing position, aircraft load, and aircraft type, is not obvious enough to mislead the identification results. Lastly, the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results, the prefabricated cracks inside the pavement slab can be identified accurately with the proposed method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position. [ABSTRACT FROM AUTHOR]

The tooth crack identification through the effect of the tooth root crack on nonlinear vibration behaviors in a straight bevel gear (SBG) system is sought. The mesh stiffness is evaluated through an analytical method based on the potential energy and Tredgold approximation associated with tooth root crack modeling. A 10-dof model is developed for the SBG system where the backlash nonlinearity is of concern. To assess the nonlinear vibration behaviors of the SBG system, first, the dynamic response is extracted analytically with the proposed dynamic model, and experimentally with a designed setup, then the extracted response is assessed based on the different crack identification statistical factors. Results indicate that the Skewness is the most effective factor in identifying the crack in the SBG system with backlash nonlinearity, compared to other investigated factors. The nonlinear vibration response as a time history, phase diagram, Poincaré map, modal analysis, and mesh stiffness FFT spectrum is analyzed and recommended as an appropriate indicator for tooth root cracks. The modeled mesh stiffness and simulated SBG system are verified by applying FEM and the experimental method. [ABSTRACT FROM AUTHOR]

Objective The main defects on metro tunnel inner wall are cracks and water seepage. Current detection methods based on manual and semi-automatic equipment have problems such as high intensity, low efficiency, and low reliability. The intelligence-based detection/identification algorithm and detection system should be studied to realize informative and intelligent detection for metro tunnel lining defects. Method The current status of metro tunnel defect inspection technology is analyzed, and a set of algorithms applicable to the identification of metro tunnel lining defects are put forward, mainly including image processing algorithm, defect category detection algorithm, and defect grading detection algorithm, etc. Four indexes are chosen to evaluate the detection effect of the identification algorithm. Furthermore, an intelligent detection system for metro tunnel lining defects based on deep learning method is established from both software and hardware aspects. Finally, the system is implemented on Beingjing Metro Line 3 to analyze the reliability of its on-site application. Result & Conclusion After the application of the intelligent detection system, the detection rate of metro tunnel lining crack defects reaches 91.95% with a false detection rate of 0.89%. The detection rate of seepage defect is 93.83% and the false detection rate 0.65%. This system can be used as the core intelligent detection platform of metro tunnel, effectively detecting various defects in metro tunnel.

Machine learning techniques can be used for the prediction of cracks in beam type structures. Indeed, these techniques present an important management aspect which consists in developing a maintenance decision model, which can anticipate future failure trends in order to improve the maintenance decision process. The prediction of open cracks on the edges of beams is a problem often encountered in industry and can be detected by considering that the crack is simulated by means of a rotating spring, whose stiffness can be identified by the size of the crack. This article proposes two different techniques for crack detection in Euler-Bernoulli model functional gradient beams. The first technique generates frequency contours from a three-dimensional plot of the crack position and size, and the intersection of distinct mode contours predicts the crack location and size. The second technique uses a meta-heuristic optimization, which is inspired by astrophysics and based on well-known exoplanet discovery methods, to determine crack size and position concurrently. The weighted sum of the squared errors between the measured and computed natural frequencies is utilized to design the objective function in this second strategy. The results reveal that the two crack size and location prediction techniques agree quite well. [ABSTRACT FROM AUTHOR]

Alternating current magnetic flux leakage (ACMFU technology has been used for sizing pipeline cracks. However, traditional ACMFL methods are limited in sensitively identifying arbitrary angles of pipeline cracks due to the complexity of crack morphology. To solve the problem, a multi-frequency balanced electromagnetic field measurement (MF-BEFM) method is proposed. First the distortion of the magnetic field and eddy current field caused by crack defects was studied. Then, the effects of the excitation frequency on balanced electromagnetic field measurement (BEFM) signals were considered to select optimal parameters. Next, the relationship between the BEFM signal and crack angle was studied. Finally, the MF-BERM experiment was conducted to prove the superiority of the proposed method. The results show that the BEFM method cannot only overcome the deficiency of directional detection of ACMFL methods but it call also achieve high detection sensitivity for identifying arbitrary angles of cracks on the pipeline surface. [ABSTRACT FROM AUTHOR]

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of "illness", if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%. [ABSTRACT FROM AUTHOR]

