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1
Raihan, Prodhan Md Safiq, Anik Md Shahjahan, Shamima Akter Shimky, Toki Thamid Zim, Summa Parven, Abdul Ali Khan, and Mir Fazle Rabbi. "Pavement Crack Detection and Solution with Artificial Intelligence." European Journal of Theoretical and Applied Sciences 2, no. 4 (July 1, 2024): 277–314. http://dx.doi.org/10.59324/ejtas.2024.2(4).25.
Повний текст джерелаАнотація:
Detecting and repairing pavement cracks is essential to ensure road safety and longevity. Traditional inspection and maintenance methods are time-consuming, expensive and often inaccurate. In recent years, there has been a growing trend to use artificial intelligence (AI) to automate the process of pavement crack detection and repair. The article focuses on using AI techniques to detect pavement cracks and provide solutions to repair them. The proposed solution is based on using deep learning algorithms to analyze high-resolution images of the road surface. Photos are taken with a vehicle camera or a drone. The deep learning algorithm is trained using a large data set of tagged sidewalk crack images. Once trained, the algorithm can accurately detect and classify the type of cracks on the pavement surface, including longitudinal, transverse, block and crocodile cracks. The algorithm can also determine the severity of each crack and help prioritize repairs. When cracks are detected, the AI system can make recommendations for repair solutions. This includes identifying the appropriate caulk or filler material to use depending on the type and severity of the crack. The AI system can also recommend the most efficient and cost-effective repair method, such as B. Crack sealing, crack filling or deep repair. Overall, using AI to detect and repair cracks in sidewalks offers a more accurate, efficient, and cost-effective solution to keep roads safe and sustainable. By automating the inspection and repair process, this technology can help prevent accidents, reduce maintenance costs, and improve overall road safety.
2
Kuttimarks, Dr M. S. "Crack Detection of Structures using Artificial Intelligence System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1894–901. http://dx.doi.org/10.22214/ijraset.2024.61958.
Повний текст джерелаАнотація:
Abstract: Structural Health Monitoring (SHM) having wider scope of operations such as structural audit, rehabilitation of structures and along with it a major scope of crack detection is also included. Effective crack detection is vital for ensuring the safety and longevity of civil engineering structures. Crack Detection in existing methods is tedious process, it requires costly equipment’s as well as time consuming, and inaccuracy in mechanical equipment’s. The technological advancements especially in the field of Artificial Intelligence (AI) maybe an acceptable solution for crack identification through mobile app using Image processing techniques. The mobile app can be used to accurately identify crack pattern and measure the size of cracks in structures (concrete and steel). The app needed to capture and process data from diverse sources, such as images, in real time. It allows for real-time monitoring of structures and ensures safety and integrity of it. The image processing algorithm is used for to accurately identifying various types of cracks in different lighting, environmental conditions, effective and optimized solution. This project explained about real-time data processing for crack detection and its challenges. This demanded robust backend infrastructure and efficient data processing techniques to deliver prompt and accurate results. User Interface and Experience developing an intuitive and user-friendly interface for the app while maintaining high performance.
3
Wang, Zi Zhen, Ri He Wang, Yu Huan Bu, and Xun Shan. "A New Method of Preparing Artificial Cores with Certain Cracks for Experiment Study of Elastic Wave Propagation." Advanced Materials Research 356-360 (October 2011): 2954–57. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.2954.
Повний текст джерелаАнотація:
Crack universally existing underground is an important kind of pores. In order to study the elastic wave propagation in fractured medium through experiment, a new method to make artificial core with certain cracks using oil well cement and camphor sheet or thin steel sheet is put forward. Geometric parameters of the crack, such as shape, size and aspect ratio, are approximately equal to that of camphor sheet or thin steel sheet. Using the thin steel sheet to make crack can be more easy and accurate to control the crack angle than using camphor sheet. The crack opening scales at millimeters. The aspect ratio of cracks formed by camphor sheet ranges from 1.4 to 8, and aspect ratio of cracks formed by thin steel sheet ranges from 2.5 to 70. This method is proved simple and feasible by experiment practice, which can provide artificial cores with certain crack for acoustic wave propagation study.
4
Wu, Zhenkai, Xizhe Li, Hanmin Xiao, Xuewei Liu, Wei Lin, Yuan Rao, Yang Li, and Jie Zhang. "The Establishment and Evaluation Method of Artificial Microcracks in Rocks." Energies 14, no. 10 (May 12, 2021): 2780. http://dx.doi.org/10.3390/en14102780.
Повний текст джерелаАнотація:
It is necessary to carry out experiments on cores with different degrees of crack development when studying the seepage law of cracked reservoirs and evaluating cracks. The seepage experiment in the laboratory requires cores with different degrees of microcrack development; cores obtained via conventional drilling cannot meet the requirements, and the efficacies and evaluation methods of geological parameters used for artificial cracks are not perfect. In this study, cores are loaded using a triaxial gripper, and cracks are produced by changing the difference of stress; the relationship between the increased rate of permeability and the change in stress concentration is used to evaluate the degree of development of the crack in real time. The angle between the cracks and the maximum principal stress direction, calculated using the Mohr–Coulomb failure criterion, is 20–27.5°, which provides theoretical support for the process of crack creation. The experimental results show that the permeability variation curve shows two obvious turning points, which divide the whole zone into a reduction zone, a slow increase zone, and a rapid increase zone. Through the obtained experimental and evaluation results, a complete system for crack creation and evaluation is established, which can provide strong support for the study of cracked reservoirs.
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Sakamoto, Junji, Yoshimasa Takahashi, and Hiroshi Noguchi. "Small Fatigue Crack Growth Behavior from Artificial Notch with Focused Ion Beam in Annealed 0.45% Carbon Steel." Key Engineering Materials 488-489 (September 2011): 319–22. http://dx.doi.org/10.4028/www.scientific.net/kem.488-489.319.
Повний текст джерелаАнотація:
The aim of this study is firstly to investigate the applicability of a sharp notch with Focused Ion Beam (FIB) as a crack for fatigue limit evaluation. Secondly we investigate a condition in which artificial defects (drilled hole, FIB notch) can be used as a crack for fatigue limit evaluation. To achieve the aim, the growth behaviors and the non-propagating crack sizes of small fatigue cracks initiated from a FIB notch and a drilled hole are carefully compared with those of an annealed fatigue crack which imitates an ideally sharp crack. The results show that a FIB notch can be used as a crack for fatigue limit evaluation under some conditions. The results also show that the condition which controls the applicability of an artificial defect as an ideal crack for fatigue limit evaluation is strongly dependent on the relation between (i) the length of a non-propagating fatigue crack and (ii) the crack length when the small fatigue crack growth behaviors from an artificial defect and an ideal pre-crack become almost the same. It is found that the length of (ii) can be obtained by the analyses using the number of cycles from a certain crack length to failure.
