Journal articles on the topic 'Crack location detection'
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1
Kim, Byeong-Cheol, and Byung-Jik Son. "Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms." Applied Sciences 13, no. 16 (August 14, 2023): 9238. http://dx.doi.org/10.3390/app13169238.
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Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety.
2
Nwosu, D. I., A. S. J. Swamidas, and J. Y. Guigne´. "Dynamic Response of Tubular T-Joints Under the Influence of Propagating Cracks." Journal of Offshore Mechanics and Arctic Engineering 118, no. 1 (February 1, 1996): 71–78. http://dx.doi.org/10.1115/1.2828804.
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This paper presents an analytical study on the vibration response of tubular T-joints for detecting the existence of cracks along their intersections. The ABAQUS finite element program was utilized for carrying out the analysis. Frequency response functions were obtained for a joint with and without cracks. The joint was modeled with 8-node degenerate shell elements having 5 degrees of freedom per node. Line spring elements were used to model the crack. The exact crack configuration (semielliptical shape, Fig. 5(b)), as observed from numerous experimental fatigue crack investigations at the critical location, has been achieved through a mapping function, that allows a crack in a planar element to be mapped on to the tube surface. The natural frequency changes with respect to crack depth show little changes, being 4.82 percent for a 83-percent crack depth for the first mode. On the other hand, significant changes have been observed for bending moment and curvature as a function of crack depth. For an 83-percent chord thickness crack, a 97-percent change in bending moment at points around the crack vicinity, and 34.15 to 78 percent change in bending moments, for those locations far away from the crack location, have been observed. Natural frequency change should be combined with other modal parameters such as “bending moment (or bending strain)” and “curvature” changes for crack detection. The presence of the crack can be detected at locations far away from the crack location using such sensors as strain gages.
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Wang, Fei, Xue Zeng Zhao, and Jia Ying Chen. "Detection of Multiple Cracks in Triangular Cantilevers Based on Frequency Measurements." Key Engineering Materials 324-325 (November 2006): 259–62. http://dx.doi.org/10.4028/www.scientific.net/kem.324-325.259.
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Triangular cantilevers are used as small force sensors. Prediction of location and size of multiple cracks from experimental results will be of value to users and designers of cantilever deflection force sensors. We extend a method for prediction of location and size of multiple cracks in rectangular cantilevers to deal with triangular cantilevers in this paper. The cracks are assumed to introduce local flexibility change and are modeled as rotational springs. The beam is divided into a number of segments, and each segment is associated with a damage index, which can be calculated through the relationship between the damage index and strain energy of each segment and the changes in the frequencies caused by the cracks. The location of cracks can be obtained with high accuracy with sufficient segment numbers. The size of a crack can be calculated through the relationship between the crack size and its stiffness, which can be obtained from the damage index related to the crack. The maximum error in prediction of the crack position in the case of double cracks is less than 15%, and it is less than 25% in prediction of the crack size.
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Khalkar, V., and S. Ramachandran. "The effect of crack geometry on non-destructive fault detection of EN 8 and EN 47 cracked cantilever beam." Noise & Vibration Worldwide 50, no. 3 (March 2019): 92–100. http://dx.doi.org/10.1177/0957456519834537.
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Since long it has been observed that the size of the crack in structures increases with time, and finally, it may lead to its catastrophic failure. Hence, it is crucial to do the vibration study of cracked structures with regard to vibration-based crack detection and the classification of cracks. So far, vibration-based non-destructive testing method is applied to many spring steel cracked cantilever beams for its possible crack detection. However, the effect of various kinds of practical cracks, that is, V-shaped and U-shaped, on the applicability of these methods has been overlooked. To investigate this issue, artificially cracks are made on the cantilever beam. By free vibration analysis, the effect of crack geometry, crack depth, and crack location on natural frequency is investigated. The natural frequency results obtained from V-shaped and U-shaped models for the same crack configurations are compared with each other and it is revealed that the results are not much sensitive for the change of crack geometry. Hence, it is clear that free vibration-based crack detection method approximately predicts the crack parameters, that is, crack location and crack depth, in structures irrespective of the crack geometry. It is also found that for the same configuration, results of natural frequency are comparatively on the lower side for U-shaped crack models than V-shaped crack models. In this study, the natural frequency of each cracked case is computed by a theoretical method and numerical method and shows good agreement. Finally, it is also observed that structural integrity of a cracked cantilever beam is a function of crack location.
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Zhao, Yiming, Jing Yan, Yanxin Wang, Qianzhen Jing, and Tingliang Liu. "Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology." Entropy 23, no. 4 (April 20, 2021): 486. http://dx.doi.org/10.3390/e23040486.
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A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.
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Yuan, Yingtao, Zhendong Ge, Xin Su, Xiang Guo, Tao Suo, Yan Liu, and Qifeng Yu. "Crack Length Measurement Using Convolutional Neural Networks and Image Processing." Sensors 21, no. 17 (September 1, 2021): 5894. http://dx.doi.org/10.3390/s21175894.
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Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. Furthermore, crack length could be measured with submillimeter accuracy.
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Sethi, Rabinarayan, S. K. Senapati, and Dayal R. Parhi. "Structural Damage Detection by Fuzzy Logic Technique." Applied Mechanics and Materials 592-594 (July 2014): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.1175.
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In this paper, a novel approach for detecting crack location and its intensity in cantilever beam by Fuzzy logic techniques is established. The analysis has been done by using ANSYS FE software. The fuzzy controller with Bell shaped membership functions are used here which consists of three input parameters are relative deviation of first three natural frequencies and two output parameters are relative crack depth and relative crack location respectively. A series of fuzzy rules are resulting from vibration parameters which are finally used for prediction of crack location and its intensity. This method provides the knowledge towards the detection, location and characterization of the damage in the cantilever beam.
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Xu, Guoyan, Xu Han, Yuwei Zhang, and Chunyan Wu. "Dam Crack Image Detection Model on Feature Enhancement and Attention Mechanism." Water 15, no. 1 (December 25, 2022): 64. http://dx.doi.org/10.3390/w15010064.
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Dam crack detection can effectively avoid safety accidents of dams. To solve the problem that the dam crack image samples are not available and the traditional algorithm detects cracks with low accuracy, we provide a dam crack image detection model based on crack feature enhancement and attention mechanism. Firstly, we expand the dam crack image dataset through a generative adversarial network based on crack feature enhancement (Cracks Enhancements GAN, CE-GAN). It can fully expand the dam crack data samples and improve the quality of the training data. Secondly, we propose a crack image detection model based on the attention mechanism (Attention-based Faster-RCNN, AF-RCNN). The attention mechanism is added in the crack detection module to give different weights to the proposal boxes around the crack target and fuse the candidate boxes with high weights to accurately detect the crack target location. The experimental results show that our algorithm achieves 81.07% mAP on the expanded dam crack dataset, which is 8.39% higher than the original Faster-RCNN algorithm. The detection accuracy is significantly improved compared with other traditional dam crack detection algorithm models.
