Structural Monitoring and Maintenance

  • 제11권3호
  • /
  • Pages.203-217
  • /
  • 2024
  • /
  • 2288-6605(pISSN)
  • /
  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An approach for structural damage identification using electromechanical impedance

  • Yujun Ye (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Yikai Zhu (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Bo Lei (China Construction Third Engineering Bureau Co., Ltd.) ;
  • Zhihai Weng (Huzhou City Investment and Development Group Co., Ltd.) ;
  • Hongchang Xu (China Construction Third Engineering Bureau Co., Ltd.) ;
  • Huaping Wan (College of Civil Engineering and Architecture, Zhejiang University)
  • 투고 : 2023.11.19
  • 심사 : 2024.03.13
  • 발행 : 2024.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Electro-mechanical impedance (EMI) technique is a low-cost structural damage detection method. It reflects structural damage through the change in admittance signal which contains the structural mechanical impedance information. The ambient temperature greatly affects the admittance signal, which hides the changes caused by structural damage and reduces the accuracy of damage identification. This study introduces a convolutional neural network to compensate for the temperature effect. The proposed method uses a framework that consists of a feature extraction network and a decoding network, and the original admittance signal with temperature information is used as the input. The output admittance signal is eliminated from the temperature effect, improving damage identification robustness. The admittance data simulated by the finite element model of the spatial grid structure is used to verify the effectiveness of the proposed method. The results show that the proposed method has advantages in identification accuracy compared with the damage index minimization method and the principal component analysis method.

키워드

참고문헌

  1. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M. and Inman, D.J. (2021), "A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications", Mech. Syst. Signal Pr., 147, 107077. https://doi.org/10.1016/j.ymssp.2020.107077.
  2. Azimi, M., Eslamlou, A.D. and Pekcan, G. (2020), "Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review", Sensors, 20(10), 2778. https://doi.org/10.3390/s20102778.
  3. Baral, S., Negi, P., Adhikari, S. and Bhalla, S. (2023), "Temperature compensation for reusable piezo configuration for condition monitoring of metallic structures: EMI approach", Sensors, 23(3), 1587. https://doi.org/10.3390/s23031587.
  4. Bhalla, S. and Soh, C.K. (2004), "Structural health monitoring by piezo-impedance transducers. I: modeling", J. Aerosp. Eng., 17(4), 154-165. https://doi.org/10.1061/(ASCE)0893-1321(2004)17:4(154).
  5. de Rezende, S.W.F., de Moura, J.D.R.V., Neto, R.M.F., Gallo, C.A. and Steffen, V. (2020), "Convolutional neural network and impedance-based SHM applied to damage detection", Eng. Res. Express, 2(3), 035031. https://doi.org/10.1088/2631-8695/abb568.
  6. Du, F., Wu, S., Xu, C., Yang, Z. and Su, Z. (2021), "Electromechanical impedance temperature compensation and bolt loosening monitoring based on modified Unet and multitask learning", IEEE Sensors J., 23(5), 4556-4567. https://doi.org/10.1109/JSEN.2021.3132943.
  7. Hou, R. and Xia, Y. (2021), "Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019", J. Sound Vib., 491, 115741. https://doi.org/10.1016/j.jsv.2020.115741.
  8. Huynh, T.C., Dang, N.L. and Kim, J.T. (2018), "PCA-based filtering of temperature effect on impedance monitoring in prestressed tendon anchorage", Smart Struct. Syst., 22(1), 57-70. https://doi.org/10.12989/sss.2018.22.1.057.
  9. Kingma, D.P. and Ba, J. (2014), "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980.
  10. Koo, K.Y., Park, S., Lee, J.J. and Yun, C.B. (2009), "Automated iImpedance-based structural health monitoring incorporating effective frequency shift for compensating temperature effects", J. Intell. Mater. Syst. Struct., 20(4), 367-377. https://doi.org/10.1177/1045389X08088664.
  11. Li, H., Ai, D., Zhu, H. and Luo, H. (2021), "Integrated electromechanical impedance technique with convolutional neural network for concrete structural damage quantification under varied temperatures", Mech. Syst. Signal Pr., 152, 107467. https://doi.org/10.1016/j.ymssp.2020.107467.
  12. Lu, R., Shen, Y., Zhang, B. and Xu, W. (2023), "Nonlinear Electro-Mechanical Impedance Spectroscopy for fatigue crack monitoring", Mech. Syst. Signal Pr., 184, 109749. https://doi.org/10.1016/j.ymssp.2022.109749.
  13. Morwal, T., Bansal, T., Azam, A. and Talakokula, V. (2023), "Monitoring chloride-induced corrosion in metallic and reinforced/prestressed concrete structures using piezo sensors-based electro-mechanical impedance technique: A review", Measurement, 218, 113102.7. https://doi.org/10.1016/j.measurement.2023.113102.
  14. Na, W.S. and Baek, J. (2018), "A review of the piezoelectric electromechanical impedance based structural health monitoring technique for engineering structures", Sensors, 18(5), 1307. https://doi.org/10.3390/s18051307.
  15. Parida L., Moharana, S., Ferreira, V.M., Giri, S.K. and Ascensao, G. (2022), "A novel CNN-LSTM hybrid model for prediction of electro-mechanical impedance signal based bond strength monitoring", Sensors, 22(24), 9920. https://doi.org/10.3390/s22249920.
  16. Park, G., Sohn, H. and Farrar, C.R. (2003), "Overview of piezoelectric impedance-based health monitoring and path forward", Shock Vib. Digest, 35(6), 451-464. https://doi.org/10.1177/05831024030356001
  17. Park, G., Kabeya, K., Cudney, H.H. and Inman, D.J. (1999), "Impedance-based structural health monitoring for temperature varying applications", JSME Int. J., 42(2), 249-258. https://doi.org/10.1299/jsmea.42.249.
  18. Qiu, H. and Li, F. (2022), "Bolt looseness monitoring based on damping measurement by using a quantitative electro-mechanical impedance method", Smart Mater. Struct., 31(9), 095022. https://doi.org/10.1088/1361-665X/ac80e1.
  19. Wan, H.P. and Ni, Y Q. (2019), "Bayesian multi-task learning methodology for reconstruction of structural health monitoring data", Struct. Health Monit., 18(4), 1282-1309. https://doi.org/10.1177/147592171879495.
  20. Wan, H.P. and Ni, Y.Q. (2018), "Bayesian modeling approach for forecast of structural stress response using structural health monitoring data", J. Struct. Eng., 144(9), 04018130. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002085.
  21. Wan, H.P., Zhu, Y.K., Luo, Y. and Todd, M.D. (2024), "Unsupervised deep learning approach for structural anomaly detection using probabilistic features", Struct. Health Monit., https://doi.org/10.1177/14759217241226804.
  22. Zhao, S., Fan, S., Yang, J. and Kitipornchai, S. (2023), "Numerical and experimental investigation of electro-mechanical impedance based concrete quantitative damage assessment", Smart Mater. Struct., 29(5), 055025. https://doi.org/10.1088/1361-665X/ab58e9.