Concrete architectural structures are widely used in urban construction, making the health diagnosis and maintenance of these structures increasingly essential and urgent. Crack identification is crucial for maintaining the structural integrity and safety of buildings. Traditional methods rely on manual inspection, which is plagued by low accuracy, inefficiency, and safety hazards. This paper proposes a technique combining an attention-based SqueezeNet network with Gradient-weighted Class Activation Mapping (Grad-CAM) for automatically recognizing and visually explaining building cracks. By integrating the Squeeze-and-Excitation (SE) attention mechanism with the lightweight SqueezeNet network, this method can adaptively adjust the importance of feature channels by learning global information, effectively improving the network’s accuracy and efficiency. The experimental results show that the Att-SqueezeNet model achieved a high precision of 0.995, a training time of only 133 seconds, and a model size of 4.9M, significantly outperforming models such as SqueezeNet, RF, CNN, VGG-19, and B-CNN. This demonstrates its robustness, rapid identification and suitability for practical applications and building crack identification. Moreover, the utilization of Grad-CAM for visualization not only offers an intuitive explanation of the model’s decision-making process but also provides a more comprehensible understanding of crack detection results. This is crucial for advancing building maintenance automation, reducing reliance on manual labor, and increasing the precision and reliability of detection tasks.

This article aims to identify the presence of cracks in slender rotating beams (Euler Bernoulli) from the dynamic behaviour of cracked beams operating at low rotational speeds. For this purpose, the behavioural model of the cracked rotating beam developed by the authors in previous works is shown. The results of the mathematical model developed (natural frequencies) feed a novel meta-heuristic optimisation algorithm based on the survival tactics of rabbits against their predators: Artificial Rabbit Optimization (ARO). The application of this algorithm to the first two natural frequencies of vibration obtained with the analytical model and contrasted in previous works gives rise to the identification of the characteristic parameters of the crack contained in the beams. The estimation of the parameters: position along the beam and crack depth, show a high similarity with the initial data, which allows validating the application of the optimisation algorithm to the identification of cracks in this type of component as a first approach to a health monitoring method for more complex rotating cantilever beam structures. [ABSTRACT FROM AUTHOR]

To reduce the reliance of existing mainstream vision-based crack detection algorithms on annotated datasets, this paper presents an unsupervised semantic segmentation-based approach. The proposed method first employs the Felzenszwalb-Huttenlocher algorithm for pre-segmenting the image, generating superpixels. Subsequently, a with an autoencoder model is designed to progressively approximate the superpixel segmentation results, and the optimal model is obtained by Bayesian optimization. Through comparative experiments with existing algorithms, it has been demonstrated that the proposed method performance comparable to supervised algorithms, even without the need for labeled data. As a result, the deployment complexity of the algorithm is significantly reduced, while expanding its applicability. [ABSTRACT FROM AUTHOR]

This paper presents a rapid and precise deep learning-based approach for measuring cracks in concrete structures. The proposed methodology involves data acquisition, pre-processing, model construction and post-processing. The Deeplabv3+ model is used for crack detection, and the use of Coordinate Attention is introduced to enhance its performance. And the implications of attention locations in the model are discussed. Insertion position after atrous spatial pyramid pooling (ASPP) operation is the most effective and accurate. The crack widths are obtained through post-processing, and the actual width is determined using the width conversion equation. The proposed method achieves an effective crack detection MIoU of 85.95 and a calculated width error of 6.3%, with a reduction of 2.5% compared to traditional models. Numerical experiments and real-world building experiments have demonstrated the feasibility and effectiveness of the proposed method. Overall, the proposed method presents a fast and dependable crack detection technique that has great potential for practical application. [ABSTRACT FROM AUTHOR]

This study presents an intelligent identification method for shale crack networks based on transfer learning. Focuses on investigating the main physical parameters of shale similar materials based on the similarity theory and the physical parameters of shale. Fracture network images obtained from shale-like materials. The fracture network images are then preprocessed using image processing technology to generate a high-quality shale image dataset. A deep learning transfer recognition model, based on ResNet-50, is constructed to detect model performance using these shale fracture network images. Experimental results and reliability analyses demonstrate that the ResNet-50 based deep learning transfer model achieves an accuracy of 87% for shale images, indicating high recognition accuracy and fast model convergence. The proposed intelligent shale crack identification method exhibits robustness and generalization ability, making it suitable for efficient fracture identification in geological, road, and bridge deck projects. [ABSTRACT FROM AUTHOR]