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Ahn, Ju-Hun, Yong-Chan Lee, Se-Min Jeong, Han-Na Kim, and Chang-Yull Lee. "Crack Detecting Method Based on Grid-Type Sensing Networks Using Electrical Signals." Sensors 23, no. 13 (July 2, 2023): 6093. http://dx.doi.org/10.3390/s23136093.
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Cracks have a primary effect on the failure of a structure. Therefore, the development of crack sensors with high accuracy and resolution and cracks detection method are important. In this study, the crack sensors were fabricated, and the crack locations were detected with the electrical signal of the crack sensor. First, a metal grid-type micro-crack sensor based on silver was fabricated. The sensor is made with electrohydrodynamics (EHD) inkjet printing technology, which is well known as the next generation of printed electronics technology. Optimal printing conditions were established through experiments, and a grid sensor was obtained. After that, single cracks and multiple cracks were simulated on the sensor, and electrical signals generated from the sensor were measured. The measured electrical signal tracked the location of the cracks in three steps: simple cross-calculation, interpolation, and modified P-SPICE. It was confirmed that cracks could be effectively found and displayed using the method presented in this paper.
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Fang, Zhi Hua, and Xiang Yang Liu. "Research on Recognition Methods of Crack Damage from Beam Based on the Vibration Modal." Applied Mechanics and Materials 578-579 (July 2014): 1024–27. http://dx.doi.org/10.4028/www.scientific.net/amm.578-579.1024.
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Structural crack damage will degrade its carrying capacity, and affect the security of the structure. Thus early detection of crack damage is a guarantee of the structure safety. Cracks can change the vibration characteristics of the structure, therefore we proposed a method of identifying the crack damage based on the vibration modal. Take both ends fixed beam as an example, through establish the finite element models of crack-free beam and the crack beam with different location and different depth, we calculate the displacement modal parameters of beam before and after the damage, analyze the variation law of displacement modal horizontal component of change and displacement modal Axial displacement difference rate of change varies with crack depth and location, results show that the variation of displacement modal horizontal component and the change rate of displacement modal Axial displacement difference along crack direction are sensitive to cracks location and depth, these can be used as a basis for identification of beam’s crack damage.
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Wu, Wei Liang, Wen Zhong Qu, and Li Xiao. "Closed Crack Detection with Nonlinear Instantaneous Baseline." Key Engineering Materials 577-578 (September 2013): 633–36. http://dx.doi.org/10.4028/www.scientific.net/kem.577-578.633.
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Closed cracks, which stay in contact unless the excitation exceeds a certain threshold, exist as great menace to structures. Since nonlinear response is more sensitive to micro damage than conventional linear approaches, analyzing the nonlinear part of the collected response of structures to an input ultrasonic excitation is more promising in damage detection. In this paper, in order to image the location of a closed crack, an instantaneous baseline measurement is adopted and the nonlinear response is extracted by using scaling subtraction method. A three-dimensional finite element model of a plate with a closed crack is developed and the behavior of the closed crack is simulated with nonlinear springs at the crack interfaces. A network of actuators and sensors which constitutes of two arrays of surface-bonded piezoelectric transducers is built. The instantaneous baselines of each path are collected when the model is excited with low amplitude excitation. To diagnose the closed crack, a higher amplitude excitation over the threshold is applied to the model and the response signals of each path are recorded. The result shows that the differences caused by the crack can be observed from the scaling subtraction of these two recorded responses and the location of closed crack can be accurately imaged.
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Yuan, Hangming, Tao Jin, and Xiaowei Ye. "Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection." Applied Sciences 13, no. 14 (July 18, 2023): 8281. http://dx.doi.org/10.3390/app13148281.
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The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works.
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Khalkar, V., and S. Ramachandran. "The effect of crack geometry on stiffness of spring steel cantilever beam." Journal of Low Frequency Noise, Vibration and Active Control 37, no. 4 (April 4, 2018): 762–73. http://dx.doi.org/10.1177/1461348418765959.
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The survival of the crack in structures always keeps the structure away from performing well in applications due to significant changes in its dynamic response. It has been observed that in service the size of the crack in structures increases with time and finally it leads to its catastrophic failure. Hence it is crucial to do the vibration study of cracked beams in regard of free vibration-based crack detection and its crack classification. Until now the vibration-based nondestructive testing methods are applied to many spring steel cracked cantilever beams for its possible crack detection. However, the effect of various kinds of practical cracks, i.e. V-shaped, U-shaped and rectangular-shaped open cracks, on the applicability of these methods has been overlooked. In order to investigate this issue, artificially cracks are made on the cantilever beam. By free vibration analysis, the effect of crack geometry, crack depth, and crack location on the beam stiffness is investigated. In this study, the stiffness of each cracked case is computed by the deflection methods and vibration methods to ensure the strong validation. The stiffness results obtained from V-shaped, U-shaped and rectangular-shaped crack models for the same configuration are compared with each other and it is found that the results of the stiffness are comparatively more sensitive to U-shaped crack models. Through vibration study, it is found that spring steel structures are slightly sensitive to the change in crack geometries as long as the vibration characteristics are concerned. Hence, it is obvious that free vibration-based crack detection method can satisfactorily predict the location and depth of the crack in any spring steel structures irrespective of the crack geometries. Apart from this, it is also found that for the same configurations, EN 8 and EN 47 cracked cantilever beams give the identical structural integrity or structural stability property for all the cracked cases. Lastly, it is also found that as the crack depth increases by keeping the crack location constant, the stiffness of the beam decreases.
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Li, Guangjun, Lin Nan, Lu Zhang, Manman Feng, Yan Liu, and Xu Meng. "Research on Infrared Image Fusion Technology Based on Road Crack Detection." Journal of World Architecture 7, no. 3 (June 28, 2023): 21–26. http://dx.doi.org/10.26689/jwa.v7i3.4826.
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This study aimed to propose road crack detection method based on infrared image fusion technology. By analyzing the characteristics of road crack images, this method uses a variety of infrared image fusion methods to process different types of images. The use of this method allows the detection of road cracks, which not only reduces the professional requirements for inspectors, but also improves the accuracy of road crack detection. Based on infrared image processing technology, on the basis of in-depth analysis of infrared image features, a road crack detection method is proposed, which can accurately identify the road crack location, direction, length, and other characteristic information. Experiments showed that this method has a good effect, and can meet the requirement of road crack detection.
