Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Structural damage detection by principle component analysis of long-gauge dynamic strains

  • Xia, Q. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Tian, Y.D. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Zhu, X.W. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Xu, D.W. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Zhang, J. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University)
  • Received : 2014.12.16
  • Accepted : 2015.02.27
  • Published : 2015.04.25
⟨ Previous Next ⟩

Abstract

A number of acceleration-based damage detection methods have been developed but they have not been widely applied in engineering practices because the acceleration response is insensitive to minor damage of civil structures. In this article, a damage detection approach using the long-gauge strain sensing technology and the principle component analysis technology is proposed. The Long gauge FBG sensor has its special merit for damage detection by measuring the averaged strain over a long-gauge length, and it can be connected each other to make a distributed sensor network for monitoring the large-scale civil infrastructure. A new damage index is defined by performing the principle component analyses of the long-gauge strains measured from the intact and damaged structures respectively. Advantages of the long gauge sensing and the principle component analysis technologies guarantee the effectiveness for structural damage localization. Examples of a simple supported beam and a steel stringer bridge have been investigated to illustrate the successful applications of the proposed method for structural damage detection.

Keywords

References

  1. Adewuyi, A.P. and Wu, Z.S. (2011), "Modal macro-strain flexibility methods for damage localization in flexural structures using long-gage FBG sensors Struct", Control Hlth. Monit., 18, 341-60. https://doi.org/10.1002/stc.377
  2. Aditi, M., Ambar, De., Damodar, M. and Dipak Kumar, M. (2013), "Damage assessment of beams from changes in natural frequencies using ant colony optimization", Struct. Eng. Mech., 45(3), 391-410. https://doi.org/10.12989/sem.2013.45.3.391
  3. Alvandi, A. and Cremona, C. (2006), "Assessment of vibration-based damage identification techniques", J. Sound Vib., 292, 179-202. https://doi.org/10.1016/j.jsv.2005.07.036
  4. An, Y. and Ou, J. (2012), "Experimental and numerical studies on damage localization of simply supported beams based on curvature difference probability method of waveform fractal dimension", J. Intell. Mater. Syst. Struct., 23, 415-26. https://doi.org/10.1177/1045389X11434172
  5. Bagchi, A., Humar, J., Xu, P. and Noman, A.S. (2010), "Model-based damage identification in a continuous bridge using vibration data", J. Perform. Constr. Facil., 24(2), 148-158. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000071
  6. Catbas, F.N., Brown, D.L. and Aktan, A.E. (2006), "Use of modal flexibility for damage detection and condition assessment: Case study and demonstrations on Large Structures", J. Struct. Eng., 132(11), 1699-1712. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:11(1699)
  7. Conte, J.P., He, X.F., Moaveni, B., Masri, S.F., Caffery, J. P., Wahbeh, M., Tasbihgoo, F., Whang, D.H. and Elgamal, A. (2008), "Dynamic testing of Alfred Zampa Memorial Bridge", J. Struc. Eng., 134(6), 1006-1015. https://doi.org/10.1061/(ASCE)0733-9445(2008)134:6(1006)
  8. Ratcliffe, C.P. (1997), "Damage detection using a modified Laplacian operator on mode shape data", J. Sound Vib., 204(3), 505-517. https://doi.org/10.1006/jsvi.1997.0961
  9. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A Summary of Vibration-based damage identification methods", Shock Vib. Dig., 30, 91-105. https://doi.org/10.1177/058310249803000201
  10. Guan, H. and Karbhari, V.M. (2008), "Improved damage detection method based on element modal strain damage index using sparse measurement", J. Sound Vib., 309, 465-94. https://doi.org/10.1016/j.jsv.2007.07.060
