Structural Monitoring and Maintenance

Techno-Press (테크노프레스)
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))
  • Received : 2022.04.14
  • Accepted : 2022.07.18
  • Published : 2022.09.25
⟨ Previous Next ⟩

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

Acknowledgement

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

References

  1. Ai, D., Luo, H., Wang, C. and Zhu, H. (2018), "Monitoring of the load-induced RC beam structural tension/compression stress and damage using piezoelectric transducers", Eng. Struct., 154, 38-51. https://doi.org/10.1016/j.engstruct.2017.10.046
  2. 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 Process., 147(107077), 1-45. https://doi.org/10.1016/j.ymssp.2020.107077
  3. Fan, X., Li, J. and Hao, H. (2021), "Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods", Adv. Struct. Eng., 24(16), 3609-3626. https://doi.org/10.1177/13694332211038444
  4. Farrar, C.R. (2001), "Historical overview of structural health monitoring", Lecture Notes on Structural Health Monitoring Using Statistical Pattern Recognition; Los Alamos Dynamics, Los Alamos, NM, USA.
  5. Giurgiutiu, V. and Zagrai, A. (2005), "Damage detection in thin plates and aerospace structures with the electro-mechanical impedance method", Struct. Health Monitor., 4(2), 99-118. https://doi.org/10.1177/1475921705049752
  6. Ho, D.D., Ngo, T.M. and Kim, J.T. (2014), "Impedance-based damage monitoring of steel column connection: numerical simulation", Struct. Monitor. Maint., Int. J., 1(3), 339-356. https://doi.org/10.12989/smm.2014.1.3.339
  7. Ho, D.D., Huynh, T.C., Luu, T.H.T. and Le, T.C. (2021), "Electro-mechanical impedance-based prestress force monitoring in prestressed concrete structures", In: Structural Health Monitoring and Engineering Structures, 148, 413-423. https://doi.org/10.1007/978-981-16-0945-9_33
  8. Huynh, T.C., Dang, N.L. and Kim, J.T. (2017), "Advances and challenges in impedance-based structural health monitoring", Struct. Monitor. Maint., Int. J., 4(4), 301-329. https://doi.org/10.12989/smm.2017.4.4.301
  9. Lee, J.J., Yun, C.B., Lee, J.W. and Jung, H.Y. (2005), "Neutral network-based damage detection for bridges considering errors in baseline finite element model", J. Sound Vib., 280, 555-578. https://doi.org/10.1016/j.jsv.2004.01.003
  10. Li, H.N., Yi, T.H., Ren, L., Li, D.S. and Huo, L.S. (2014), "Reviews on innovations and applications in structural health monitoring for infrastructures", Struct. Monitor. Maint., Int. J., 1(1), 1-45. https://doi.org/10.12989/smm.2014.1.1.001
  11. Liang, C., Sun, F.P. and Rogers, C.A. (1994), "Coupled electro-mechanical analysis of adaptive material systems-determination of the actuator power consumption and system energy transfer", J. Intell. Mater. Syst. Struct., 5, 12-20. https://doi.org/10.1177/1045389X9400500102
  12. Liu, X. and Jiang, Z. (2009), "Design of a PZT patch for measuring longitudinal mode impedance in the assessment of truss structure damage", Smart Mater. Struct., 18(125017). https://doi.org/10.1088/0964-1726/18/12/125017
  13. Min, J., Park, S., Yun, C.B., Lee, C.G. and Lee, C. (2012), "Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity", Eng. Struct., 39, 210-220. https://doi.org/10.1016/j.engstruct.2012.01.012
  14. Nguyen, T.T., Phan, T.T.V., Ho, D.D., Pradhan, A.M.S, and Huynh, T.C. (2022), "Deep learning-based autonomous damage-sensitive feature extraction for impedance-based prestress monitoring", Eng. Struct., 259(114172), 1-18. https://doi.org/10.1016/j.engstruct.2022.114172
  15. Park, G., Sohn, H., Farrar, C. and Inman, D. (2003), "Overview of piezoelectric impedance-based health monitoring and path forward", Shock Vib. Digest, 35, 451-463. https://doi.org/10.1177/05831024030356001
  16. Park, S., Ahmad, S., Yun, C.B. and Roh, Y. (2006), "Multiple crack detection of concrete structures using impedance-based structural health monitoring techniques", Experim. Mech., 46(5), 609-618. https://doi.org/10.1007/s11340-006-8734-0
  17. Rao, A.S., Nguyen, T., Palaniswami, M. and Ngo, T. (2021), "Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure", Struct. Health Monitor., 20(4), 2124-2142. https://doi.org/10.1177/1475921720965445
  18. Ryu, J.Y., Huynh, T.C. and Kim, J.T. (2017), "Experimental investigation of magnetic-mount PZT-interface for impedance-based damage detection in steel girder connection", Struct. Monitor. Maint., Int. J., 4(3), 237-253. https://doi.org/10.12989/smm.2017.4.3.237
  19. Sun, F.P., Chaudhry, Z., Liang, C. and Rogers, C.A. (1995), "Truss structure integrity identification using PZT sensor-actuator", J. Intell. Mater. Syst. Struct., 6, 134-139. https://doi.org/10.1177/1045389X9500600117
  20. Yao, Y., Tung, S.T.E. and Glisic, B. (2014), "Crack detection and characterization techniques-An overview", Struct. Control Health Monitor., 21(12), 1387-1413. https://doi.org/10.1002/stc.1655
  21. Zuo, C., Feng, X., Zhang, Y., Lu, L. and Zhou, J. (2017), "Crack detection in pipelines using multiple electromechanical impedance sensors", Smart Mater. Struct., 26(10). https://doi.org/10.1088/1361-665X/aa7ef