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An, Qing, Xijiang Chen, Xiaoyan Du, Jiewen Yang, Shusen Wu, and Ya Ban. "Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning." Complexity 2021 (August 9, 2021): 1–15. http://dx.doi.org/10.1155/2021/3159968.
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For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.
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Song, Xiangbo. "Automatic bridge crack detection device based on quadrotor UAV." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012004. http://dx.doi.org/10.1088/1742-6596/2031/1/012004.
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Abstract For the bridge located in deep valley, with high tower and long span structure, the traditional artificial crack detection can not meet the needs of today. In order to solve this problem, a new bridge crack detection equipment is designed based on quadrocopter unmanned aerial vehicle. According to this equipment, the developed system contains: reconnaissance unmanned aerial vehicle system, acquisition and transmission system, crack positioning system, and graphics operations system. Reconnaissance UAV system is improved based on quadrocoper UAV to meet the requirements of flight power, flight control, flight safety and signal stability. In the integrated transmission system, the designed UAV equipped with Hawkeye 5S motion camera, and 4G communication is used to complete the real-time image transmission; The crack location system uses BDS / GPS dual-mode combined single-point positioning module to realize the accurate location of bridge surface cracks; Graphics operations system, based on C++ and python language, designed a superpotent actual-time video streaming graphics operations method, and used median filter, image grayscale, histogram equalization, threshold segmentation and other methods to process the crack image. The designed bridge crack detection equipment can effectively solve the problem of low crack detection efficiency, and realize the intelligence and automation of crack identification. It has practical engineering application value and promotes the development of bridge crack detection technology.
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Zohra, Fatema Tuz, Omar Salim, Hossein Masoumi, Nemai C. Karmakar, and Shuvashis Dey. "Health Monitoring of Conveyor Belt Using UHF RFID and Multi-Class Neural Networks." Electronics 11, no. 22 (November 15, 2022): 3737. http://dx.doi.org/10.3390/electronics11223737.
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Conveyor belts in mining sites are prone to cracks, which leads to dramatic degradation of overall system performance and the breakdown of operation. Crack detection using radio frequency identification (RFID) sensing technology is recently proposed to provide robust and low-cost health monitoring systems for conveyor belts. The intelligent machine learning (ML) technique is one of the most promising solutions for crack detection and successful implementation within the IoT paradigm. This paper presents a conveyor belt structural health monitoring (SHM) model using ML and Internet of Things (IoT) connectivity. The model is extensively tested, and the classification is conducted based on simulated data obtained from an Ultra High Frequency (UHF) RFID sensor. Here, the sensor is laid on a belt, and the data are obtained at different crack orientations of vertical, horizontal, and diagonal cracks, for varying crack widths of 0.5 to 5 mm at 10 different locations on the sensor. The ML model is tested with different input features and training algorithms, and their performances are compared and analysed to identify the superior input feature and training algorithm. This method produces high accuracy in determining crack width, orientation, and location. The findings show that the proposed detection system based on ML modelling could detect cracks with 100% accuracy. The proposed system can also distinguish between vertical, horizontal, and diagonal cracks with an accuracy of 83.9%, and has a significant identification rate of 84.4% accuracy for detecting crack-width as narrow as 0.5 mm. Moreover, the model can predict the region of the crack with an accuracy of 95.5%. Overall, the results show that the proposed model is very robust and can perform SHM of conveyor belts with high accuracy for a range of parameters and classification scenarios. The method has huge industrial significance in coal mines.
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Gui, Xin, Zhengying Li, Xuelei Fu, Changjia Wang, Yiming Wang, Hongli Li, and Honghai Wang. "High-Density Distributed Crack Tip Sensing System Using Dense Ultra-Short FBG Sensors." Sensors 19, no. 7 (April 10, 2019): 1702. http://dx.doi.org/10.3390/s19071702.
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Crack generation starts at the crack tip, which bears the highest stress concentration. Under further stress, the crack propagates and leads to severe structural damage. To avoid such damage, the identification of the crack tips, and monitoring of the surrounding stress and strain fields, are very important. In this work, the location of, and strain distribution monitoring around, the crack tip are achieved using a dense ultra-short (DUS) fiber Bragg grating (FBG) array together with an improved optical frequency domain reflectometry (OFDR) interrogator. The adjacent grating interference correlation algorithm helps overcome the limitation on the demodulation precision, which is imposed by the inherently broad reflection spectra of individual ultra-short gratings. High spatial resolution measurement of the strain profile around the crack tip is performed at different levels of induced strain. Furthermore, the vertical-crossed layout is adopted to avoid the omission of cracks, which usually occurs in the case of the one direction layout. We achieve 1 mm spatial resolution and 7.5 m detection distance. Location of a single crack, multiple cracks, and an oblique crack was realized experimentally by locating the crack tips. The experimental results are consistent with the theoretical analysis, verifying the feasibility of the DUS-FBG system for high-density distributed crack tip sensing.
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Mayekar, Vijay. "Crack Detection by Ultrasonic System on Locomotive-Track." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1910–16. http://dx.doi.org/10.22214/ijraset.2021.37681.
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Abstract: It has been found that major cause of the Railways accidents is due to the derailment attributed to the cracks developed on the rails. Crack development and its prolongation happens due to regular movement of locomotives, absence of timely detection. The maintenance lapse poses serious questions on the security of Railways transport operation. Manual detection of cracks on locomotive-tracks by maintenance team is arduous task, time consuming, irregular due to environmental factors and tight scheduling of trains, which may lead to maintenance work errors. This project aims at addressing the issue of crack detection by ultrasonic crack detection system, which would work in real time, with the control action taken to avoid major accident. Ultrasonic crack detection system is mounted on the locomotive itself, include precision ultrasonic sensor, LCD display, GPS module and its power supply. System will detect crack, subsequently it will generate Visual warning-signal for the authorities and generate location coordinates of the Crack. This will help for authority to generate caution signal for locomotives running on the same track for slow movement and emergency maintenance action could be undertaken. Keywords: locomotive-track, crack, ultrasonic Sensor, GPS Module, Arduino microcontroller
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Sayyad, FB, B. Kumar, and SA Khan. "Approximate analytical method for damage detection in free–free beam by measurement of axial vibrations." International Journal of Damage Mechanics 22, no. 1 (March 27, 2012): 133–42. http://dx.doi.org/10.1177/1056789512440897.
