Artigos de revistas sobre o tema "Artificial crack"
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Raihan, Prodhan Md Safiq, Anik Md Shahjahan, Shamima Akter Shimky, Toki Thamid Zim, Summa Parven, Abdul Ali Khan e Mir Fazle Rabbi. "Pavement Crack Detection and Solution with Artificial Intelligence". European Journal of Theoretical and Applied Sciences 2, n.º 4 (1 de julho de 2024): 277–314. http://dx.doi.org/10.59324/ejtas.2024.2(4).25.
Texto completo da fonteKuttimarks, Dr M. S. "Crack Detection of Structures using Artificial Intelligence System". International Journal for Research in Applied Science and Engineering Technology 12, n.º 5 (31 de maio de 2024): 1894–901. http://dx.doi.org/10.22214/ijraset.2024.61958.
Texto completo da fonteWang, Zi Zhen, Ri He Wang, Yu Huan Bu e Xun Shan. "A New Method of Preparing Artificial Cores with Certain Cracks for Experiment Study of Elastic Wave Propagation". Advanced Materials Research 356-360 (outubro de 2011): 2954–57. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.2954.
Texto completo da fonteWu, Zhenkai, Xizhe Li, Hanmin Xiao, Xuewei Liu, Wei Lin, Yuan Rao, Yang Li e Jie Zhang. "The Establishment and Evaluation Method of Artificial Microcracks in Rocks". Energies 14, n.º 10 (12 de maio de 2021): 2780. http://dx.doi.org/10.3390/en14102780.
Texto completo da fonteSakamoto, Junji, Yoshimasa Takahashi e 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 (setembro de 2011): 319–22. http://dx.doi.org/10.4028/www.scientific.net/kem.488-489.319.
Texto completo da fonteFathalla, Eissa, Yasushi Tanaka, Koichi Maekawa e Akito Sakurai. "Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks". Applied Sciences 8, n.º 7 (21 de julho de 2018): 1197. http://dx.doi.org/10.3390/app8071197.
Texto completo da fonteHendroprasetyo, Wing, e 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, n.º 1 (1 de janeiro de 2022): 012041. http://dx.doi.org/10.1088/1755-1315/972/1/012041.
Texto completo da fonteKim, Jung Jin, Ah-Ram Kim e Seong-Won Lee. "Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures". Applied Sciences 10, n.º 22 (16 de novembro de 2020): 8105. http://dx.doi.org/10.3390/app10228105.
Texto completo da fonteM N, Sumaiya, Prajwal K, Rao Shravan Vasudev, Shreya K A, Thrishul R e R. Manjunath Prasad. "Comparative Analysis of Concrete Crack Detection using Image Processing and Artificial Intelligence". Journal of Image Processing and Artificial Intelligence 9, n.º 1 (11 de janeiro de 2023): 8–15. http://dx.doi.org/10.46610/joipai.2023.v09i01.002.
Texto completo da fonteSun, Xichen, Jie Chen, Siyi Lu, Miaomiao Liu, Siyu Chen, Yifei Nan, Yang Wang e Jun Feng. "Ureolytic MICP-Based Self-Healing Mortar under Artificial Seawater Incubation". Sustainability 13, n.º 9 (25 de abril de 2021): 4834. http://dx.doi.org/10.3390/su13094834.
Texto completo da fonteZhang, Yue, Xuemin Zhang, Yun Su, Xuan Li, Shiwei Ma, Su Zhang, Weihe Ren e Kang Li. "Tunnel Lining Crack Detection Method Based on Polarization 3D Imaging". Photonics 10, n.º 10 (27 de setembro de 2023): 1085. http://dx.doi.org/10.3390/photonics10101085.