6
Fathalla, Eissa, Yasushi Tanaka, Koichi Maekawa, and Akito Sakurai. "Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks." Applied Sciences 8, no. 7 (July 21, 2018): 1197. http://dx.doi.org/10.3390/app8071197.
Повний текст джерелаАнотація:
By integrating a multi-scale simulation with the pseudo-cracking method, the remaining fatigue life of in-service reinforced concrete (RC) bridge decks can be estimated based upon their site-inspected crack patterns. But, it still takes time for computation. In order to achieve a quick deterioration-magnitude assessment of RC decks based upon their crack patterns, two evaluation methods are proposed. A predictive correlation between the remaining fatigue life and the cracks density (both cracks length and width) is presented as a fast judgment. For fair-detailed judgment, an artificial neural network (ANN) model is also introduced which is the basis of the machine learning. Both assessment methods are built commonly by thousands of artificial random crack patterns to cover all possible ranges since the variety of the real crack patterns on site is more or less limited. The built ANN performances are examined by k-fold cross-validation besides checking the prediction accuracy of real crack patterns of bridge RC decks. Finally, the hazard map of the deck’s bottom surface is introduced to indicate the location of higher risk cracking, which derives from the estimated weight of individual neuron in the built artificial neural network.
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Hendroprasetyo, Wing, and Henry Haidar Jati Andrian. "Analysis of Eddy Current Testing Detection Ability to the Varied Longitudinal Cracks on Coated Weld Metal Tee Joint of 5083 Aluminum Ship Structure." IOP Conference Series: Earth and Environmental Science 972, no. 1 (January 1, 2022): 012041. http://dx.doi.org/10.1088/1755-1315/972/1/012041.
Повний текст джерелаАнотація:
Abstract Due to its service, crack sometimes occur on the coated 5083 aluminum weld metal. One method for finding crack under protective coating is by using eddy current testing (ECT). This method of nondestructive testing relies on a circular electrical path generated by the coils positioned just above the surface being examined. The purpose of this research is to analyze the sensitivity of ECT by using variety of crack dimensions on 5083 aluminum welded plate tee joint. Five test pieces, each 200 mm × 50 mm × 10 mm were used and each sample contain four cracks. Artificial cracks are fabricated using electrical discharge machining (EDM) with length and depth variations. Variations in crack length are 5.0 mm, 7.0 mm, 9.0 mm, and 15.0 mm, and variation in depth are 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm and 2.5 mm. Results obtained by the analysing height and average length of the crack signal indication are as follows: for 0.5 mm crack depth, the height of the crack signal is 5 mm; 1.0 mm depth is 11.75 mm; 1.5 mm depth is 19.75 mm; 2.0 mm depth is 26.5 mm and 2.5 mm depth is 31.25 mm. The degree of accuracy obtained for the detection of crack depth is 93.57%. For the detection of crack length, it is found that the measured average length of artificial cracks is 5 mm actual crack length has measured crack length of 6.32 mm; 7 mm actual crack length has measured crack length value of 7.18 mm; 9 mm actual crack length has a measured crack length value of 8.8 mm and 15 mm actual crack length has measured crack length of 14.48 mm. The degree of accuracy obtained for crack length detection is 91.31%.
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Kim, Jung Jin, Ah-Ram Kim, and Seong-Won Lee. "Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures." Applied Sciences 10, no. 22 (November 16, 2020): 8105. http://dx.doi.org/10.3390/app10228105.
Повний текст джерелаАнотація:
The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement.
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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.
Повний текст джерелаАнотація:
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, Xichen, Jie Chen, Siyi Lu, Miaomiao Liu, Siyu Chen, Yifei Nan, Yang Wang, and Jun Feng. "Ureolytic MICP-Based Self-Healing Mortar under Artificial Seawater Incubation." Sustainability 13, no. 9 (April 25, 2021): 4834. http://dx.doi.org/10.3390/su13094834.
Повний текст джерелаАнотація:
Ureolytic microbial-induced calcium carbonate precipitation (MICP) is a promising green technique for addressing sustainable building concerns by promoting self-healing mortar development. This paper deals with bacteria-based self-healing mortar under artificial seawater incubation for the sake of fast crack sealing with sufficient calcium resource supply. The ureolytic MICP mechanism was explored by morphology characterization and compositional analysis. With polyvinyl alcohol fiber reinforcement, self-healing mortar beams were produced and bent to generate 0.4 mm width cracks at the bottom. The crack-sealing capacity was evaluated at an age of 7 days, 14 days, and 28 days, suggesting a 1-week and 2-week healing time for 7-day- and 14-day-old samples. However, the 28-day-old ones failed to heal the cracks completely. The precipitation crystals filling the crack gap were identified as mainly vaterite with cell imprints. Moreover, fiber surface was found to be adhered by bacterial precipitates indicating fiber–matrix interfacial bond repair.
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Zhang, Yue, Xuemin Zhang, Yun Su, Xuan Li, Shiwei Ma, Su Zhang, Weihe Ren, and Kang Li. "Tunnel Lining Crack Detection Method Based on Polarization 3D Imaging." Photonics 10, no. 10 (September 27, 2023): 1085. http://dx.doi.org/10.3390/photonics10101085.
Повний текст джерелаАнотація:
Non-contact and non-destructive polarization 3D imaging uses a passive, single-frame array image to calculate 3D information, making it possible to obtain high-precision 3D information about tunnel cracks, and offering outstanding technical advantages. Based on the introduction of the principle of crack detection with polarization 3D imaging, a tunnel lining crack detection plan was developed and a detection equipment was designed. The method and process of polarization 3D imaging for lining crack detection are described in detail. A model of the impact of the tunnel environment on 3D detection and a method for obtaining absolute information have been established to obtain high-precision 3D information about cracks. In a real tunnel environment, tests were conducted to detect wide cracks, narrow cracks, and artificial cracks. The crack detection accuracy with respect to the crack width was 0.2–0.3 mm, and with respect to crack length was 0.2–0.3 mm. At the same time, crack depth information could be obtained. The present research results can provide technical support for the application of polarization 3D imaging in tunnel crack detection.
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Rifdah Mufiidah Harahap, Darlina Tanjung, M Husni Malik Hasibuan, and Marwan Lubis. "Analisis Deteksi Kedalaman Retak Pada Beton Mengunakan Metode UPV Testing." Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik Sipil 2, no. 2 (March 22, 2024): 112–20. http://dx.doi.org/10.61132/konstruksi.v2i2.236.