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Nowadays, sophisticated structures and machinery parts are constructed by using metallic beams. Beams are widely used as structural element in civil, mechanical, naval, and aeronautical engineering. In structures and machinery, one undesirable phenomenon is crack initiation in which the impact cannot be seen overnight. Cracks develop gradually through time that lead finally to catastrophic failure. Therefore, crack should be monitored regularly with more care. This will lead to more effective preventive measure and ensure continuous operation of the structure and machine. Damage in structure alters its dynamic characteristics. The change is characterized by change in modal parameters, that is, modal frequencies. Thus, vibration technique can be suitably used as a nondestructive test for crack detection of component to be tested. Mostly modal frequencies are used for monitoring the crack because modal frequencies are properties of the whole structure component. In this paper, efforts are made to develop suitable methods that can serve as the basis to detection of crack location and crack size from measured axial vibration data. This method is used to address the inverse problem of assessing the crack location and crack size in various beam structure. The method is based on measurement of axial natural frequencies, which are global parameter and can be easily measured from any point on the structure and also indeed, the advantage in modeling complexity. In theoretical analysis, the relationship between the natural frequencies, crack location, and crack size has been developed. For identification of crack location and crack size, it was shown that data on the variation of the first two natural frequencies is sufficient. The experimental analysis is done to verify the practical applicability of the theoretical method developed.
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Yu, Lingjun, and Qi Li. "Deep Learning based Pavement Crack Detection System." Journal of Physics: Conference Series 2560, no. 1 (August 1, 2023): 012045. http://dx.doi.org/10.1088/1742-6596/2560/1/012045.
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Abstract The pavement crack causes the highway service life to shorten, the safety hidden danger to increase. The low efficiency and high cost of manual inspection makes it difficult to detect pavement cracks. This paper proposes a fast and efficient deep learning pavement crack detection system. CRACK2000, an image segmentation dataset with complex interference background and multiple crack types, is constructed based on perspective transformation and image cropping. The scheme corrects the pavement crack images by perspective transformation. The extraction of pavement crack depth features is completed by applying the U-Net network. Finally, the pavement condition index PCI (pavement condition index) is calculated by quantifying the different types of crack information based on the segmentation results. The experimental results show that the Precision, Recall, F1-score and AUC of the U-Net network are 76.67%, 72.32%, 74.43% and 99.46% respectively. The AUC values reflect that the method is more capable of filtering out complex background interference from cracked images. The automatic pavement crack detection system designed in this paper can accurately locate and classify the location and category of pavement cracks, and perform quantitative pavement evaluation to obtain the pavement deterioration of the road section and the corresponding repair recommendations, enhancing the practicality of pavement crack detection.
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Sahu, Sasmita, Priyadarshi Biplab Kumar, and Dayal R. Parhi. "A hybridised CSAGA method for damage detection in structural elements." Mechanics & Industry 19, no. 4 (2018): 407. http://dx.doi.org/10.1051/meca/2018023.
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In recent years, significant developments have been noticed in nondestructive techniques for damage detection in cracked structures. Some of the proposed methods can be used to find out the existence of the crack; other methods locate and simultaneously find out the damage severity. In the current investigation, a novel hybridised method is proposed for damage detection in structural elements. The proposed method can be used to investigate both location and nature of damage in structures within a reasonable time limit. The problem in the current analysis requires a set of dynamic parameters that depend on the dynamics of the cracked structure due to the presence of the crack. In the present study, the first three natural frequencies of a structure are considered as the inputs to find out the damage location. A finite element method is used to generate the first three natural frequencies of a cracked cantilever beam with multiple cracks. A method hybridizing the nature-inspired artificial intelligence techniques has been implemented for crack detection. Here, clonal selection algorithm and genetic algorithm have been integrated to design the framework of the hybrid technique. The changes in the natural frequencies are given as inputs to the hybrid technique and the output from the technique is the locations of damage.
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Makris, Ruben, Falk Hille, Marc Thiele, Dirk Kirschberger, and Damian Sowietzki. "Crack luminescence as an innovative method for detection of fatigue damage." Journal of Sensors and Sensor Systems 7, no. 1 (April 10, 2018): 259–66. http://dx.doi.org/10.5194/jsss-7-259-2018.
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Abstract. Conventional non-destructive testing methods for crack detection provide just a snapshot of fatigue crack evolution at a specific location in the moment of examination. The crack luminescence coating realizes a clear visibility of the entire crack formation. The coating consists of two layers with different properties and functions. The bottom layer emits light as fluorescence under UV radiation. The top layer covers the fluorescing one and prevents the emitting of light in case of no damage at the surface. Several different experiments show that due to the sensitive coating even the early stage of crack formation can be detected. That makes crack luminescence helpful for investigating the incipient crack opening behavior. Cracks can be detected and observed during operation of a structure, making it also very interesting for continuous monitoring. Crack luminescence is a passive method and no skilled professionals are necessary to detect cracks, as for conventional methods. The luminescent light is clearly noticeable by unaided eye observations and also by standard camera equipment, which makes automated crack detection possible as well. It is expected that crack luminescence can reduce costs and time for preventive maintenance and inspection.
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Ding, Weihua, Lin Zhu, Hu Li, Man Lei, Fan Yang, Junrong Qin, and Aiguo Li. "Relationship between Concrete Hole Shape and Meso-Crack Evolution Based on Stereology Theory and CT Scan under Compression." Materials 15, no. 16 (August 16, 2022): 5640. http://dx.doi.org/10.3390/ma15165640.
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To achieve more accurate prediction of the potential failure location and to conduct a deeper analysis of the failure mechanism of concrete constructions, it is critical to probe the evolution process of internal meso-cracks that bear various intensities of load. While a computer Tomography (CT) test provides a non-destructive detection technique for obtaining the internal meso-damage state of concrete, traditional image processing and Digital Image Correlation (DIC) are ineffective in extracting meso-damage information from concrete CT images. On the other hand, by observing the shape change law of concrete’s internal holes under load, it is proposed to use the hole roundness and area fraction formula, developed based on the stereology principle and morphology, to characterize and predict the potential failure location. Four features particularly addressed include the CT image as a whole, image equal partitioning, crack and non-crack areas, and representative holes. The approach is to explore the variation law of critical hole shape parameters, especially the hole roundness under different loading stages, and analyze the relationship between the change in hole shapes and the final macro-crack positions. It is found that compared with the average area fraction, the average hole roundness value of cross section images is more sensitive to the change in stress. In both uniform partitioning and non-uniform partitioning, the average hole roundness value near the final macro-crack location exhibits an increase trend with the stress, while the smoothing effect caused by the hole roundness averaging always exists. Near the final macro-crack location, the roundness of each individual hole is positively associated with the stress, while away from the final macro-crack location such a relation may not be observed. This trend expounds the evolution process of meso-damage in concrete, and the finding can be used to predict the accurate locations of macro-cracks.
