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Crack identification using electrical impedance tomography and transfer learning.

• Authors : Quqa, Said^1,2,3 (AUTHOR) ; Landi, Luca¹ (AUTHOR); Loh, Kenneth J.^2,3 (AUTHOR)

• Source: Computer-Aided Civil & Infrastructure Engineering. Nov2023, Vol. 38 Issue 17, p2426-2442. 17p.

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Crack identification in laminated composites based on modal responses using metaheuristics, artificial neural networks and response surface method: a comparative study.

• Authors : de Assis, Felipe Mouallem¹ (AUTHOR); Gomes, Guilherme Ferreira¹ (AUTHOR)

• Source: Archive of Applied Mechanics. Oct2021, Vol. 91 Issue 10, p4389-4408. 20p.

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Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions.

• Authors : Saeed, R.¹ ; Galybin, A.¹; Popov, V.¹

• Source: Neural Computing & Applications. Oct2012, Vol. 21 Issue 7, p1629-1645. 17p. 1 Black and White Photograph, 5 Diagrams, 5 Charts, 9 Graphs.

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Neural network approach to crack identification.

• Authors : Sikora, R.; Chady, T.

• Source: International Journal of Applied Electromagnetics & Mechanics. 1998, Vol. 9 Issue 4, p391. 8p.

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Numerical Analysis of Crack Path Effects on the Vibration Behaviour of Aluminium Alloy Beams and Its Identification via Artificial Neural Networks.

• Authors : Doğanay Katı, Hilal^1,2 (AUTHOR) ; Buhari, Jamilu¹ (AUTHOR) ; Francese, Arturo¹ (AUTHOR)

• Source: Sensors (14248220). Feb2025, Vol. 25 Issue 3, p838. 17p.

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Research on a Road Crack Detection Method Based on YOLO11-MBC.

• Authors : Li, Jinhui¹ (AUTHOR); Jiang, Xiaowei^1,2 (AUTHOR) ; Peng, Hui^1,2 (AUTHOR)

• Source: Sensors (14248220). Dec2025, Vol. 25 Issue 24, p7435. 21p.

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Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks.

• Authors : Fu, Ronghua¹ (AUTHOR) ; Xu, Hao^2,3 (AUTHOR) ; Wang, Zijian⁴ (AUTHOR)

• Source: Sensors (14248220). Apr2020, Vol. 20 Issue 7, p2021. 1p.

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Intelligent identification of cracking based on wavelet transform and artificial neural network analysis of acoustic emission signals.

• Authors : Zong-lian Wang¹; Jian-guo Ning¹; Hui-lan Ren¹

• Source: Insight: Non-Destructive Testing & Condition Monitoring. Aug2018, Vol. 60 Issue 8, p426-433. 8p. 1 Black and White Photograph, 6 Diagrams, 1 Chart, 7 Graphs.

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Quantification of cracks in beams on the Pasternak foundation using Haar wavelets and machine learning.

• Authors : Hein, Helle¹ ; Jaanuska, Ljubov¹

• Source: Proceedings of the Estonian Academy of Sciences. 2022, Vol. 71 Issue 1, p16-29. 14p.

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Structural crack detection using deep learning–based fully convolutional networks.

• Source: Advances in Structural Engineering. Dec2019, Vol. 22 Issue 16, p3412-3419. 8p.

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