Liquefied natural gas (LNG) storage tanks are an essential infrastructure for national energy security, and the monitoring of their integrity is the basis for ensuring the regular operation of storage tanks. Existing monitoring methods do not reflect changes in storage tanks well because most monitoring methods are based on a technical approach that allows only one type of monitoring data to be obtained, which does not give comprehensive monitoring results. In this study, we incorporate control measurements, light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) photogrammetry, thermal infrared camera monitoring, interferometric synthetic aperture radar (InSAR), and other techniques. An integrated air--space--land multisource remote sensing monitoring scheme is proposed, and the scheme's feasibility is verified through actual measurements, providing strong support for subsequent, efficient, and all-around monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a new method for identifying and characterizing straight cracks in plate-like structures is presented. The method combines the finite element method (FEM) using the software Abaqus and the success history-based adaptive differential evolution algorithm (SHADE). The objective of the method is to minimize the mean relative error between the measured experimental frequencies of a plate with an unknown crack identity and the numerical frequencies obtained using the Shade-FEM approach. The crack identity is defined by its length, orientation, and centre coordinates. To validate the effectiveness of the proposed approach, two strategies are applied. In the first strategy, the inverse problem is solved using the natural frequencies of a plate with a known crack identity obtained through modal simulation in Abaqus. In the second strategy, the experimental frequencies of a cracked plate are used. The results of the study demonstrate that the proposed approach achieves promising results with just a population size of 25 and 150 iterations. The outcomes show high accuracy, as indicated by a relative error of the objective function below 0.8%. Overall, the study demonstrates the effectiveness of using the Shade-FEM approach for identifying and characterizing straight cracks in plate-like structures, offering potential applications in various engineering and structural integrity fields.

It is meaningful to monitor and identify concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper, a new method based on distributed long-gauge optic fiber sensing is proposed to implement crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then, the indicators with different sensors are normalized by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of the damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence of the aircraft, such as taxiing position, aircraft load, and aircraft type, is not obvious enough to mislead the identification results. Lastly, the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results, the prefabricated cracks inside the pavement slab can be identified accurately with the proposed method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position.

With the continuous expansion of the application range of gob–side entry retaining technology, the depth, height, and advancing speed of coal seams also increase, which brings great problems to the stability control of surrounding rock structures of gob–side entry retaining. As one of the main bearing structures of the surrounding rock, the stability of the roadway–side support body is a key factor for the success of gob–side entry retaining. In order to study the deformation characteristics and instability mechanism of roadway-side support body, based on the roadway–side support materials of gob-side entry retaining, the dynamic expansion test of back–filling concrete cracks under uniaxial compression was carried out. The YOLOv5 algorithm was applied to establish the fine identification and quantitative characterization method of macroscopic cracks of the samples, and the dynamic expansion rule of roadway-side support body cracks and its dimensional effect were revealed by combining the fractal theory. The results show that the F1 value and average precision mean of the intelligent dynamic crack identification model reached 75% and 71%, respectively, the GIoU loss value tends to fit around 0.038, and the model reached the overall optimal solution. During the uniaxial compression process, micro cracks on the surface of the back–filling concrete first initiated at the end, and after reaching the yield stress, the macroscopic cracks developed significantly. Moreover, several secondary cracks expanded, pooled, and connected from the middle of the specimen to the two ends, inducing the overall instability of the specimen. The surface crack expansion rate, density, and fractal dimension all show stage change characteristics with the increase in stress, and the main crack expansion rate has obvious precursor characteristics. With the increase in the size, the decrease in crack density after back–filling concrete failures gradually decreases from 93.19% to 4.08%, the surface crack network develops from complex to simple, and the failure mode transits from tensile failure to shear failure. The above research results provide a basic experimental basis for design optimization and instability prediction of a roadway–side support body for engineering-scale applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application. [ABSTRACT FROM AUTHOR]

Our study focuses on the dynamic transient analysis of arched beam bridges over rivers, which face unique geohazards and challenges, including vibrations and dynamic loads that can affect structural integrity. The finite element software ANSYS v. 19.3 was employed to assess acceleration time histories at various bridge positions. Using MATLAB, we conducted wavelet packet decomposition to extract insights from the data, specifically isolating river-induced influences. In this article, the introduction of the wavelet packet rate index (WPERI) is presented as a novel metric for the detection of cracks in the curved bridge segments over rivers. The WPERI proves reliable in accounting for the river environment's impact on structural integrity. Our findings highlight the sensitivity and precision of the WPERI in accurately detecting cracks and vulnerabilities in these riverine bridges. By combining WPT, finite element analysis, and signal decomposition, our research offers insights into tailored crack detection methods for riverine bridges. This study underscores the potential of WPT as a tool for identifying and characterizing cracks in curved bridge elements over rivers. The innovative WPERI provides a holistic approach to addressing structural issues, thus enhancing bridge durability amid changing environmental conditions. It contributes significantly to structural engineering and paves the way for the further exploration of river-specific crack detection techniques. [ABSTRACT FROM AUTHOR]