Повний текст джерелаАнотація:
Non-destructive tests are currently widely used in evaluating the quality of concrete installed in the field. One NDT is using the Ultrasonic Pulse Velocity (UPV) method. In general, the use of UPV in concrete is to estimate concrete strength, determine the homogeneity of concrete and detect concrete damage, for example the presence of voids or cracks. This research is intended to determine the accuracy of UPV test results in detecting concrete cracks at several variations in crack depth. In this research, the UPV tool validation process was carried out by making 1 type of test object. Namely 1 beam specimen for testing crack depth (UPV) with 6 variations of artificial cracks where the artificial cracks are made using plywood. Each sample has been treated so that it can describe the crack depth according to the existing plan. In testing, it was found that the estimated crack depth in variation I was 33.2 mm deep, variation II was 18.8 mm deep, variation III was 104.4 mm deep, variation IV was 115.8 mm deep, variation V was 24.4 mm deep and variation VI 159.3 mm deep. And the average accuracy is 13.60%. The smaller the wave travel time, the smaller the crack depth, the smaller the wave emission and the longer the wave path to detect cracks. which means that the accuracy of UPV crack depth testing tends to decrease with the unevenness of the concrete surface.
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Jiang, Sheng, Mansour Sharafisafa, and Luming Shen. "Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates." Materials 14, no. 11 (June 3, 2021): 3042. http://dx.doi.org/10.3390/ma14113042.
Повний текст джерелаАнотація:
Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.
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Yang, Gang, Jianchao Wu, and Qing Hu. "Rapid detection of building cracks based on image processing technology with double square artificial marks." Advances in Structural Engineering 22, no. 5 (November 1, 2018): 1186–93. http://dx.doi.org/10.1177/1369433218810183.
Повний текст джерелаАнотація:
In order to measure the crack width of dangerous buildings quickly and accurately, this article presents a new crack width measurement method, which is based on image processing technology, using double square artificial markers to identify building cracks and calculate crack width. It makes two 10 mm × 10 mm black square artificial marks and places them near the sides of the crack. Then it uses a camera to collect crack images and transfer photos to a computer. The crack image is subjected to image graying, binarization, denoising, image segmentation, and pixel calibration based on the image processing technique. Finally, the actual length value of the unit pixel is calculated. Then it can calculate the actual width of the crack according to the number of pixels included in the test crack. The calculation results show that the accuracy of the proposed method is 98.56% compared with the measured data. The calculation method can accurately and effectively detect the crack width of dangerous buildings and improve work efficiency. At the same time, it can avoid long hours of work in dangerous operating environments.
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Shehata, Hesham M., Yasser S. Mohamed, Mohamed Abdellatif, and Taher H. Awad. "Crack Width Estimation Using Feed and Cascade Forward Back Propagation Artificial Neural Networks." Key Engineering Materials 786 (October 2018): 293–301. http://dx.doi.org/10.4028/www.scientific.net/kem.786.293.
Повний текст джерелаАнотація:
Automatic crack inspection techniques that limit the necessity of human have the potential to lower the cost and time of the process. In this study, a maximum crack width estimation approach is presented. Seventy nine segments of cracks are used for training the neural networks and twenty six segments are used for examination. The maximum width for each segment is measured using laser scanning microscope and segment image is captured and magnified using the microscope camera in order to obtain the extracted crack profile number of pixels. Feed and cascade forward back propagation artificial neural networks are designed and constructed. The input and output for the networks are the crack width in terms of number of pixels and the maximum estimated crack width respectively. It is shown that, the artificial neural networks technique can effectively be used to estimate the crack width. The feedforward back propagation structure which is designed with two layers and training function TRAINLM gives the best results in examination.
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Hu, Guo X., Bao L. Hu, Zhong Yang, Li Huang, and Ping Li. "Pavement Crack Detection Method Based on Deep Learning Models." Wireless Communications and Mobile Computing 2021 (May 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/5573590.
Повний текст джерелаАнотація:
Severe weather and long-term driving of vehicles lead to various cracks on asphalt pavement. If these cracks cannot be found and repaired in time, it will have a negative impact on the safe driving of vehicles. Traditional artificial detection has some problems, such as low efficiency and missing detection. The detection model based on machine learning needs artificial design of pavement crack characteristics. According to the pavement distress identification manual proposed by the Federal Highway Administration (FHWA), these categories have three different types of cracks, such as fatigue, longitudinal crack, and transverse cracks. In the face of many types of pavement cracks, it is difficult to design a general feature extraction model to extract pavement crack features, which leads to the poor effect of the automatic detection model based on machine learning. Object detection based on the deep learning model has achieved good results in many fields. As a result, those models have become possible for pavement crack detection. This paper discusses the latest YOLOv5 series detection model for pavement crack detection and is to find out an effective training and detection method. Firstly, the 3001 asphalt crack pavement images with the original size of 2976 × 3978 pixels are collected using a digital camera and are randomly divided into three types according to the severity levels of low, medium, and high. Then, for the dataset of crack pavement, YOLOv5 series models are used for training and testing. The experimental results show that the detection accuracy of the YOLOv5l model is the highest, reaching 88.1%, and the detection time of the YOLOv5s model is the shortest, only 11.1 ms for each image.
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Cui, Zhendong, and Weige Han. "In SituScanning Electron Microscope (SEM) Observations of Damage and Crack Growth of Shale." Microscopy and Microanalysis 24, no. 2 (April 2018): 107–15. http://dx.doi.org/10.1017/s1431927618000211.
Повний текст джерелаАнотація:
AbstractTo better understand the formation and evolution of hierarchical crack networks in shales, observations of microscopic damage, and crack growth were conducted using anin situtensile apparatus inside a scanning electron microscope. An arched specimen with an artificial notch incised into the curved edge was shown to afford effective observation of the damage and crack growth process that occurs during the brittle fracturing of shale. Because this arched specimen design can induce a squeezing effect, reducing the tensile stress concentration at the crack tip, and preventing the brittle shale from unstable fracturing to some extent. Both induced and natural pores and cracks were observed at different scales around the main crack path or on fractured surfaces. Observations indicate that the crack initiation zone develops around the crack tip where tensile stresses are concentrated and micro/nanoscale cracks nucleate. Crack advancement generally occurs by the continuous generation and coalescence of damage zones having intermittent en echelon microscopic cracks located ahead of the crack tips. Mineral anisotropy and pressure build-up around crack tips causes crack kinking, deflection, and branching. Crack growth is often accompanied by the cessation or closure of former branch cracks due to elastic recovery and induced compressive stress. The branching and interactions of cracks form a three-dimensional hierarchical network that includes induced branch cracks having similar paths, as well as natural structures such as nanopores, bedding planes, and microscopic cracks.
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Liu, Yifan, Weiliang Gao, Tingting Zhao, Zhiyong Wang, and Zhihua Wang. "A Rapid Bridge Crack Detection Method Based on Deep Learning." Applied Sciences 13, no. 17 (August 31, 2023): 9878. http://dx.doi.org/10.3390/app13179878.
