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http://dx.doi.org/10.12989/smm.2022.9.3.221

Nondestructive crack detection in metal structures using impedance responses and artificial neural networks  

Ho, Duc-Duy (Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT))
Luu, Tran-Huu-Tin (Vietnam National University Ho Chi Minh City)
Pham, Minh-Nhan (Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT))
Publication Information
Structural Monitoring and Maintenance / v.9, no.3, 2022 , pp. 221-235 More about this Journal
Abstract
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.
Keywords
artificial neural network; crack; damage detection; electro-mechanical impedance; structural health monitoring;
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Times Cited By KSCI : 4  (Citation Analysis)
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