In this study, a new method for identifying and characterizing straight cracks in plate-like structures is presented. The method combines the finite element method (FEM) using the software Abaqus and the success history-based adaptive differential evolution algorithm (SHADE). The objective of the method is to minimize the mean relative error between the measured experimental frequencies of a plate with an unknown crack identity and the numerical frequencies obtained using the Shade-FEM approach. The crack identity is defined by its length, orientation, and centre coordinates. To validate the effectiveness of the proposed approach, two strategies are applied. In the first strategy, the inverse problem is solved using the natural frequencies of a plate with a known crack identity obtained through modal simulation in Abaqus. In the second strategy, the experimental frequencies of a cracked plate are used. The results of the study demonstrate that the proposed approach achieves promising results with just a population size of 25 and 150 iterations. The outcomes show high accuracy, as indicated by a relative error of the objective function below 0.8%. Overall, the study demonstrates the effectiveness of using the Shade-FEM approach for identifying and characterizing straight cracks in plate-like structures, offering potential applications in various engineering and structural integrity fields. [ABSTRACT FROM AUTHOR]

One of the most dangerous field problems in the civil engineering discipline is the suddenly developed cracks in the building, which could be caused by the swelling of the subsurface soil. Thus, this work has focused on employing a procedure in the geographic information system known as the inverse distance weighted (IDW) technique, to analyze the extent of cracks in a residential complex in the city of Kirkuk in Iraq using the physical and chemical soil data for seven boreholes from the field of the study. Physical soil parameters such as liquid limit (LL), gravel, sand, silt and clay percentages were characterized first, followed by chemical properties such as gypsum content (GYP), total suspended solids (TSS), potential of hydrogen (pH), and organic content (ORG). Furthermore, statistical studies such as plasticity index (PI) and soil characteristics association, linear single, and various linear multi-regression models were used. The data analysis shows that there are significantly positive and negative relationships between PI as a swelling indicator and the physical and chemical soil properties, although weak to moderate correlations were observed between PI and these variables. The PI values were accurately predicted by the proposed linear multi-regression models of the physical and integrated physical and chemical soil characteristics, with multiple R values of 0.92 for both models. As a result, the suggested statistical models can provide complete geographic and mechanical explanations for the crack sources in the investigated residential complex. [ABSTRACT FROM AUTHOR]

In order to realize high-precision and high-efficiency identification of multi-scale distribution characteristics of coal fractures, carry out the study of multi-scale distribution characteristics identification methods based on CT digital core deep learning. Industrial CT scanning system is used to collect a large number of coal original CT digital core information array, the CT digital core information array is converted into a two-dimensional gray-scale image and then it is divided into square images of different scales and the image brightness is enhanced to different levels as training samples, Finally, the construction and optimization of model parameters of AlexNet, ResNet-18, GoogLeNet and Inception-V3 models for the identification of CT-containing fractures are realized by Matlab platform. Study the recognition accuracy and verification accuracy of different model training under different number of training samples; Study the accuracy, calculation efficiency and training time of different models for images with different scales and brightness under the same training sample, obtain the optimal model for calculating the fractal dimension of two-dimensional CT images with fractures, then, the fractal distribution characteristics of each fracture image are calculated according to the statistical method of box-counting dimension, compared with the traditional binarization method and human eye recognition method, The applicability of the multi-scale distribution characteristics identification method of coal fractures based on CT digital core deep learning is verified. The result shows: ① ResNet-18 model is the optimal model for calculating the fractal dimension of two-dimensional CT images with cracks when the image sample is brightness 4 and the scale is 3.5 mm to 21 mm, the model has high accuracy and short training time in calculating the fractal dimension of two-dimensional CT fracture images. ② Compared with the traditional binarization method, the multi-scale recognition method of coal fracture based on CT digital core deep learning has the advantages of fast speed, high accuracy and is not easily affected by impurities in coal.

In this paper, the images of tunnel surface are obtained by tunnel lining rapid inspection system, and tunnel crack forest dataset (TCFD) is established. The disaster characteristics of tunnel cracks are analyzed and summarized. Solutions of tunnel crack segmentation (TCS) method are developed for the detection and recognition of cracks on tunnel lining. According to the image features of the tunnel lining and the optical principal of detection equipment, effective image pre-processing steps are carried out before crack extraction. The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks. Local threshold segmentation method is used to traverse the blocks successively, and the first target block with crack is obtained. The seed in the target block were obtained by adaptive localization method and mapped to the whole image. Region growing is performed through crack seed until complete tunnel crack is extracted. The results show that the precision, recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%, 93.07% and 92.82% without strong interference. According to the binary images processed by TCS method, the projection images of different types of tunnel cracks and their respective laws are obtained. Furthermore, the TCS method is implemented and deployed as a GUI software application.