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Ntakpe, Jean Louis, Gilbert Rainer Gillich, Florian Muntean, Zeno Iosif Praisach, and Peter Lorenz. "Vibration-Based Crack Detection in L-Frames." Applied Mechanics and Materials 658 (October 2014): 261–68. http://dx.doi.org/10.4028/www.scientific.net/amm.658.261.
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This paper presents a novel non-destructive method to locate and size damages in frame structures, performed by examining and interpreting changes in measured vibration response. The method bases on a relation, prior contrived by the authors, between the strain energy distribution in the structure for the transversal vibration modes and the modal changes (in terms of natural frequencies) due to damage. Using this relation a damage location indicator DLI was derived, which permits to locate cracks in spatial structures. In this paper an L-frame is considered for proving the applicability of this method. First the mathematical expressions for the modes shapes and their derivatives were determined and simulation result compared with that obtained by finite element analysis. Afterwards patterns characterizing damage locations were derived and compared with measurement results on the real structure; the DLI permitted accurate localization of any crack placed in the two structural elements.
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Feng, Chuncheng, Hua Zhang, Haoran Wang, Shuang Wang, and Yonglong Li. "Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network." Sensors 20, no. 7 (April 7, 2020): 2069. http://dx.doi.org/10.3390/s20072069.
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Crack detection on dam surfaces is an important task for safe inspection of hydropower stations. More and more object detection methods based on deep learning are being applied to crack detection. However, most of the methods can only achieve the classification and rough location of cracks. Pixel-level crack detection can provide more intuitive and accurate detection results for dam health assessment. To realize pixel-level crack detection, a method of crack detection on dam surface (CDDS) using deep convolution network is proposed. First, we use an unmanned aerial vehicle (UAV) to collect dam surface images along a predetermined trajectory. Second, raw images are cropped. Then crack regions are manually labelled on cropped images to create the crack dataset, and the architecture of CDDS network is designed. Finally, the CDDS network is trained, validated and tested using the crack dataset. To validate the performance of the CDDS network, the predicted results are compared with ResNet152-based, SegNet, UNet and fully convolutional network (FCN). In terms of crack segmentation, the recall, precision, F-measure and IoU are 80.45%, 80.31%, 79.16%, and 66.76%. The results on test dataset show that the CDDS network has better performance for crack detection of dam surfaces.
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Barat, Vera, Artem Marchenkov, Sergey Ushanov, Vladimir Bardakov, and Sergey Elizarov. "Investigation of Acoustic Emission of Cracks in Rails under Loading Close to Operational." Applied Sciences 12, no. 22 (November 17, 2022): 11670. http://dx.doi.org/10.3390/app122211670.
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The paper is devoted to the study of the possibility of detecting cracks in railway rails by the acoustic emission (AE) method. An experimental study of AE signals under cyclic compression loading of rail fragments, which simulates the rail operating load, has been carried out. Fragments of rails without defects, as well as fragments containing pre-grown fatigue cracks, were studied. It was found that AE signals generated by a rail with a crack have higher activity compared to signals from defect-free specimens. It is shown that the AE signals during the loading of defect-free specimens have a short duration and low amplitude and may be caused by the deformation of non-metallic inclusions. The crack presence leads to an increase in the AE hits rate and changes the nature of the distribution of the AE hits amplitudes. It is shown that the crack location has no effect on the reliability of its detection by the AE method. Criteria of crack detection by AE testing are offered as a result of this study.
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Fan, Jin Zhi. "Surface Crack Detection in Building Wall Based on Computer Vision." Applied Mechanics and Materials 651-653 (September 2014): 524–27. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.524.
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the detection technology of surface crack in building wall is studied to improve the accuracy of detection. To detect surface crack in the building wall, there will be a pixel overlap or distorted in the location of image connection if using traditional detection method to make fusion process for the different pixels. due to the accuracy requirements of image pixels in surface crack detection in building wall is relatively high, resulting in too low accuracy rate of surface crack detection in building wall. In order to avoid the above problem, a detection method for surface crack in building wall based on computer vision is proposed. The crack region’s pixel in the image of building wall’s surface is calculated, and thus to provide the basis for surface crack detection in building wall. According to the theory of computer vision, the spatial location of the surface crack region in building wall is obtained. Experiments show that this detection system can improve the accuracy of detection, and achieve satisfactory results.
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Meng, G., and E. J. Hahn. "Dynamic Response of a Cracked Rotor With Some Comments on Crack Detection." Journal of Engineering for Gas Turbines and Power 119, no. 2 (April 1, 1997): 447–55. http://dx.doi.org/10.1115/1.2815595.
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By considering time-dependent terms as external excitation forces, the approximate dynamic response of a cracked horizontal rotor is analyzed theoretically and numerically. The solution is good for small cracks and small vibrations in the stable operating range. For each steady-state harmonic component, the forward and backward whirl amplitudes, the shape and orientation of the elliptic orbit, and the amplitude and phase of the response signals are analyzed, taking into account the effect of crack size, crack location, rotor speed, and unbalance. It is found that the crack causes backward whirl, the amplitude of which increases with the crack. For a cracked rotor, the response orbit for each harmonic component is an ellipse, the shape and orientation of which depend on the crack size. The influence of the crack on the synchronous response of the system can be regarded as an additional unbalance whereupon, depending on the speed and the crack location, the response amplitude differs from that of the uncracked rotor. The nonsynchronous response provides evidence of crack in the subcritical range, but is too small to be detected in the supercritical range. Possibilities for crack detection over the full-speed range include the additional average (the constant) response component, the backward whirl of the response, the ellipticity of the orbit, the angle between the major axis and the vertical axis, and the phase angle difference between vertical and horizontal vibration signals.
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Chen, Keqin, Amit Yadav, Asif Khan, Yixin Meng, and Kun Zhu. "Improved Crack Detection and Recognition Based on Convolutional Neural Network." Modelling and Simulation in Engineering 2019 (October 14, 2019): 1–8. http://dx.doi.org/10.1155/2019/8796743.
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Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.
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Park, Philip, Yong Hak Huh, Dong Jin Kim, and Byung Jik Son. "Crack Detection by Static Measurement in Steel Beams." Key Engineering Materials 321-323 (October 2006): 394–99. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.394.
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Effects of crack on static behavior in steel box girder were investigated to identify the possibility of crack detection and location using conventionally measured static data including deflection and inclination. Finite element analysis with finely meshed 3-D models and experiments were performed for steel box girder with crack. To eliminate the variation according to load magnitude, compliance technique was applied to analysis of the result. Through this study, the quantitative relationship between crack size and structural responses of deflection and inclination were obtained as a form of relation-curve. This relation-curve can be utilized to evaluate crack size in the steel box girder bridge. The results also demonstrated that the location of crack can be estimated by static measurement.