Повний текст джерелаАнотація:
The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision.
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Zheng, Mu Lin, Zhang Wei Ling, Min Wang, Shuai Kong, and Wei Can Guo. "The Experimental Research on Horizontal Underground Tank Magnetic Flux Leakage Testing." Applied Mechanics and Materials 752-753 (April 2015): 1236–39. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1236.
Повний текст джерелаАнотація:
Magnetic flux leakage (MFL) testing is widely used to inspect and characterize defects in storage tank floors, pipelines and other structures. In this paper, magnetic flux leakage testing technology is applied to the horizontal product oil underground tank wall inspection. The artificial defects were prefabricated on the tank wall such as corrosion pits, grooving and other artificial defects to simulate the corrosion, cracks and other actual defects in actual working conditions. The experimental research of the mutual influence between magnetic flux leakage and defects’ parameters were carried out, such as the depth and width of cracks, and depth and diameter of corrosion pits. Then the relationship between the defect’s parameter and magnetic field amplitude was obtained. The experimental results showed that, whether corrosion or crack, the depth is a very important factor affecting the leakage magnetic field amplitude. Especially for crack, width, length and the inclination angle between crack and magnetic field had great influence on the detection of the cracks, and too small angle to lead to misjudgment and undetected of crack. Therefore, single direction inspection may lead to undetected in engineering practice.
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Zhu, Yantao, and Hongwu Tang. "Automatic Damage Detection and Diagnosis for Hydraulic Structures Using Drones and Artificial Intelligence Techniques." Remote Sensing 15, no. 3 (January 20, 2023): 615. http://dx.doi.org/10.3390/rs15030615.
Повний текст джерелаАнотація:
Large-volume hydraulic concrete structures, such as concrete dams, often suffer from damage due to the influence of alternating loads and material aging during the service process. The occurrence and further expansion of cracks will affect the integrity, impermeability, and durability of the dam concrete. Therefore, monitoring the changing status of cracks in hydraulic concrete structures is very important for the health service of hydraulic engineering. This study combines computer vision and artificial intelligence methods to propose an automatic damage detection and diagnosis method for hydraulic structures. Specifically, to improve the crack feature extraction effect, the Xception backbone network, which has fewer parameters than the ResNet backbone network, is adopted. With the aim of addressing the problem of premature loss of image detail information and small target information of tiny cracks in hydraulic concrete structures, an adaptive attention mechanism image semantic segmentation algorithm based on Deeplab V3+ network architecture is proposed. Crack images collected from concrete structures of different types of hydraulic structures were used to develop crack datasets. The experimental results show that the proposed method can realize high-precision crack identification, and the identification results have been obtained in the test set, achieving 90.537% Intersection over Union (IOU), 91.227% Precision, 91.301% Recall, and 91.264% F1_score. In addition, the proposed method has been verified on different types of cracks in actual hydraulic concrete structures, further illustrating the effectiveness of the method.
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Abdollahzadeh Jamalabadi, Mohammad Yaghoub. "The Use of Artificial Intelligence for Image Processing of Crack Patterns in Panel Painting." Sumerianz Journal of Scientific Research, no. 51 (January 24, 2022): 1–12. http://dx.doi.org/10.47752/sjsr.51.1.12.
Повний текст джерелаАнотація:
In today world the image processing tools serve as a tool to study the various aspects of an image. In this study the purpose is to study the crack patterns in panel painting. First the cracks are extracted from a painting. Then it simplified with a line and a system of lines and the points of cross overs are studied as a model of crack pattern in that painting. Finally, the type of each cross points (X, Y or O), statics of line lengths and orientation, and crack island area are reported. The artificial intelligence is used for estimation of evolution of crack pattern is done based on continuation of end points and separating the big island to the smaller in load direction.
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Wu, Dongling, Hongxiang Zhang, and Yiying Yang. "Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC)." Journal of Advanced Transportation 2022 (June 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/4117957.
Повний текст джерелаАнотація:
In civil engineering, image recognition technology in artificial intelligence is widely used in structural damage detection. Traditional crack monitoring based on concrete images uses image processing, which requires high image preprocessing techniques, and the results of detection are vulnerable to factors, such as lighting and noise. In this study, the full convolutional neural networks FCN-8s, FCN-16s, and FCN-32s are applied to monitoring of concrete apparent cracks and according to the image characteristics of concrete cracks and experimental results. The FCN-8s model was tested with a correct crack monitoring rate of 0.6721, while the new network model had a correct crack detection rate of 0.7585, a significant improvement in the correct crack detection rate.
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Liu, Qi, Shancheng Cao, and Zhiwen Lu. "An Improved Crack Breathing Model and Its Application in Crack Identification for Rotors." Machines 11, no. 5 (May 20, 2023): 569. http://dx.doi.org/10.3390/machines11050569.
Повний текст джерелаАнотація:
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.
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Kim, Jae-Seong, Bo-Young Lee, Woong-Gi Hwang, and Sung-Sik Kang. "The Effect of Welding Residual Stress for Making Artificial Stress Corrosion Crack in the STS 304 Pipe." Advances in Materials Science and Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/932512.
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The stress corrosion crack is one of the fracture phenomena for the major structure components in nuclear power plant. During the operation of a power plant, stress corrosion cracks are initiated and grown especially in dissimilar weldment of primary loop components. In particular, stress corrosion crack usually occurs when the following three factors exist at the same time: susceptible material, corrosive environment, and tensile stress (residual stress included). Thus, residual stress becomes a critical factor for stress corrosion crack when it is difficult to improve the material corrosivity of the components and their environment under operating conditions. In this study, stress corrosion cracks were artificially produced on STS 304 pipe itself by control of welding residual stress. We used the instrumented indentation technique and 3D FEM analysis (using ANSYS 12) to evaluate the residual stress values in the GTAW area. We used the custom-made device for fabricating the stress corrosion crack in the inner STS 304 pipe wall. As the result of both FEM analysis and experiment, the stress corrosion crack was quickly generated and could be reproduced, and it could be controlled by welding residual stress.
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Aman, Alexandra-Teodora, Cristian Tufisi, Gilbert-Rainer Gillich, and Tiberiu Manescu. "Damage detection in variable temperature conditions using artificial intelligence." Vibroengineering Procedia 51 (October 20, 2023): 186–92. http://dx.doi.org/10.21595/vp.2023.23679.