If not detected early, the cracks in structural components may ultimately result in the failure of the structure. This issue becomes even more critical when the component under investigation is a prosthesis placed in the human body. This study presents a crack location identification method based on the time domain in a cantilever beam of metallic biomaterials (CBMB). The absolute difference between the central difference approximation of the root mean square (RMS) of displacement of points on the cracked and uncracked beams was applied as a cracked location indicator. Captured time-domain data (displacement) at each node of the cracked and uncracked beams were processed into a central difference approximation of the RMS of displacement. Then, the crack could be detected by a sudden change of the cracked location indicator. The feasibility and effectiveness of the proposed method were validated by numerical simulations. The finite-element simulation of a CBMB with a transverse notch was analyzed in the numerical study. The notch or crack was detected along the beam under a moving load at various locations. A set of simulation experiments and numerical calculations was performed to determine whether the proposed identification method would accurately detect the location of a crack in a cantilever beam under a moving load compared to the location found by an exact solution method. The results showed that the proposed method was not only as able as the analytical method but also robust against noise: it was able to detect a crack precisely under 5% noise.

The present paper addresses development of a procedure for crack detection in functionally graded Timoshenko beam using a distributed piezoelectric sensor. Crack is represented by a pair of translational and rotational springs of stiffness calculated from its depth. A piezoelectric layer bonded to the beam is employed as a distributed sensor. Adopting the double beam model for the beam with the sensor, governing equations are conducted and utilized for establishing a database to propose a procedure for detecting a single crack in a functionally graded beam using the sensor output charge. The modal sensor charge provides a novel indicator more efficient for crack identification in functionally graded beams than natural frequencies. The effect of measurement noise, sensor thickness, and material parameters on crack identification has been investigated for beams with different boundary conditions. [ABSTRACT FROM AUTHOR]

Crack imaging is attractive in the community of nondestructive testing and structural health monitoring of plate-like structures. However, most existing research focuses on the imaging of single crack, while limited work has been reported for the imaging of multiple cracks. To this end, the application of the Lamb wave-based technique is presented in this paper to image the multiple cracks. The antisymmetric mode (A0) of Lamb wave propagation in the thin plate is simulated using the numerical finite element method, and a circular piezoelectric sensor array is considered to capture the Lamb wave signals. The damage index taking into account the attenuation of the reflected signal amplitude is proposed using the reflection signals from the cracks, and the Hilbert transform is used to extract the information of damage. The damage scanning map is constructed to show the damage location in the plate. The proposed damage identification algorithm based on Lamb wave propagation is also used to detect two cracks in the thin plates. The consideration of wave attenuation improves the identification of multiple cracks. The proposed damage identification algorithm using a phased piezoelectric sensor array shows its potential to locate the cracks in thin plates. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The results of high-precision asphalt concrete pavement crack identification can provide help for pavement maintenance. Therefore, methods of image feature enhancement and crack identification of asphalt concrete pavement cracks are proposed. First of all, we used an industrial CCD camera mounted on a vehicle to collect an asphalt concrete pavement crack image. Then, after using the NeighShrink algorithm to denoise the acquired image, a fractional differential image enhancement algorithm was designed based on image feature blocks to enhance the image features. On this basis, crack characteristics were segmented and processed by watershed algorithm. Through crack direction identification and crack parameter extraction, crack distribution direction, crack length and width and other parameters of asphalt concrete pavement were obtained in order to achieve accurate identification of asphalt concrete pavement cracks. The experiment found that this method can effectively remove noise information from asphalt concrete crack images; after applying this method, the image entropy value of each image was improved, with a minimum improvement of 0.38 and a maximum improvement of 1.98. The time consumed by this method in identifying cracks in asphalt concrete pavement varied between 1.4 s and 2.4 s. When identifying the length of cracks in asphalt concrete pavement, the maximum deviation value was only 0.47 mm; when identifying the width of cracks in asphalt concrete pavement, the maximum deviation value was only 0.31 mm. The above results indicate that by enhancing the image features of asphalt concrete cracks, this method achieves more accurate identification results for crack distribution direction, length and width values, with high identification efficiency and good application effect. [ABSTRACT FROM AUTHOR]