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Li, Shengli, Chaoqun Wang, Panxu Sun, Guangming Wu, and Dongwei Wang. "A localization method for concealed cracks in the road base based on ground penetrating radar." Advances in Mechanical Engineering 8, no. 12 (December 2016): 168781401668315. http://dx.doi.org/10.1177/1687814016683154.
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Phenomenon of concealed cracks is one of the familiar failures in the road base. Accurate concealed crack localization of the road base using ground penetrating radar still suffers from some limitations. To accurately locate the concealed cracks in the road base, an effective concealed crack localization method based on ground penetrating radar is proposed in this article: first, continuous ground penetrating radar scanning and manual point-by-point detection are performed to determine the location of the concealed crack roughly; then the ground penetrating radar–based ellipse method and an improved data processor based on MATLAB are used for processing the data to determine the accurate location of the concealed crack. The proposed method is experimentally validated using a rectangle concrete block with a concealed crack, and the results of the proposed method are compared with that of the previous method. All results indicate that the proposed method can be used to locate concealed cracks fast and well; moreover, the localization accuracy of the proposed method is obviously higher than the previous method; all these lay a good foundation for engineering applications.
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Ashwini, Kosanam, Sasmita Sahu, Bijaya Bijeta Nayak, and Sudesna Roy. "Damage detection in structural elements: using adaptive Mamdani model." E3S Web of Conferences 391 (2023): 01165. http://dx.doi.org/10.1051/e3sconf/202339101165.
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In real life all the structural and machine elements work under dynamic or variable loading. Application of dynamic loading leads to fluctuating stress. Due to fluctuating stress fatigue cracks initiates. These fatigue cracks are the main reason of failures. So, it is very important to detect the crack and predict the crack life. There are different types of damages but crack is one of the most encountered damage. There are different conventional methods to detect the damage but these methods are time taking and requires removal from the machines. Therefore, researchers are giving more importance to the unconventional methods to find the damage. In the present work a method has been introduced to find the damage site using Fuzzy Logic System and Regression Analysis. In particular, this paper focuses on applying statistical process control methods. A data pool has been created from the dynamic analysis of the cracked cantilever beam and then the data pool is trained in the proposed methodology to find the crack location. It has been noticed that the proposed methodology gives result within the tolerable range.
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Tapeinos, Christos I., Maria D. Kamitsou, Konstantinos G. Dassios, Dimitris Kouzoudis, Aggeliki Christogerou, and Georgios Samourgkanidis. "Contactless and Vibration-Based Damage Detection in Rectangular Cement Beams Using Magnetoelastic Ribbon Sensors." Sensors 23, no. 12 (June 9, 2023): 5453. http://dx.doi.org/10.3390/s23125453.
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This study investigated the innovative use of magnetoelastic sensors to detect the formation of single cracks in cement beams under bending vibrations. The detection method involved monitoring changes in the bending mode spectrum when a crack was introduced. The sensors, functioning as strain sensors, were placed on the beams, and their signals were detected non-invasively using a nearby detection coil. The beams were simply supported, and mechanical impulse excitation was applied. The recorded spectra displayed three distinct peaks representing different bending modes. The sensitivity for crack detection was determined to be a 24% change in the sensing signal for every 1% decrease in beam volume due to the crack. Factors influencing the spectra were investigated, including pre-annealing of the sensors, which improved the detection signal. The choice of beam support material was also explored, revealing that steel yielded better results than wood. Overall, the experiments demonstrated that magnetoelastic sensors enabled the detection of small cracks and provided qualitative information about their location.
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Gavrilov, A. A., G. I. Grebenyuk, V. I. Maksak, and N. A. Morozov. "Crack modeling of metal rod eigen-frequencies." Vestnik Tomskogo gosudarstvennogo arkhitekturno-stroitel'nogo universiteta. JOURNAL of Construction and Architecture 23, no. 2 (April 30, 2021): 56–64. http://dx.doi.org/10.31675/1607-1859-2021-23-2-56-64.
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The paper presents the development of approaches to the crack detection in metal rod structures based on the analysis of the lowest eigen-frequency modes. Full-scale experiments and numerical calculations are carried out, and the obtained results are compared. A vibration analyzer is used for full-scale experiments, and numerical calculations are performed by using Autodesk Inventor. With regard to the internal friction, the antinodes of various vibration forms were identified using a specially developed program. The model includes sensors for the the field experiment as masses affecting the frequency-response characteristics. The dependences are obtained for eigen-frequencies in the presence of cracks and for the crack locations. The polynomial dependences of the crack location on the lowest eigen-frequency modes of the rod can be used to analyze the crack position of in cantilever beams.
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Zhang, Qinghua, and Ziming Xiong. "Crack Detection of Reinforced Concrete Structures Based on BOFDA and FBG Sensors." Shock and Vibration 2018 (September 3, 2018): 1–10. http://dx.doi.org/10.1155/2018/6563537.
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Reinforced concrete structural elements, as an important component of buildings and structures, require inspection for the purposes of crack detection which is an important part of structural health monitoring. Now existing crack detection methods usually use a single technology and can only detect internal or external cracks. In this paper, the authors propose a new sensing system combining BOFDA (Brillouin optical frequency-domain analysis) and FBG (fiber Bragg grating) technology, which are used to detect internal and surface cracks and their development in reinforced concrete structures, and an attempt is made to estimate the width of surface cracks. In these experiments, a special reinforced concrete beam structure was designed by the author for crack detection under load. Four continuous distributed optical fibers are fixed on the steel skeleton, which is located within the reinforced concrete beam. Three FBG sensors are fixed on the lower surface of the beam, near its centre. By analysing the sensor data, it can be found that the BOFDA-distributed fiber can be used to detect internal cracking before surface cracking, and the difference between scans can be used to judge the time of onset of internal cracking, but the relative error in position is about 5%, while the FBG sensor can detect the cracking time of microcracks on the lower surface in near-real-time and can be used to calculate the crack width. Through the experiment, it is found that if the combination of BOFDA and FBG technology is adopted, we can initially use the strain data obtained by multiple groups of BOFDA monitoring to predict the general location of the internal cracks, then to monitor the exact location of the surface cracks by FBG in the medium term, and to estimate the width of the final expansion of the cracks finally.
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Wang, Fei, and Xue Zeng Zhao. "Nondestructive Detection of a Crack in a Triangular Cantilever Beam Based on Frequency Measurement." Key Engineering Materials 353-358 (September 2007): 2285–88. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2285.