Повний текст джерелаАнотація:
When considering damage detection using the natural frequencies of structures, small frequency drops can indicate either the presence of cracks or a temperature change. This change can lead to additional stress affecting the modal parameters for specific structures, making it much harder to detect, locate, and evaluate damage accurately. The current research aims to describe a method for detecting transverse cracks in beams, considering temperature variations. The considered beam is fixed at both ends, thus inducing axial forces when the temperature is increased. The influence of temperature is considered using adjustment coefficients developed for each vibration mode. This coefficient can be used to accurately calculate the natural frequency for an intact or damaged beam. An analytical method for determining the natural frequencies caused by the changing temperature and the presence of a transverse crack is described and used to generate data for training a feedforward artificial neural network (ANN). The ANN’s capability of determining the position of transverse cracks in double-clamped beams subjected to small temperature changes is proven by creating numerical simulations with known crack positions and thermal conditions for testing the developed method.
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Buffière, Jean Yves, Emilie Ferrié, Wolfgang Ludwig, and Anthony Gravouil. "Characterisation and Modelling of the Three Dimensional Propagation of Short Fatigue Cracks." Materials Science Forum 519-521 (July 2006): 997–1004. http://dx.doi.org/10.4028/www.scientific.net/msf.519-521.997.
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This paper reports recent results on the characterisation and modelling of the three dimensional (3D) propagation of small fatigue cracks using high resolution synchrotron X ray micro-tomography. Three dimensional images of the growth of small fatigue cracks initiated in two Al alloys on natural or artificial defects are shown. Because of the small size of the investigated samples (millimetric size), fatigue cracks grown in conventional Al alloys with a grain size around 100 micrometers can be considered as microstructurally short cracks. A strong interaction of these cracks with the grain boundaries in the bulk of the material is shown, resulting in a tortuous crack path. In ultra fine grain alloys, the crack shapes tend to be more regular and the observed cracks tend to grow like ”microstructurally long cracks” despite having a small physical size. Finite Element meshes of the cracks can be generated from the reconstructed tomographic 3D images. Local values of the stress intensity factor K along the experimental crack fronts are computed using the Extended Finite Element method and correlated with the crack growth rate.
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Li, You Tang, and Huai Qing Li. "Analysis of Stress Singularity near the Tip of Artificial Crack." Key Engineering Materials 525-526 (November 2012): 445–48. http://dx.doi.org/10.4028/www.scientific.net/kem.525-526.445.
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A generalized expression of the stress-singularity function at the tip of artificial crack is proposed, and a formula to calculate the stress intensity factor of artificial crack is obtained in the paper. The solutions of stress singularity of a cracked bi-materials beam under uniform tension and bending were computed. The results show that the degree of stress-singularity is determined by the exponent λ at the tip of artificial crack, and the exponent λ is, not only determined by materials parameter of artificial crack but also by angle. Key words: artificial crack; bi-material; stress singularity; eigen value; stress extrapolation method
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Theocaris, P. S. "Peculiarities of the artificial crack." Engineering Fracture Mechanics 38, no. 1 (January 1991): 37–54. http://dx.doi.org/10.1016/0013-7944(91)90205-f.
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Wu, Yangxu, Wanting Yang, Jinxiao Pan, and Ping Chen. "Asphalt pavement crack detection based on multi-scale full convolutional network." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 1495–508. http://dx.doi.org/10.3233/jifs-191105.
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Pavement crack assessment is an important indicator for evaluating road health. However, due to the dark color of the asphalt pavement and the texture characteristics of the pavement, current asphalt pavement crack detection technology cannot meet the requirements of accuracy and efficiency. In this paper, we propose an end-to-end multi-scale full convolutional neural network to achieve the semantic segmentation of cracks in road images by learning the crack characteristics in the complex fine grain background of asphalt pavement. The method uses DenseNet and deconvolution network framework to achieve pixel-level detection and fuses features learned from different scales of convolutional kernels through a full convolutional network to obtain richer information on multi-scale features, allowing more detailed representation of crack features in high-resolution images. And the back end joins the SVM classifier to achieve crack classification after crack segmentation. Then we create a road test standard data set containing 12 cracks and evaluate it on the data. The experimental results show that the method achieves good segmentation effect for 12 types of cracks, and the crack segmentation for asphalt pavement is better than the most advanced methods.
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Barrarat, F., B. Helifa, I. K. Lefkaier, S. Bensaid, and K. Rayane. "Defect Reconstruction Using Multilayer Perceptron for Regression and Classification Tasks Based On Eddy Current Signatures." Materials Evaluation 82, no. 10 (October 1, 2024): 47–56. http://dx.doi.org/10.32548/2024.me-04439.
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The safety of engineering structures can be significantly compromised by cracks resulting from manufacturing procedures or prior loading, potentially leading to severe and catastrophic industrial accidents. Therefore, it is crucial to accurately and quantitatively characterize cracks in such structures. One common technique for detecting defects in metallic structures is eddy current testing (ECT). This paper proposes a method for the reliable estimation of crack shape and dimensions in conductive materials using the principles of ECT combined with a machine learning algorithm. First, numerical simulations are used to examine the relationship between the detection signature and the crack length and depth. Then, an artificial neural network based on a machine learning technique is employed to inversely characterize the cracks. The predicted results demonstrate that the crack length, depth, and shape can be accurately determined by the proposed algorithm. These findings, obtained using various specimens with known cracks, validate the applicability of the proposed approach for crack characterization.
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Her, Shiuh Chuan, and Sheng Tung Lin. "Characterization of Surface Crack Using Surface Waves." Applied Mechanics and Materials 166-169 (May 2012): 1931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1931.
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Surface cracks are the most common defects in structures. Ultrasonic has been widely used as a non-destructive evaluation technology in the case of crack characterization. In this investigation, surface waves are applied to a steel block with artificial slots to characterize the crack depth. A series of test specimen with different depths of surface crack ranging from 4mm to 10mm was fabricated. The depth of the surface crack was evaluated using the pitch-catch ultrasonic technology. In this work, 2.25 MHz, 5 MHz and 10 MHz of incident waves were employed to investigate the effect of frequency on the crack depth detection. Experimental test results show that the accuracy of crack depth detection is increasing with the increase of frequency.
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Hu, Jue, Weiping Xu, Bin Gao, Gui Tian, Yizhe Wang, Yingchun Wu, Ying Yin, and Juan Chen. "Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System." Metals 8, no. 8 (August 6, 2018): 612. http://dx.doi.org/10.3390/met8080612.
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Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.
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Knorr, Alain Franz, and Michael Marx. "Calculating the Resistance of a Grain Boundary against Fatigue Crack Growth." Advanced Materials Research 891-892 (March 2014): 929–35. http://dx.doi.org/10.4028/www.scientific.net/amr.891-892.929.