  11. He, X.F., Moaveni, B., Conte, J.P., Elgamal, A. and Masri, S.F. (2009), "System identification of Alfred Zampa Memorial Bridge using dynamic field test data", J. Struct. Eng., 135(1), 54-66. https://doi.org/10.1061/(ASCE)0733-9445(2009)135:1(54)
  12. Santos, J.P., Orcesi, A.D., Cremona, C. and Silveira, P. (2015), "Baseline-free real-time assessment of structural changes", Struct. Infrastr. Eng., Main. Manag. Life-Cycl. Des. Perform., 11(2), 145-161.
  13. Kazemi, S., Fooladi, A. and Rahai, A.R. (2010), "Implementation of the modal flexibility variation to fault identification in thin plates", Acta Astronaut., 66, 414-26. https://doi.org/10.1016/j.actaastro.2009.06.020
  14. Lei, Y., Jiang, Y.Q. and Xu, Z.Q. (2012), "Structural damage detection with limited input and output measurement signals", Mech. Syst. Signal Pr., 28, 229-243. https://doi.org/10.1016/j.ymssp.2011.07.026
  15. Lei, Y., Wu Y. and Li, T. (2012), "Identification of nonlinear structural parameters under limited input and output measurements", Int. J. Nonlin. Mech., 47, 1141-1146. https://doi.org/10.1016/j.ijnonlinmec.2011.09.004
  16. Li, S.Z. and Wu, Z.S. (2007), "Development of distributed long-gage fiber optic sensing system for structural health monitoring", Struc. Health Monit, 6(2), 133-143. https://doi.org/10.1177/1475921706072078
  17. Mehmet, E.A. and Durmus, A. (2014), "Modal parameter identification of in-filled RC frames with low strength concrete using ambient vibration", Struct. Eng. Mech., 50(2), 137-149. https://doi.org/10.12989/sem.2014.50.2.137
  18. Yoon, M.K., Heider, D., Gillespie Jr., J.W., Ratcliffe, C.P. and Crane, R.M.(2005), "Local damage detection using the two-dimensional gapped smoothing method", J. Sound Vib., 279 ,119-139. https://doi.org/10.1016/j.jsv.2003.10.058
  19. Montalvao, D., Mafia, N.M. M. and Ribeiro, A. M. R. (2006), "A review of vibration-based structural health monitoring with special emphasis on composite materials", Shock Vib. Dig., 38, 295-234. https://doi.org/10.1177/0583102406065898
  20. Rezaiee-Pajand, M. and Kazemiyan, M.S. (2014), "Damage identification of 2D and 3D trusses by using complete and incomplete noisy measurements", Struct. Eng. Mech., 52, 149-172. https://doi.org/10.12989/sem.2014.52.1.149
  21. Ni, Y.Q., Zhou, X.T. and Ko, J.M. (2006), "Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks", J. Sound Vib., 290, 242-263. https://doi.org/10.1016/j.jsv.2005.03.016
  22. Yazdanpanah, O., Seyedpoor, S.M. and Akbarzadeh Bengar, H. (2015), "A new damage detection indicator for beams based on mode shape data", and 53(4), 725-744. https://doi.org/10.12989/sem.2015.53.4.725
  23. Rucevskis, S. and Wesolowski, M. (2010), "Identification of damage in abeam structure by using modal shape curvature squares", Shock Vib., 17, 601-10. https://doi.org/10.1155/2010/729627
  24. Zhang, J., Prader, J., Grimmelsman, K.A., Moon, F., Aktan, A.E. and Shama, A. (2013), "Experimental vibration analysis for structural identification of a Long-Span suspension bridge", J. Eng. Mech., 139(6), 748-759. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000416
  25. Zhang, J., Hong, W., Tang, Y.S., Yang, C.Q., Wu, Q. and Wu. Z.S. (2014), "Structural health monitoring of a steel stringer bridge with area sensing", Struct. Eng. Mech., 10(8), 1049-1058.
  26. Zou, Y., Tong, L. and Steven, G.P. (2000), "Vibration based model-dependent damage (delamination) identification and health monitoring for composite structures-a review", J. Sound Vib., 230, 357-378. https://doi.org/10.1006/jsvi.1999.2624

Cited by

  1. Covariance of dynamic strain responses for structural damage detection vol.95, 2017, https://doi.org/10.1016/j.ymssp.2017.03.020
  2. Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network pp.1868-5145, 2018, https://doi.org/10.1007/s12652-018-0997-7
  3. Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition vol.26, pp.2, 2018, https://doi.org/10.1002/stc.2288