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Triangular cantilevers are usually used as small force sensors in the transverse direction. Analyzing the effect of a crack on transverse vibration of a triangular cantilever will be of value to users and designers of cantilever deflection force sensors. We present a method for prediction of location and size of a crack in a triangular cantilever beam based on measurement of the natural frequencies in this paper. The crack is modeled as a rotational spring. The beam is treated as two triangular beams connected by a rotational spring at the crack location. Formulae for representing the relation between natural frequencies and the crack details are presented. To detect crack details from experiment results, the plots of the crack stiffness versus its location for any three natural modes can be obtained through the relation equation, and the point of intersection of the three curves gives the crack location. The crack size is then calculated using the relation between its stiffness and size. An example to demonstrate the validity and accuracy of the method is presented.
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Brockman, R. A., R. John, and M. A. Huelsman. "Using deformation modes to identify cracks in turbine engine compressor disks." Aeronautical Journal 113, no. 1150 (December 2009): 811–19. http://dx.doi.org/10.1017/s0001924000003468.
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Abstract Recent studies show that analytical predictions of crack growth in rotating components can be used in conjunction with displacement measurement techniques to identify critical levels of fatigue damage. However, investigations of this type traditionally have focused on the detection of damage at known flaw locations. This paper addresses the related problem of estimating damage associated with flaws at unknown locations, through the combined use of analytical models and measured vibration signatures. Because the measured data are insufficient to identify a unique solution for the location and severity of fatigue cracks, the function of the analytical model is to bound the extent of damage occurring at life-limiting locations. The prediction of remaining life based on estimates of worst-case fatigue damage and crack locations also is discussed.
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Winklberger, Markus, Christoph Kralovec, Christoph Humer, Peter Heftberger, and Martin Schagerl. "Crack Identification in Necked Double Shear Lugs by Means of the Electro-Mechanical Impedance Method." Sensors 21, no. 1 (December 23, 2020): 44. http://dx.doi.org/10.3390/s21010044.
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This contribution investigates fatigue crack detection, localization and quantification in idealized necked double shear lugs using piezoelectric transducers attached to the lug shaft and analyzed by the electro-mechanical impedance (EMI) method. The considered idealized necked lug sample has a simplified geometry and does not includes the typical bearing. Numerical simulations with coupled-field finite element (FE) models are used to study the frequency response behavior of necked lugs. These numerical analyses include both pristine and cracked lug models. Through-cracks are located at 90∘ and 145∘ to the lug axis, which are critical spots for damage initiation. The results of FE simulations with a crack location at 90∘ are validated with experiments using an impedance analyzer and a scanning laser Doppler vibrometer. For both experiments, the lug specimen is excited and measured using a piezoelectric active wafer sensor in a frequency range of 1 kHz to 100 kHz. The dynamic response of both numerical calculations and experimental measurements show good agreement. To identify (i.e., detect, locate, and quantify) cracks in necked lugs a two-step analysis is performed. In the first step, a crack is detected data-based by calculating damage metrics between pristine and damaged state frequency spectra and comparing the resulting values to a pre-defined threshold. In the second step the location and size of the detected crack is identified by evaluation of specific resonance frequency shifts of the necked lug. Both the search for frequencies sensitive to through-cracks that allow a distinction between the two critical locations and the evaluation of the crack size are model-based. This two-step analysis based on the EMI method is demonstrated experimentally at the considered idealized necked lug, and thus, represents a promising way to reliably detect, locate and quantify fatigue cracks at critical locations of real necked double shear lugs.
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Yoon, Dong Jin, Sang Il Lee, Jaehwa Kwon, and Young Sup Lee. "Characteristics of Patch Type Smart-Piezo-Sensor for Smart Structures." Key Engineering Materials 297-300 (November 2005): 2010–15. http://dx.doi.org/10.4028/www.scientific.net/kem.297-300.2010.
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Structural health monitoring (SHM) is a new technology that has been increasingly evaluated by the industry as a potential approach to improve the cost and ease of structural inspection. Piezoelectric smart active layer (SAL) sensor was fabricated to verify the applicability of finding cracks and conducting source location in a various materials. A crack detection and source location works were done in three kinds of test condition such as aluminum plates with crack for patch type SAL sensor, a smart airplane with embedding SAL sensor, and a concrete beam with real crack for practical application. From this experimental study, the evaluation algorithm for the arrival time delay and decrease of signal amplitude was suggested in this paper. Consequently, it was found that the SAL sensor and detection algorithm developed in this study can be effectively used to detect and monitor damages in the both existing structures and new designed smart structures.
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Park, So Soon, Seok Hwan Ahn, Chang Kwon Moon, and Ki Woo Nam. "Fatigue Crack Propagation Behavior and Degradation Characteristics of STS316L by Nondestructive Evaluation." Key Engineering Materials 297-300 (November 2005): 2016–21. http://dx.doi.org/10.4028/www.scientific.net/kem.297-300.2016.
Full textAbstract:
Structural health monitoring (SHM) is a new technology that has been increasingly evaluated by the industry as a potential approach to improve the cost and ease of structural inspection. Piezoelectric smart active layer (SAL) sensor was fabricated to verify the applicability of finding cracks and conducting source location in a various materials. A crack detection and source location works were done in three kinds of test condition such as aluminum plates with crack for patch type SAL sensor, a smart airplane with embedding SAL sensor, and a concrete beam with real crack for practical application. From this experimental study, the evaluation algorithm for the arrival time delay and decrease of signal amplitude was suggested in this paper. Consequently, it was found that the SAL sensor and detection algorithm developed in this study can be effectively used to detect and monitor damages in the both existing structures and new designed smart structures.
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Ranjan, Rajeev. "Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System." International Journal of Manufacturing, Materials, and Mechanical Engineering 6, no. 4 (October 2016): 1–10. http://dx.doi.org/10.4018/ijmmme.2016100101.
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The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.
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Dias Gueiral, Nuno Eduardo, Elisabete Maria da Silva Marques Nogueira, and Antonio Manuel de Amaral Monteiro Ramos. "Crack Detection by Wavelet-Based Acoustic Emission Test In Vitro Cemented Implant." Materials Science Forum 638-642 (January 2010): 558–63. http://dx.doi.org/10.4028/www.scientific.net/msf.638-642.558.