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One problem of the quantitative description of small fatigue crack propagation is the fluctuating crack growth rate induced by obstacles like grain or phase boundaries. Sometimes cracks stop completely for a large number of cycles sometimes cracks only decelerate, both resulting in an additional number of life time cycles. However, so far it is not clear, what actually determines the resistance of a grain boundary against fatigue cracks. Therefore we investigate small crack propagation through grain boundaries systematically by in-situ imaging in the scanning electron microscope and focused ion beam (FIB) crack initiation. By this unique technique, artificial stage I cracks with constant crack parameters can be observed while interacting with different grain boundaries which gives detailed information on the interaction mechanisms. We identified different useful aspects of the interaction between microcracks and microstructural barriers on the microscopic scale. 3D-tomographs revealed by serial sectioning and FIB give information about the transition process from the initial grain to the neighbouring one. The resulting purely geometrical consideration leads to a quantitative description of the blocking effect of grain boundaries and can be used to calculate the probability of a crack transfer from the orientation data of two neighboring grains only.
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Gao, Xin Wen, ShuaiQing Li, Bang Yang Jin, Min Hu, and Wei Ding. "Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 4453–69. http://dx.doi.org/10.3233/jifs-201296.
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With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity.
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Abhijeet H. Kekan et al.,, Abhijeet H. Kekan et al ,. "Crack Depth and Crack Location Identification using Artificial Neural Network." International Journal of Mechanical and Production Engineering Research and Development 9, no. 2 (2019): 699–708. http://dx.doi.org/10.24247/ijmperdapr201970.
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Gomera, Mufaro, and Yunus Ballim. "An artificial intelligence approach to detection and assessment of concrete cracks based on visual inspection photographs." MATEC Web of Conferences 364 (2022): 05020. http://dx.doi.org/10.1051/matecconf/202236405020.
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This paper reports on the development of an artificial intelligence system, based on convolutional neural networks and machine learning algorithms to assess photographic images of concrete surfaces for the presence and characteristics of cracks. CNNs are deep learning techniques that are particularly useful for image categorization. An important challenge in the development of the system was to ensure that real cracks could be distinguished from non-crack features or profiles on the concrete surface. After development, the AI system was trained using 1900 images of cracked and non-cracked concrete surfaces. A further 1100 images were then used for validation and testing of the system. The images were segmented or pixelated in order to simplify the representation of the image and make it easier to locate objects and boundaries. The system was further developed to estimate the length and average width of cracks in an image. The testing protocols showed that the AI model was 99.6% accurate in classifying cracked and non-cracked images. Furthermore, the average error for calculation of crack length and crack width was 1.5% and 5% respectively. These results show good promise for development of a fully-fledged AI system to support inspection and maintenance of RC structures.
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Luo, Mian, Ye Liu, Xu Li, and Junjie Dai. "Crack Self-Healing of Cement Mortar Containing Ureolytic Bacteria Immobilized in Artificial Functional Carrier under Different Exposure Environments." Buildings 12, no. 9 (September 1, 2022): 1348. http://dx.doi.org/10.3390/buildings12091348.
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The ureolytic bacteria and nutrients were immobilized in the artificial functional carrier (AFC) and the self-healing cement mortar, based on the AFC-encapsulated bacteria, was prepared for this paper. The crack self-healing effect of mortars with and without bacteria under different exposure environments (standard curing, dry–wet cycle curing, and water curing) was investigated by the visual observation of surface and internal cracks, water permeability tests, and mechanical performance recovery. In addition, the internal healing products of the cracks were observed using the metallographic microscope. The results show that the mortar specimens containing ureolytic bacteria immobilized in artificial functional carrier have a higher crack area repair ratio, and better water tightness regain and recovery ratio of flexural strength compared with the control mortars under the same exposure environment. The self-healing effect of mortar cracks with and without bacteria is obviously affected by the exposure environments. The self-healing effect of the cracks are the best when the mortar specimens are cured in water, followed by dry–wet cycle curing, and the self-healing effect of the cracks is the worst in standard curing, indicating that the presence of water is necessary for crack self-healing. The mortar specimens with bacteria generate more repair products in the surface and interior of the cracks to greatly improve the self-repair ability of the specimens, which promotes the recovery of water tightness and mechanical performance.
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Chen, Juntao, Yi Zhang, Kai Ma, Daozeng Tang, Hao Li, and Chengxiang Zhang. "Analysis of Mining Crack Evolution in Deep Floor Rock Mass with Fault." Geofluids 2021 (December 3, 2021): 1–15. http://dx.doi.org/10.1155/2021/5583877.
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To further explore the crack evolution of floor rock mass, the mechanism of fault activation, and water inrush, this paper analyzes the crack initiation and propagation mechanism of floor rock mass and obtains the initiation criteria of shear cracks, layered cracks, and vertical tension cracks. With the help of simulation software, the process of fault activation and crack evolution under different fault drop and dip angles was studied. The results show that the sequence of crack presented in the mining rock mass is vertical tension cracks, shear cracks, and layered cracks. The initiation and propagation of the shear cracks at the coal wall promote the fault activation, which tends to be easily caused at a specific inclination angle between 45° and 75°. The fault drop has no obvious impact on the evolution of floor rock cracks and will not induce fault activation. However, the increase of the drop will cause the roof to collapse, reducing the possibility of water inrush disaster. Research shows that measures such as adopting improved mining technology, reducing mining disturbance, increasing coal pillar size, and grouting before mining as reinforcement and artificial forced roof can effectively prevent water inrush disasters caused by deep mining due to fault activation.
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Chen, Zhenmao, Ladislav Janousek, Noritaka Yusa, and Kenzo Miya. "A Nondestructive Strategy for the Distinction of Natural Fatigue and Stress Corrosion Cracks Based on Signals From Eddy Current Testing." Journal of Pressure Vessel Technology 129, no. 4 (September 7, 2006): 719–28. http://dx.doi.org/10.1115/1.2767365.
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In this paper, a novel nondestructive strategy is proposed for distinguishing differences between a stress corrosion crack (SCC) and a fatigue crack (FC) based on signals from eddy current testing (ECT). The strategy consists of measurement procedures with a special ECT probe and crack type judgment scheme based on an index parameter that is defined as the amplitude ratio of the measured signals. An ECT probe, which can induce eddy current flowing mainly in a selected direction, is proposed and applied to detect crack signals by scanning along the crack with different probe orientations. It is clear that the ratio of the amplitudes of signals detected for parallel and perpendicular probe orientations is sensitive to the microstructure of the crack, i.e., the parameter is much bigger for a fatigue crack than that of a SCC. Therefore, whether a crack is a SCC or a FC can be recognized nondestructively by comparing the index parameter with a threshold value that can be previously determined. In order to verify the validity of the proposed strategy, many artificial SCC and FC test pieces were fabricated and ECT inspections were performed to measure the corresponding crack signals. Numerical simulations were also conducted to investigate the physical principles of the new methodology. From both the numerical and experimental results, it is demonstrated that the strategy is very promising for the distinction of artificial SCC and FC; there is also good possibility that this method can be applied to natural cracks if the threshold value can be properly determined.
