Steel and Composite Structures

  • 제42권5호
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  • Pages.685-697
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  • 2022
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Vibration-based delamination detection of composites using modal data and experience-based learning algorithm

  • Luo, Weili (School of Civil Engineering, Guangzhou University) ;
  • Wang, Hui (School of Civil Engineering, Guangzhou University) ;
  • Li, Yadong (School of Civil Engineering, Guangzhou University) ;
  • Liang, Xing (School of Civil Engineering, Guangzhou University) ;
  • Zheng, Tongyi (School of Civil Engineering, Guangzhou University)
  • 투고 : 2020.12.10
  • 심사 : 2022.03.10
  • 발행 : 2022.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, a vibration-based method using the change ratios of modal data and the experience-based learning algorithm is presented for quantifying the position, size, and interface layer of delamination in laminated composites. Three types of objective functions are examined and compared, including the ones using frequency changes only, mode shape changes only, and their combination. A fine three-dimensional FE model with constraint equations is utilized to extract modal data. A series of numerical experiments is carried out on an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam for investigating the influence of the objective function, the number of modal data, the noise level, and the optimization algorithms. Numerical results confirm that the frequency-and-mode-shape-changes-based technique yields excellent results in all the three delamination variables of the composites and the addition of mode shape information greatly improves the accuracy of interface layer prediction. Moreover, the EBL outperforms the other three state-of-the-art optimization algorithms for vibration-based delamination detection of composites. A laboratory test on six CFRP beams validates the frequency-and-mode-shape-changes-based technique and confirms again its superiority for delamination detection of composites.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (Grant No. 51808147).

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