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One the mechanisms of failure in total hip arthroplasty in cemented prosthesis is cement fatigue. The main objective of this work is to use Acoustic Emission (AE) as a non-destructive and non-intrusive monitoring test in a cemented prosthesis. The femoral component was sinusoidally loading in a fatigue machine. Experimental data collected during acoustic emission test was treated and analysed by Wavelet Transform and allowed to locate a crack in cement mantle of femoral component. Other complementary diagnostic tests were used to confirm the existence of a fault (crack). One of them was penetrating liquids in different cut sections of femoral component. The other one was microscopic analysis that allowed observing the existence of a crack which location is pointed out by the results of AE answer. The AE sources locations are situated inside the crack observed in the optical microscope. The Wavelet Transform (WT) AE signals demonstrated the accuracy of damage location in bone cement and thus becoming useful in other orthopedics studies.
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M N, Sumaiya, Prajwal K, Rao Shravan Vasudev, Shreya K A, Thrishul R, and R. Manjunath Prasad. "Comparative Analysis of Concrete Crack Detection using Image Processing and Artificial Intelligence." Journal of Image Processing and Artificial Intelligence 9, no. 1 (January 11, 2023): 8–15. http://dx.doi.org/10.46610/joipai.2023.v09i01.002.
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Cracks in concrete structures can be formed due to many reasons such as physical damage, hydraulic shrinkage, thermal shrinkage, swelling, and corrosion of steel reinforcements. These vulnerable entities present in structures are responsible for reducing the performance and strength of the concrete. Inspecting these entities and deciding the nature of these cracks is an essential element for the maintenance of the structure. Concrete crack detection based on Image Processing and Artificial Intelligence involves using computer vision techniques to automatically identify and classify cracks in concrete structures. Detection and classification of these cracks can be done using a combination of various machine learning algorithms and image processing techniques. Our project aims to detect the location of the cracks on the structures using various processing techniques such as resizing, gray scaling, binarization, segmentation, enhancement, and filtering out the noise. Detection of the cracks can be performed using thresholding techniques. Later classification of these detected cracks can be done using Deep Learning techniques concerning various deciding parameters such as length, width, and area of cross-section of the detected crack. The cracks can be classified based on their size they can be thin, medium, or wide cracks. They can also be classified based on the appearance of the crack, which may be longitudinal, vertical or transversal.
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Sun, Zhaoyun, Junzhi Zhai, Lili Pei, Wei Li, and Kaiyue Zhao. "Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion." Sensors 23, no. 7 (April 6, 2023): 3772. http://dx.doi.org/10.3390/s23073772.
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To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm.
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Liu, Xuekun, Shixi Yang, Yongqiang Liu, Yongwei Chi, and Xiwen Gu. "Surface Crack Identification on a Cylinder Using the Signal Enhancement of the Scanning Laser Line Source Method." Applied Sciences 8, no. 10 (October 1, 2018): 1796. http://dx.doi.org/10.3390/app8101796.
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Cylindrical structures play an important role in industrial fields. The surface crack is a typical defect in the cylindrical structures. Non-destructive surface crack detection of these structures is critical to the safe operation of the equipment. In this study, the signal enhancement of the scanning laser line source (SLLS) method is investigated by a numerical simulation method to identify the location and depth of the surface crack in the aluminum cylinder. A fully coupled explicit finite element model is established to study the signal enhancement of cylindrical surface waves on the aluminum cylinder. The simulation results indicate that the signal enhancement of the SLLS is more sensitive to the surface crack of a cylinder than that of the scanning laser detection (SLD) because of the wider span and higher peak. Due to the phase shift characteristics of surface waves on the cylinder, the maximum peak-to-peak amplitude of signal enhancement in the SLLS method (the SLLS peak) is affected by the detection position and diameter of the cylinder. Therefore, an optimization approach for detection position in SLLS is proposed for the location of surface crack on the cylinder. The locations of the surface crack on the solid cylinders with different diameters are investigated using simulated laser ultrasonic field data. Moreover, we find that the SLLS peak for signal enhancement can effectively respond to the crack depth within a limited scope which is dependent on the directivity pattern of the longitudinal waves.
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Tewelde, Samrawit A., and Marek Krawczuk. "Nonlinear Vibration Analysis of Beam and Plate with Closed Crack: A Review." Acta Mechanica et Automatica 16, no. 3 (September 1, 2022): 274–85. http://dx.doi.org/10.2478/ama-2022-0033.
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Abstract The effect of nonlinearity is high sensitivity in damage detection, especially for closed cracks and delamination. This review illustrates the results of several researchers dealing with nonlinear effects caused by the closure of cracks in the structure, i.e., beam and plate structures. Early detection of damage is an important aspect for the structure and, therefore, continuous progress is being made in developing new and effective methods that use nonlinear effects for early detection of damage and barely visible cracks, i.e., closed cracks and delamination, as well as for the determination of crack size and location. After analysing various methods, the merits, drawbacks and prospects of a number of nonlinear vibration methods for structural damage detection are discussed, and recommendations are made for future researchers.
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LEE, T. Y., and T. Y. KAM. "DETECTION OF CRACK LOCATION VIA A GLOBAL MINIMIZATION APPROACH." Engineering Optimization 21, no. 2 (July 1993): 147–59. http://dx.doi.org/10.1080/03052159308940972.
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Wang, Linlin, Junjie Li, and Fei Kang. "Crack Location and Degree Detection Method Based on YOLOX Model." Applied Sciences 12, no. 24 (December 8, 2022): 12572. http://dx.doi.org/10.3390/app122412572.
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Damage detection and evaluation are concerns in structural health monitoring. Traditional damage detection techniques are inefficient because of the need for damage detection before evaluation. To address these problems, a novel crack location and degree detector based on YOLOX is proposed, which directly realizes damage detection and evaluation. Moreover, the detector presents a superior detection effect and speed to other advanced deep learning models. Additionally, rather than at the pixel level, the detection results are determined in actual scales according to resolution. The results demonstrate that the proposed model can detect and evaluate damage accurately and automatically.
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Wang, Quan, Zhijie Zhang, Wuliang Yin, Haoze Chen, and Yushan Liu. "Defect Detection Method for CFRP Based on Line Laser Thermography." Micromachines 13, no. 4 (April 13, 2022): 612. http://dx.doi.org/10.3390/mi13040612.
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A continuous line laser scanning inspection technique for tracing load-bearing structures was developed and applied to defect detection of unidirectional carbon-fiber-reinforced polymers for aero engines. The heat transfer model of the material was analyzed using the finite element software COMSOL. Meanwhile, a laser platform was built and an image algorithm was used to verify the feasibility of the method. The potential of this technique for detecting defects and providing information on the location of defects in carbon fiber composites was analyzed. Results indicate line laser thermal imaging can successfully determine the size, location, and crack angle of surface damage with extremely high accuracy. The positioning accuracy error for impact and fracture defects is less than 20%, and the detection rate can reach 100% if the defect is in the special position of just leaving the heating area. The angle detection of fracture cracks can be accurate within 10°.