Texto completo da fonteRifdah Mufiidah Harahap, Darlina Tanjung, M Husni Malik Hasibuan e Marwan Lubis. "Analisis Deteksi Kedalaman Retak Pada Beton Mengunakan Metode UPV Testing". Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik Sipil 2, n.º 2 (22 de março de 2024): 112–20. http://dx.doi.org/10.61132/konstruksi.v2i2.236.
Texto completo da fonteJiang, Sheng, Mansour Sharafisafa e Luming Shen. "Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates". Materials 14, n.º 11 (3 de junho de 2021): 3042. http://dx.doi.org/10.3390/ma14113042.
Texto completo da fonteYang, Gang, Jianchao Wu e Qing Hu. "Rapid detection of building cracks based on image processing technology with double square artificial marks". Advances in Structural Engineering 22, n.º 5 (1 de novembro de 2018): 1186–93. http://dx.doi.org/10.1177/1369433218810183.
Texto completo da fonteShehata, Hesham M., Yasser S. Mohamed, Mohamed Abdellatif e Taher H. Awad. "Crack Width Estimation Using Feed and Cascade Forward Back Propagation Artificial Neural Networks". Key Engineering Materials 786 (outubro de 2018): 293–301. http://dx.doi.org/10.4028/www.scientific.net/kem.786.293.
Texto completo da fonteHu, Guo X., Bao L. Hu, Zhong Yang, Li Huang e Ping Li. "Pavement Crack Detection Method Based on Deep Learning Models". Wireless Communications and Mobile Computing 2021 (15 de maio de 2021): 1–13. http://dx.doi.org/10.1155/2021/5573590.
Texto completo da fonteCui, Zhendong, e Weige Han. "In SituScanning Electron Microscope (SEM) Observations of Damage and Crack Growth of Shale". Microscopy and Microanalysis 24, n.º 2 (abril de 2018): 107–15. http://dx.doi.org/10.1017/s1431927618000211.
Texto completo da fonteLiu, Yifan, Weiliang Gao, Tingting Zhao, Zhiyong Wang e Zhihua Wang. "A Rapid Bridge Crack Detection Method Based on Deep Learning". Applied Sciences 13, n.º 17 (31 de agosto de 2023): 9878. http://dx.doi.org/10.3390/app13179878.
Texto completo da fonteZheng, Mu Lin, Zhang Wei Ling, Min Wang, Shuai Kong e Wei Can Guo. "The Experimental Research on Horizontal Underground Tank Magnetic Flux Leakage Testing". Applied Mechanics and Materials 752-753 (abril de 2015): 1236–39. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1236.
Texto completo da fonteZhu, Yantao, e Hongwu Tang. "Automatic Damage Detection and Diagnosis for Hydraulic Structures Using Drones and Artificial Intelligence Techniques". Remote Sensing 15, n.º 3 (20 de janeiro de 2023): 615. http://dx.doi.org/10.3390/rs15030615.
Texto completo da fonteAbdollahzadeh Jamalabadi, Mohammad Yaghoub. "The Use of Artificial Intelligence for Image Processing of Crack Patterns in Panel Painting". Sumerianz Journal of Scientific Research, n.º 51 (24 de janeiro de 2022): 1–12. http://dx.doi.org/10.47752/sjsr.51.1.12.
Texto completo da fonteWu, Dongling, Hongxiang Zhang e Yiying Yang. "Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC)". Journal of Advanced Transportation 2022 (15 de junho de 2022): 1–10. http://dx.doi.org/10.1155/2022/4117957.
Texto completo da fonteLiu, Qi, Shancheng Cao e Zhiwen Lu. "An Improved Crack Breathing Model and Its Application in Crack Identification for Rotors". Machines 11, n.º 5 (20 de maio de 2023): 569. http://dx.doi.org/10.3390/machines11050569.
Texto completo da fonteKim, Jae-Seong, Bo-Young Lee, Woong-Gi Hwang e 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.