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Behera, Sanjay Kumar, Dayal R. Parhi, and Harish C. Das. "Approach to establish a hybrid intelligent model for crack diagnosis in a fix-hinge beam structure." International Journal of Structural Integrity 10, no. 2 (April 8, 2019): 208–29. http://dx.doi.org/10.1108/ijsi-05-2018-0029.
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Purpose With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in solving the crack-related problems. The purpose of this paper is to establish a methodology that can detect and analyze crack development in a beam structure subjected to transverse free vibration. Design/methodology/approach Hybrid intelligent systems have acquired their own distinction as a potential problem-solving methodology adopted by researchers and scientists. It can be applied in many areas like science, technology, business and commerce. There have been the efforts by researchers in the recent past to combine the individual artificial intelligent techniques in parallel to generate optimal solutions for the problems. So it is an innovative effort to develop a strong computationally intelligent hybrid system based on different combinations of available artificial intelligence (AI) techniques. Findings In the present research, an integration of different AI techniques has been tested for accuracy. Theoretical, numerical and experimental investigations have been carried out using a fix-hinge aluminum beam of specified dimension in the presence and absence of cracks. The paper also gives an insight into the comparison of relative crack locations and crack depths obtained from numerical and experimental results with that of the results of the hybrid intelligent model and found to be in good agreement. Originality/value The paper covers the work to verify the accuracy of hybrid controllers in a fix-hinge beam which is very rare to find in the available literature. To overcome the limitations of standalone AI techniques, a hybrid methodology has been adopted. The output results for crack location and crack depth have been compared with experimental results, and the deviation of results is found to be within the satisfactory limit.
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Ju, Xiaochen, Xinxin Zhao, and Shengsheng Qian. "TransMF: Transformer-Based Multi-Scale Fusion Model for Crack Detection." Mathematics 10, no. 13 (July 5, 2022): 2354. http://dx.doi.org/10.3390/math10132354.
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Cracks are widespread in infrastructure that are closely related to human activity. It is very popular to use artificial intelligence to detect cracks intelligently, which is known as crack detection. The noise in the background of crack images, discontinuity of cracks and other problems make the crack detection task a huge challenge. Although many approaches have been proposed, there are still two challenges: (1) cracks are long and complex in shape, making it difficult to capture long-range continuity; (2) most of the images in the crack dataset have noise, and it is difficult to detect only the cracks and ignore the noise. In this paper, we propose a novel method called Transformer-based Multi-scale Fusion Model (TransMF) for crack detection, including an Encoder Module (EM), Decoder Module (DM) and Fusion Module (FM). The Encoder Module uses a hybrid of convolution blocks and Swin Transformer block to model the long-range dependencies of different parts in a crack image from a local and global perspective. The Decoder Module is designed with symmetrical structure to the Encoder Module. In the Fusion Module, the output in each layer with unique scales of Encoder Module and Decoder Module are fused in the form of convolution, which can release the effect of background noise and strengthen the correlations between relevant context in order to enhance the crack detection. Finally, the output of each layer of the Fusion Module is concatenated to achieve the purpose of crack detection. Extensive experiments on three benchmark datasets (CrackLS315, CRKWH100 and DeepCrack) demonstrate that the proposed TransMF in this paper exceeds the best performance of present baselines.
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Wu, Zihao, Yunchao Tang, Bo Hong, Bingqiang Liang, and Yuping Liu. "Enhanced Precision in Dam Crack Width Measurement: Leveraging Advanced Lightweight Network Identification for Pixel-Level Accuracy." International Journal of Intelligent Systems 2023 (September 2, 2023): 1–16. http://dx.doi.org/10.1155/2023/9940881.
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In dam engineering, the presence of cracks and crack width are important indicators for diagnosing the health of dams. The accurate measurement of cracks facilitates the safe use of dams. The manual detection of such defects is unsatisfactory in terms of cost, safety, accuracy, and the reliability of evaluation. The introduction of deep learning for crack detection can overcome these issues. However, the current deep learning algorithms possess a large volume of model parameters, high hardware requirements, and difficulty toward embedding in mobile devices such as drones. Therefore, we propose a lightweight MobileNetV2_DeepLabV3 image segmentation network. Furthermore, to prevent interference by noise, light, shadow, and other factors for long-length targets when segmenting, the atrous spatial pyramid pooling (ASPP) module parameters in the DeepLabV3+ network structure were modified, and a multifeature fusion structure was used instead of the parallel structure in ASPP, allowing the network to obtain richer crack features. We collected the images of dam cracks from different environments, established segmentation datasets, and obtained segmentation models through network training. Experiments show that the improved MobileNetV2_DeepLabV3 algorithm exhibited a higher crack segmentation accuracy than the original MobileNetV2_DeepLabV3 algorithm; the average intersection rate attained 83.23%; and the crack detail segmentation was highly accurate. Compared with other semantic segmentation networks, its training time was at least doubled, and the total parameters were reduced by more than 2 to 7 times. After extracting cracks through the semantic segmentation, we proposed to use the method of inscribed circle of crack outline to calculate the maximum width of the detected crack image and to convert it into the actual width of the crack. The maximum relative error rate was 11.22%. The results demonstrated the potential of innovative deep learning methods for dam crack detection.
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Wang, Li, and Zhenmao Chen. "Sizing of natural crack using multi-output support vector regression method from multi-frequency eddy current testing signals." International Journal of Applied Electromagnetics and Mechanics 64, no. 1-4 (December 10, 2020): 721–28. http://dx.doi.org/10.3233/jae-209383.
Повний текст джерелаАнотація:
In the nondestructive evaluation for components of key equipment, sizing of natural crack is important in order to guarantee both the safety and efficient operation for large mechanical systems. Natural cracks have complex boundary and there may be electric current flowing through crack faces. If a simple model of artificial notch is used to simulate it, errors often occur in crack depth reconstruction from eddy current testing (ECT) signals. However, if a complex crack conductivity model is used, quantitative evaluation of natural crack will be transformed into a multivariable nonlinear optimization problem and the solution is difficult. In this paper, based on the relationship between crack parameters and features of multi-frequency ECT signals, a multi-output support vector regression algorithm using domain decomposition for parameters was proposed. The algorithm realized the quantitative evaluation of multiple parameters of crack in turn. Numerical examples with simulated and measured ECT signals were presented to verify the efficiency of the proposed strategy.
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Hwu, Chyan Bin, and Ying Chun Liang. "Crack Identification by Artificial Neural Network." Key Engineering Materials 145-149 (October 1997): 405–10. http://dx.doi.org/10.4028/www.scientific.net/kem.145-149.405.