To improve the accuracy of computer vision-based bridge crack-width identification, two factors were investigated in this study. First, a fully convolutional neural network combined with a U-Net architecture was used to extract crack pixels and a crack midline (i.e., crack skeleton). A database including 100 images with 572 × 572 pixels labelled for cracks was developed. The results revealed a mean of 84.4% of the average mean intersection over union of the U-Net. In addition, a crack-width direction identification method based on the slope of the crack skeleton was proposed, and the accurate extraction of the nonuniform width parameter along the crack was realised. Second, to obtain a mapping from the width pixels to width physical dimensions, a pixel calibration experiment was conducted. Finally, a nonlinear regression model of the distance, focal length, and actual pixel size was developed to overcome the light distortion of the camera. Ultimately, the combination of the two factors completed a high-precision extraction of the entire process of crack-width identification and achieved a 0.01-mm precision under a 96% guarantee rate. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based crack detection techniques have been widely used in ground crack detection, urban street crack detection, ordinary wall crack detection, and road tunnel crack detection. However, due to the scarcity of data, crack detection in railway tunnels is temporarily rare, and at the same time, some existing railway tunnels of relatively old age have extremely limited lighting conditions, which are subject to the dark conditions in railway tunnels, as well as the structural surface noise and crack-like interferences that can cause great challenges to the identification of cracks in railway tunnels. Based on this, this paper collects images inside real-world railway tunnels, produces a dataset, and proposes a novel and effective hybrid neural network tunnel crack disease recognition iFormer Unet model, which is based on the iFormer block module that can extract high-frequency features and low-frequency features at the same time, and constructs a U-shape network consisting of an encoder, a Bottleneck, a decoder, and a jump connection U-shaped network composed of encoder, Bottleneck, decoder and jump connection. The results of 10-fold cross-validation in the experiments show that the proposed method has a relatively low misdetection rate of about 7.56%, with about 30.31M Params and 34.84G FLOPs. iFormer Unet model has the lowest misdetection rate compared to the Swin Unet and Unet models, which are 5.28% and 8.58% lower, respectively, when tested on six image categories. 5.28% and 8.58% respectively. The proposed iFormer Unet algorithm realises the automatic identification of cracks in railway tunnels under harsh environments, which provides a certain reference and basis for the maintenance of railway tunnels.

The traditional detection process of deformation or crack of crane main girder structure is tedious, inefficient and difficult to control safety risk. In this work, combining unmanned aerial vehicle (UAV) images with photogrammetry technology, an efficient and high-precision method for crane main girder defect detection and identification based on UAV 3D reconstruction is proposed. Firstly, the flight path, suitable for the chamfered rectangular section is selected, according to the structural characteristics of the main girder and the flight requirements of the UAV photography, and the calculation equation of flight strategy parameters is deduced. Secondly, based on 3D reconstruction methods and crucial techniques, the influence of image number, marker points and structure size effects on the accuracy of the 3D reconstruction model are investigated, separately, thus comprehensively verifying the accuracy of the 3D reconstructed model. Finally, the proposed method is tested and evaluated by the experiments for deformation detection of crane main girder structure and crack identification of scaled carton, obtaining millimeter-level accurate results, and the high precision deformation detection and crack identification using non-contact 3D reconstruction are realized. The results demonstrate that the combination of UAV and photogrammetry show potential in the deformation detection of crane main girder structure, it also has a prospect in accurately evaluating the geometry of cracks and helping structural engineers to assess the health status of structures.

One of the most dangerous field problems in the civil engineering discipline is the suddenly developed cracks in the building, which could be caused by the swelling of the subsurface soil. Thus, this work has focused on employing a procedure in the geographic information system known as the inverse distance weighted (IDW) technique, to analyze the extent of cracks in a residential complex in the city of Kirkuk in Iraq using the physical and chemical soil data for seven boreholes from the field of the study. Physical soil parameters such as liquid limit (LL), gravel, sand, silt and clay percentages were characterized first, followed by chemical properties such as gypsum content (GYP), total suspended solids (TSS), potential of hydrogen (pH), and organic content (ORG). Furthermore, statistical studies such as plasticity index (PI) and soil characteristics association, linear single, and various linear multi-regression models were used. The data analysis shows that there are significantly positive and negative relationships between PI as a swelling indicator and the physical and chemical soil properties, although weak to moderate correlations were observed between PI and these variables. The PI values were accurately predicted by the proposed linear multi-regression models of the physical and integrated physical and chemical soil characteristics, with multiple R values of 0.92 for both models. As a result, the suggested statistical models can provide complete geographic and mechanical explanations for the crack sources in the investigated residential complex.