Texto completo da fonteAman, Alexandra-Teodora, Cristian Tufisi, Gilbert-Rainer Gillich e Tiberiu Manescu. "Damage detection in variable temperature conditions using artificial intelligence". Vibroengineering Procedia 51 (20 de outubro de 2023): 186–92. http://dx.doi.org/10.21595/vp.2023.23679.
Texto completo da fonteBuffière, Jean Yves, Emilie Ferrié, Wolfgang Ludwig e Anthony Gravouil. "Characterisation and Modelling of the Three Dimensional Propagation of Short Fatigue Cracks". Materials Science Forum 519-521 (julho de 2006): 997–1004. http://dx.doi.org/10.4028/www.scientific.net/msf.519-521.997.
Texto completo da fonteLi, You Tang, e Huai Qing Li. "Analysis of Stress Singularity near the Tip of Artificial Crack". Key Engineering Materials 525-526 (novembro de 2012): 445–48. http://dx.doi.org/10.4028/www.scientific.net/kem.525-526.445.
Texto completo da fonteTheocaris, P. S. "Peculiarities of the artificial crack". Engineering Fracture Mechanics 38, n.º 1 (janeiro de 1991): 37–54. http://dx.doi.org/10.1016/0013-7944(91)90205-f.
Texto completo da fonteWu, Yangxu, Wanting Yang, Jinxiao Pan e Ping Chen. "Asphalt pavement crack detection based on multi-scale full convolutional network". Journal of Intelligent & Fuzzy Systems 40, n.º 1 (4 de janeiro de 2021): 1495–508. http://dx.doi.org/10.3233/jifs-191105.
Texto completo da fonteBarrarat, F., B. Helifa, I. K. Lefkaier, S. Bensaid e K. Rayane. "Defect Reconstruction Using Multilayer Perceptron for Regression and Classification Tasks Based On Eddy Current Signatures". Materials Evaluation 82, n.º 10 (1 de outubro de 2024): 47–56. http://dx.doi.org/10.32548/2024.me-04439.
Texto completo da fonteHer, Shiuh Chuan, e Sheng Tung Lin. "Characterization of Surface Crack Using Surface Waves". Applied Mechanics and Materials 166-169 (maio de 2012): 1931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1931.
Texto completo da fonteHu, Jue, Weiping Xu, Bin Gao, Gui Tian, Yizhe Wang, Yingchun Wu, Ying Yin e Juan Chen. "Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System". Metals 8, n.º 8 (6 de agosto de 2018): 612. http://dx.doi.org/10.3390/met8080612.
Texto completo da fonteKnorr, Alain Franz, e Michael Marx. "Calculating the Resistance of a Grain Boundary against Fatigue Crack Growth". Advanced Materials Research 891-892 (março de 2014): 929–35. http://dx.doi.org/10.4028/www.scientific.net/amr.891-892.929.
Texto completo da fonteGao, Xin Wen, ShuaiQing Li, Bang Yang Jin, Min Hu e Wei Ding. "Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection". Journal of Intelligent & Fuzzy Systems 40, n.º 3 (2 de março de 2021): 4453–69. http://dx.doi.org/10.3233/jifs-201296.
Texto completo da fonteAbhijeet 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, n.º 2 (2019): 699–708. http://dx.doi.org/10.24247/ijmperdapr201970.
Texto completo da fonteGomera, Mufaro, e 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.
Texto completo da fonteLuo, Mian, Ye Liu, Xu Li e Junjie Dai. "Crack Self-Healing of Cement Mortar Containing Ureolytic Bacteria Immobilized in Artificial Functional Carrier under Different Exposure Environments". Buildings 12, n.º 9 (1 de setembro de 2022): 1348. http://dx.doi.org/10.3390/buildings12091348.
Texto completo da fonteChen, Juntao, Yi Zhang, Kai Ma, Daozeng Tang, Hao Li e Chengxiang Zhang. "Analysis of Mining Crack Evolution in Deep Floor Rock Mass with Fault". Geofluids 2021 (3 de dezembro de 2021): 1–15. http://dx.doi.org/10.1155/2021/5583877.