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Lee, Sang Eon, and Jung-Wuk Hong. "Effect of Crack Closure on Magnitude of Modulated Wave." International Journal of Structural Stability and Dynamics 20, no. 13 (December 2020): 2041018. http://dx.doi.org/10.1142/s0219455420410187.
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Fatigue cracks generated by repeated loads cause structural failures. Such cracks grow continuously and at an increasing speed owing to the concentration of stresses near the crack tips. Therefore, the early detection of fatigue cracks is imperative in the field of structural-health monitoring for the safety of structures exposed to dynamic loading. In particular, the detection of those cracks subjected to compression is known as a challenging problem in the nondestructive inspection area. The nonlinear ultrasonic modulation technique is effective for the detection of microcracks smaller than the size of a wavelength because this technique uses the deformation of waves passing through the crack surfaces. However, the technique has not been thoroughly verified for detecting cracks subjected to external forces. In this study, nonlinear ultrasonic modulation tests are performed on two types of crack specimens under compressive forces. The results show that in fatigue-cracked specimens, the cracks can be detected using modulated waves even under strong compressions. With artificial cracks, buckling occurs at a relatively low compression, and the amounts of modulated waves rapidly increase due to the bending of the specimen before buckling failure takes place. In this study, the crack detection methodology under compression is proposed and experimentally verified. The proposed method might be beneficial to find cracks under compression in various structural components.
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Bai, Xiaotian, Zhaonan Zhang, Huaitao Shi, Zhong Luo, and Tao Li. "Identification of Subsurface Mesoscale Crack in Full Ceramic Ball Bearings Based on Strain Energy Theory." Applied Sciences 13, no. 13 (June 30, 2023): 7783. http://dx.doi.org/10.3390/app13137783.
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Subsurface mesoscale cracks exist widely in the outer ring of full ceramic ball bearings (FCBBs), which is a potential threat for the stable operation of related devices such as aero engines, food processing machinery, and artificial replacement hip joints. This paper establishes a dynamic model of subsurface mesoscale cracks in the outer ring of FCBBs based on strain energy theory, and the influence of different crack lengths on the running state is analyzed. The existence of mesoscale cracks is regarded as weakening on the stiffness coefficient, and the deterioration degree of outer ring stiffness of subsurface cracks is thereby quantified. It is found that a small wave peak appears in the vibration time-domain signal when there is a mesoscale crack on the outer ring subsurface, and the crack evolution is evaluated by the amplitude of the corresponding feature frequency. Finally, the accuracy of the model is verified by experiments. The model realizes the identification and degree evaluation of subsurface mesoscale cracks in FCBBs, and provides theoretical references for the diagnosis and status monitoring for FCBB rotor systems.
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Bian, Ziyan. "Identification of Concrete Crack Using Deep Learning Based Approach." Advances in Research 25, no. 5 (October 1, 2024): 272–80. http://dx.doi.org/10.9734/air/2024/v25i51160.
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Cracks reflect the safety and durability of concrete structures, and the existing artificial crack detection has the disadvantages of low efficiency and large error. However, the use of deep learning of images to identify cracks has the advantages of high efficiency, small error and low cost. This paper systematically discusses the deep learning in the identification of concrete structure cracks, expounds the deep learning technology, studies the SqueezeNet network model and YOLO (You Only Look Once) network model in the field of concrete structure crack identification, and improves the model according to the test results. In this paper, the classification accuracy based on crack trend is improved by increasing the width and depth of the Fire module of the SqueezeNet network. Secondly, the target detection accuracy is improved by the transfer learning of the YOLO model. The results show that the classification accuracy of the improved SqueezeNet model is more than 82% and the recognition accuracy of YOLO model is more than 92%.
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Song, Dowon, Taeseup Song, Ungyu Paik, Guanlin Lyu, Yeon-Gil Jung, Baig-Gyu Choi, In-Soo Kim, and Jing Zhang. "Crack-Resistance Behavior of an Encapsulated, Healing Agent Embedded Buffer Layer on Self-Healing Thermal Barrier Coatings." Coatings 9, no. 6 (May 31, 2019): 358. http://dx.doi.org/10.3390/coatings9060358.
Повний текст джерелаАнотація:
In this work, a novel thermal barrier coating (TBC) system is proposed that embeds silicon particles in coating as a crack-healing agent. The healing agent is encapsulated to avoid unintended reactions and premature oxidation. Thermal durability of the developed TBCs is evaluated through cyclic thermal fatigue and jet engine thermal shock tests. Moreover, artificial cracks are introduced into the buffer layer’s cross section using a microhardness indentation method. Then, the indented TBC specimens are subject to heat treatment to investigate their crack-resisting behavior in detail. The TBC specimens with the embedded healing agents exhibit a relatively better thermal fatigue resistance than the conventional TBCs. The encapsulated healing agent protects rapid large crack openings under thermal shock conditions. Different crack-resisting behaviors and mechanisms are proposed depending on the embedding healing agents.
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ur-Rehman, A., and P. F. Thomason. "THE EFFECT OF ARTIFICIAL FATIGUE-CRACK CLOSURE ON FATIGUE-CRACK GROWTH." Fatigue & Fracture of Engineering Materials and Structures 16, no. 10 (October 1993): 1081–90. http://dx.doi.org/10.1111/j.1460-2695.1993.tb00079.x.
Повний текст джерела
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Lee, Taehee, Jung-Ho Kim, Sung-Jin Lee, Seung-Ki Ryu, and Bong-Chul Joo. "Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning." Applied Sciences 13, no. 4 (February 12, 2023): 2367. http://dx.doi.org/10.3390/app13042367.
Повний текст джерелаАнотація:
Signs of functional loss due to the deterioration of structures are primarily identified from cracks occurring on the surface of structures, and continuous monitoring of structural cracks is essential for socially important structures. Recently, many structural crack monitoring technologies have been developed with the development of deep-learning artificial intelligence (AI). In this study, stacking ensemble learning was applied to predict the structural cracks more precisely. A semantic segmentation model was primarily used for crack detection using a deep learning AI model. We studied the crack-detection performance by training UNet, DeepLabV3, DeepLabV3+, DANet, and FCN-8s. Owing to the unsuitable crack segmentation performance of the FCN-8s, stacking ensemble learning was conducted with the remaining four models. Individual models yielded an intersection over union (IoU) score ranging from approximately 0.4 to 0.6 for the test dataset. However, when the metamodel completed with stacking ensemble learning was used, the IoU score was 0.74, indicating a high-performance improvement. A total of 1235 test images was acquired with drones on the sea bridge, and the stacking ensemble model showed an IoU of 0.5 or higher for 64.4% of the images.