Structural Monitoring and Maintenance

  • 제9권3호
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  • Pages.221-235
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  • 2022
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

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))
  • 투고 : 2022.04.14
  • 심사 : 2022.07.18
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.

참고문헌

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