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Journal articles on the topic 'Artificial crack'

Author: Grafiati

Published: 2 November 2024

Last updated: 31 July 2025

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1

Raihan, Prodhan Md Safiq, Anik Md Shahjahan, Shamima Akter Shimky, et al. "Pavement Crack Detection and Solution with Artificial Intelligence." European Journal of Theoretical and Applied Sciences 2, no. 4 (2024): 277–314. http://dx.doi.org/10.59324/ejtas.2024.2(4).25.

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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 came

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Prodhan, Md Safiq Raihan, Md Shahjahan Anik, Akter Shimky Shamima, et al. "Pavement Crack Detection and Solution with Artificial Intelligence." European Journal of Theoretical and Applied Sciences 2, no. 4 (2024): 277–314. https://doi.org/10.59324/ejtas.2024.2(4).25.

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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 came

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Dania, Rita, Adebisi Bamgbade, and Abdulmajid Isiaka. "EVALUATION OF BUILDING CRACKS USING ARTIFICIAL INTELLIGENCE BASED CRACK DETECTION SOFTWARE (ABECIS)." Nile Journal of Engineering and Applied Science 2, no. 2 (2025): 1. https://doi.org/10.5455/njeas.197737.

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Various traditional methods have been applied to detect cracks, these are prone to errors, require high human capital, high cost of equipment is involved and it is time consuming. In recent times, more sophisticated technologically based methods have been developed and used in the detection and analysis of cracks, inclusive of more advanced Artificial Intelligence (AI) technology. This study is aimed at evaluating the AI based Crack detection software for crack analysis. Cracks images were identified and captured using an android based smart phone from some selected residential buildings withi

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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 (2024): 1894–901. http://dx.doi.org/10.22214/ijraset.2024.61958.

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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 identifica

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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.

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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. Th

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Wu, Zhenkai, Xizhe Li, Hanmin Xiao, et al. "The Establishment and Evaluation Method of Artificial Microcracks in Rocks." Energies 14, no. 10 (2021): 2780. http://dx.doi.org/10.3390/en14102780.

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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 betwee

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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.

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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

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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 (2018): 1197. http://dx.doi.org/10.3390/app8071197.

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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

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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 (2022): 012041. http://dx.doi.org/10.1088/1755-1315/972/1/012041.

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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 fabricat

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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 (2020): 8105. http://dx.doi.org/10.3390/app10228105.

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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 de

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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 (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 c

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Zhang, Yue, Xuemin Zhang, Yun Su, et al. "Tunnel Lining Crack Detection Method Based on Polarization 3D Imaging." Photonics 10, no. 10 (2023): 1085. http://dx.doi.org/10.3390/photonics10101085.

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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 detectio

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Sun, Xichen, Jie Chen, Siyi Lu, et al. "Ureolytic MICP-Based Self-Healing Mortar under Artificial Seawater Incubation." Sustainability 13, no. 9 (2021): 4834. http://dx.doi.org/10.3390/su13094834.

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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.

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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 (2024): 112–20. http://dx.doi.org/10.61132/konstruksi.v2i2.236.

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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

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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 (2021): 3042. http://dx.doi.org/10.3390/ma14113042.

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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 orien

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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 (2018): 1186–93. http://dx.doi.org/10.1177/1369433218810183.

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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

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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.

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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 ne

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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 (2018): 107–15. http://dx.doi.org/10.1017/s1431927618000211.

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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 s

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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.

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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, lo

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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 (2023): 9878. http://dx.doi.org/10.3390/app13179878.

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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

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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.

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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

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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 (2023): 615. http://dx.doi.org/10.3390/rs15030615.

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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 hyd

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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.

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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 co

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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.

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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 continua

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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 (2023): 569. http://dx.doi.org/10.3390/machines11050569.

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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

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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 corrosivit

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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 singul

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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 bou

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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 (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

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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.

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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 i

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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 (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, numerica

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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

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Theocaris, P. S. "Peculiarities of the artificial crack." Engineering Fracture Mechanics 38, no. 1 (1991): 37–54. http://dx.doi.org/10.1016/0013-7944(91)90205-f.

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Hu, Jue, Weiping Xu, Bin Gao, et al. "Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System." Metals 8, no. 8 (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 neur

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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 (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 m

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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 io

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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

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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 (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 m

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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 conc

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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 (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

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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

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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 (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

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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 (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

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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 (2020): 721–28. http://dx.doi.org/10.3233/jae-209383.

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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 optim

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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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Doğanay Katı, Hilal, Jamilu Buhari, Arturo Francese, Feiyang He, and Muhammad Khan. "Numerical Analysis of Crack Path Effects on the Vibration Behaviour of Aluminium Alloy Beams and Its Identification via Artificial Neural Networks." Sensors 25, no. 3 (2025): 838. https://doi.org/10.3390/s25030838.

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Understanding and predicting the behaviour of fatigue cracks are essential for ensuring safety, optimising maintenance strategies, and extending the lifespan of critical components in industries such as aerospace, automotive, civil engineering and energy. Traditional methods using vibration-based dynamic responses have provided effective tools for crack detection but often fail to predict crack propagation paths accurately. This study focuses on identifying crack propagation paths in an aluminium alloy 2024-T42 cantilever beam using dynamic response through numerical simulations and artificial

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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 (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

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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 (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 sti

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Bian, Ziyan. "Identification of Concrete Crack Using Deep Learning Based Approach." Advances in Research 25, no. 5 (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

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Song, Dowon, Taeseup Song, Ungyu Paik, et al. "Crack-Resistance Behavior of an Encapsulated, Healing Agent Embedded Buffer Layer on Self-Healing Thermal Barrier Coatings." Coatings 9, no. 6 (2019): 358. http://dx.doi.org/10.3390/coatings9060358.

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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 s

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