Texto completo da fonteChen, Zhenmao, Ladislav Janousek, Noritaka Yusa e 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, n.º 4 (7 de setembro de 2006): 719–28. http://dx.doi.org/10.1115/1.2767365.
Texto completo da fonteBehera, Sanjay Kumar, Dayal R. Parhi e 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, n.º 2 (8 de abril de 2019): 208–29. http://dx.doi.org/10.1108/ijsi-05-2018-0029.
Texto completo da fonteJu, Xiaochen, Xinxin Zhao e Shengsheng Qian. "TransMF: Transformer-Based Multi-Scale Fusion Model for Crack Detection". Mathematics 10, n.º 13 (5 de julho de 2022): 2354. http://dx.doi.org/10.3390/math10132354.
Texto completo da fonteWu, Zihao, Yunchao Tang, Bo Hong, Bingqiang Liang e Yuping Liu. "Enhanced Precision in Dam Crack Width Measurement: Leveraging Advanced Lightweight Network Identification for Pixel-Level Accuracy". International Journal of Intelligent Systems 2023 (2 de setembro de 2023): 1–16. http://dx.doi.org/10.1155/2023/9940881.
Texto completo da fonteWang, Li, e 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, n.º 1-4 (10 de dezembro de 2020): 721–28. http://dx.doi.org/10.3233/jae-209383.
Texto completo da fonteHwu, Chyan Bin, e Ying Chun Liang. "Crack Identification by Artificial Neural Network". Key Engineering Materials 145-149 (outubro de 1997): 405–10. http://dx.doi.org/10.4028/www.scientific.net/kem.145-149.405.
Texto completo da fonteLee, Sang Eon, e Jung-Wuk Hong. "Effect of Crack Closure on Magnitude of Modulated Wave". International Journal of Structural Stability and Dynamics 20, n.º 13 (dezembro de 2020): 2041018. http://dx.doi.org/10.1142/s0219455420410187.
Texto completo da fonteBai, Xiaotian, Zhaonan Zhang, Huaitao Shi, Zhong Luo e Tao Li. "Identification of Subsurface Mesoscale Crack in Full Ceramic Ball Bearings Based on Strain Energy Theory". Applied Sciences 13, n.º 13 (30 de junho de 2023): 7783. http://dx.doi.org/10.3390/app13137783.
Texto completo da fonteBian, Ziyan. "Identification of Concrete Crack Using Deep Learning Based Approach". Advances in Research 25, n.º 5 (1 de outubro de 2024): 272–80. http://dx.doi.org/10.9734/air/2024/v25i51160.
Texto completo da fonteSong, Dowon, Taeseup Song, Ungyu Paik, Guanlin Lyu, Yeon-Gil Jung, Baig-Gyu Choi, In-Soo Kim e Jing Zhang. "Crack-Resistance Behavior of an Encapsulated, Healing Agent Embedded Buffer Layer on Self-Healing Thermal Barrier Coatings". Coatings 9, n.º 6 (31 de maio de 2019): 358. http://dx.doi.org/10.3390/coatings9060358.
Texto completo da fonteur-Rehman, A., e P. F. Thomason. "THE EFFECT OF ARTIFICIAL FATIGUE-CRACK CLOSURE ON FATIGUE-CRACK GROWTH". Fatigue & Fracture of Engineering Materials and Structures 16, n.º 10 (outubro de 1993): 1081–90. http://dx.doi.org/10.1111/j.1460-2695.1993.tb00079.x.
Texto completo da fonteLee, Taehee, Jung-Ho Kim, Sung-Jin Lee, Seung-Ki Ryu e Bong-Chul Joo. "Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning". Applied Sciences 13, n.º 4 (12 de fevereiro de 2023): 2367. http://dx.doi.org/10.3390/app13042367.
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