Thin cylindrical shells are susceptible to cracking under long-term load and external impact, and it is of considerable scientific and technical value to investigate the nonlinear vibration response characteristics and monitor the health condition of the shell structure. Based on the Flügge shell theory, the nonlinear dynamic model for the thin cylindrical shell is established. By the partial Fourier transform combined with the residue theorem, the forced vibration generation and propagation mechanism of the thin cylindrical shell are investigated, and the analytical solution of forced vibration displacement in the space domain is obtained. Then, the local flexibility matrix is derived from the perspective of fracture mechanics, and the continuous coordination condition on both sides of the straight crack is constructed using the linear spring model. Combined with the wave superposition principle, the analytical approach for nonlinear vibration response is proposed to reveal the evolution law of vibration characteristics of the thin cylindrical shell with a straight crack, and then, a straight crack identification method based on natural frequency isolines and amplitude maximization methods is presented. Finally, the effect of various morphological information of the straight crack on the nonlinear vibration response characteristics of the thin cylindrical shell is studied in detail, and a numerical case is conducted to verify the effectiveness of the proposed straight crack identification method. [ABSTRACT FROM AUTHOR]

The crack breathing model and crack identification method for rotors using nonlinearity induced by cracks are studied in this work. Firstly, the finite element method is utilized to model a rotor–bearing system with a response-dependent breathing crack to obtain the numerical data for crack identification. During the modelling, an improved breathing crack model is proposed, focused on the unreasonable assumption about crack closure line in the original crack closure line position (CCLP) model. Compared with the original model, the improved breathing model can reflect the nonlinear behavior of cracks better. Secondly, based on the established model, super-harmonic features at 1/3 and 1/2 of the critical rotating speeds under different crack locations and crack depths are extracted for crack identification. Additionally, the super-harmonic features from two measurement points are used as inputs into an artificial neural network with a Levenberg–Marquardt back-propagation algorithm, corresponding crack positions and depths as outputs. The robustness of the method is tested by examining the identification results under different levels of noise. The results show that the proposed crack identification method is efficient for simultaneous identification of crack depth and position in operating rotors. [ABSTRACT FROM AUTHOR]

At the request of two types of complex reservoir projects in the depth of a domestic oilfield, the multi-sensor combined field geological outcrop collection technology and multi-scale digital outcrop construction technology are used to establish a comprehensive geological knowledge base based on multi-scale-multi-information three-dimensional digital outcrop, one of the important tasks is the automatic identification and research of deep learning in lithology, seam and other aspects. Based on the high resolution and complexity of the crack images taken by the drone, the original images were cut in the image preprocessing stage and grayscaled to reduce the amount of computation. Using a network model based on U-Net structure, the attention mechanism is introduced in the jump connection stage of the traditional U-Net model to improve the grasping ability of the target features and improve the performance of the model. Using the processed images of real rock cracks, the model was trained and tested. The experimental results show that the improved network model has a good crack identification effect, the accuracy can reach 88%, which is 2. 37% higher than that of the original network, the loss is about 0. 110, and the F1 score can reach more than 85. [ABSTRACT FROM AUTHOR]

The crack identification and extraction is the basis of understanding the evolution law of surrounding rock cracks. The existing methods for extracting stressed rock cracks based on visible light photos are poor in extracting the cracks of complex rocks (such as granite) composed of polychromatic minerals. In order to solve the problem, a method for extracting stressed rock cracks based on light reflectivity mutation is proposed. The method comprises four key steps of light reflectivity calculation, light reflectivity mutation point identification, light reflectivity mutation point space heterogeneity analysis and crack information extraction. The spatial differentiation of the mutation points of light reflectivity caused by crack growth is the key characteristic of crack identification. Therefore, the index-differentiation coefficient C, which reflects the concentration degree of spatial point distribution, is introduced. It is used to judge whether there is a crack in the digital image according to the spatial differentiation of the mutation points of light reflectivity. After the crack in the digital photo is judged, on one hand, the instantaneous information of the crack activity is reflected through the change speed of the light reflectivity, so as to study when and where cracks occur or expand in the specimen. On the other hand, the cumulative information of crack activity is extracted to analyze the development, propagation and penetration degree of rock surface cracks over a period of time. The experimental result shows that: ① The development and propagation of stress cracks in rock can cause the mutation of light reflectivity. The mutation speed can reach 0.2/s, which is much larger than the change speed (0.03/s) caused by other random factors. ② At the crack active time, the mutation point of light reflectance shows significant spatial differentiation (C value can reach 189), and the differentiation coefficient is much larger than that in random distribution (C value is 1). ③ The proposed method can extract the instantaneous information and cumulative information of crack activity. Finally, the reason for the change of light reflectivity during rock crack, the significance of rock mechanics of the proposed method and its application prospect in roadway safety inspection are discussed.

