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Structural Monitoring and Maintenance
  Volume 9, Number 3, September 2022 , pages 221-235

Nondestructive crack detection in metal structures using impedance responses and artificial neural networks
Duc-Duy Ho, Tran-Huu-Tin Luu and Minh-Nhan Pham

    Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.
Key Words
    artificial neural network; crack; damage detection; electro-mechanical impedance; structural health monitoring
(1) Duc-Duy Ho, Minh-Nhan Pham:
Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam;
(2) Duc-Duy Ho, Tran-Huu-Tin Luu, Minh-Nhan Pham:
Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam;
(3) Tran-Huu-Tin Luu:
Vietnam National University Ho Chi Minh City, Campus in Ben Tre, Ben Tre, Vietnam.

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