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data. [ABSTRACT FROM AUTHOR]

Pavement will inevitably be damaged in the process of use; pavement damage detection and assessment are an important part of maintenance management. In order to prevent road diseases, it is necessary to fix the road cracks and implement automatic road crack inspection and monitoring. In this paper, the automatic identification of road cracks is realized by constructing the Mask R-CNN model. The labeled area can be segmented by pixels and positioned at the original data by integrating the image data used for training and the labeled data into a network model. The effect of the training model can be improved by increasing the number of data sets, the pixel of the fracture image, the background of the fracture, and the marking method of the fracture type. The validity and accuracy of the test results were characterized by RPN bounding-box loss, classification loss, mask loss, and total loss. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the application range of gob–side entry retaining technology, the depth, height, and advancing speed of coal seams also increase, which brings great problems to the stability control of surrounding rock structures of gob–side entry retaining. As one of the main bearing structures of the surrounding rock, the stability of the roadway–side support body is a key factor for the success of gob–side entry retaining. In order to study the deformation characteristics and instability mechanism of roadway-side support body, based on the roadway–side support materials of gob-side entry retaining, the dynamic expansion test of back–filling concrete cracks under uniaxial compression was carried out. The YOLOv5 algorithm was applied to establish the fine identification and quantitative characterization method of macroscopic cracks of the samples, and the dynamic expansion rule of roadway-side support body cracks and its dimensional effect were revealed by combining the fractal theory. The results show that the F1 value and average precision mean of the intelligent dynamic crack identification model reached 75% and 71%, respectively, the GIoU loss value tends to fit around 0.038, and the model reached the overall optimal solution. During the uniaxial compression process, micro cracks on the surface of the back–filling concrete first initiated at the end, and after reaching the yield stress, the macroscopic cracks developed significantly. Moreover, several secondary cracks expanded, pooled, and connected from the middle of the specimen to the two ends, inducing the overall instability of the specimen. The surface crack expansion rate, density, and fractal dimension all show stage change characteristics with the increase in stress, and the main crack expansion rate has obvious precursor characteristics. With the increase in the size, the decrease in crack density after back–filling concrete failures gradually decreases from 93.19% to 4.08%, the surface crack network develops from complex to simple, and the failure mode transits from tensile failure to shear failure. The above research results provide a basic experimental basis for design optimization and instability prediction of a roadway–side support body for engineering-scale applications.

Our study focuses on the dynamic transient analysis of arched beam bridges over rivers, which face unique geohazards and challenges, including vibrations and dynamic loads that can affect structural integrity. The finite element software ANSYS v. 19.3 was employed to assess acceleration time histories at various bridge positions. Using MATLAB, we conducted wavelet packet decomposition to extract insights from the data, specifically isolating river-induced influences. In this article, the introduction of the wavelet packet rate index (WPERI) is presented as a novel metric for the detection of cracks in the curved bridge segments over rivers. The WPERI proves reliable in accounting for the river environment’s impact on structural integrity. Our findings highlight the sensitivity and precision of the WPERI in accurately detecting cracks and vulnerabilities in these riverine bridges. By combining WPT, finite element analysis, and signal decomposition, our research offers insights into tailored crack detection methods for riverine bridges. This study underscores the potential of WPT as a tool for identifying and characterizing cracks in curved bridge elements over rivers. The innovative WPERI provides a holistic approach to addressing structural issues, thus enhancing bridge durability amid changing environmental conditions. It contributes significantly to structural engineering and paves the way for the further exploration of river-specific crack detection techniques.

In recent years, the aging of power plants and other infrastructure facilities has become an issue. Therefore, a health-monitoring technology has attracted attention in order to ensure the integrity of structures. An inverse analysis is conducted to estimate the size and position of cracks in this technology. The accuracy of the inverse analysis is dependent on the form of the objective function and the optimization calculation method. Generally, optimization calculation methods using the gradient of the objective function is adopted. However, it is known to have the drawback that the search solution strongly depends on the initial value setting. The biomimetics-based cuckoo optimization algorithm (COA) is a method that can overcome this drawback. However, there are few examples of estimating the crack size and position in structures using the COA. In this study, an inverse analysis method is developed to estimate the crack angle and position of circumferential through-wall crack in pipes. In this study, transfer matrix method (TMM) was utilized to numerically calculate the deformation and rotation angle of the pipe, which are required in the inverse analysis. For this purpose, the fundamental theory of the TMM was extended to the element with a through-wall crack. Consequently, it was confirmed that the crack angle, position and magnitude of the applied shear load can be accurately estimated by the developed method.