DOI QR코드

DOI QR Code

Hierarchical neural network for damage detection using modal parameters

  • Chang, Minwoo (Northern Railroad Research Center, Korea Railroad Research Institute) ;
  • Kim, Jae Kwan (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Lee, Joonhyeok (Infrastructure ENG Team, Samsung C&T Corporation)
  • Received : 2018.08.28
  • Accepted : 2019.04.13
  • Published : 2019.05.25

Abstract

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

Keywords

Acknowledgement

Supported by : Seoul National University

References

  1. Ali, J.B., Fnaiech, N., Saidi, L., Chebel-Morello, B. and Fnaiech, F. (2015), "Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals", Appl. Acoust., 89, 16-27. https://doi.org/10.1016/j.apacoust.2014.08.016.
  2. Allemang, R.J. (2003), "The modal assurance criterion-twenty years of use and abuse", Sound Vib., 37(8), 14-23.
  3. Bakhary, N., Hao, H. and Deeks, A.J. (2010), "Structure damage detection using neural network with multi-stage substructuring", Adv. Struct. Eng., 13(1), 95-110. https://doi.org/10.1260/1369-4332.13.1.95
  4. Cha, Y.J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput. Aided Civ. Infrastruct. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263.
  5. Chang, M. (2018), "Application studies on structural modal identification toolsuite for seismic response of shear frame structure", J. Earthq. Eng. Soc. Korea, 22(3), 201-210. https://doi.org/10.5000/EESK.2018.22.3.201
  6. Chang, M., Maguire, M. and Sun, Y. (2017), "Framework for mitigating human bias in selection of explanatory variables for bridge deterioration modeling", J. Infrastruct. Sys., 23(3), 04017002. https://doi.org/10.5000/EESK.2018.22.3.201.
  7. Chang, M. and Pakzad, S.N. (2014), "Observer Kalman filter identification for output-only systems using interactive structural modal identification toolsuite", J. Bridge Eng., 19(5), 04014002. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000530.
  8. Chang, P.C., Flatau, A. and Liu, S.C. (2003), "Health monitoring of civil infrastructure", Struct. Health Monit., 2(3), 257-267. https://doi.org/10.1177/1475921703036169.
  9. Chen, J.D. and Loh, C.H. (2018), "Two-stage damage detection algorithms of structure using modal parameters identified from recursive subspace identification", Earthq. Eng. Struct. Dyn., 47(3), 573-593. https://doi.org/10.1002/eqe.2980.
  10. Cho, H.N., Choi, Y.M., Lee, S.C., and Hur, C.K. (2004), "Damage assessment of cable stayed bridge using probabilistic neural network", Struct. Eng. Mech., 17(3-4), 483-492. http://doi.org/10.12989/sem.2004.17.3_4.483.
  11. Cornwell, P., Doebling, S.W. and Farrar, C.R. (1999), "Application of the strain energy damage detection method to plate like structures", J Sound Vib., 224(2), 359-374. https://doi.org/10.1006/jsvi.1999.2163.
  12. Dorafshan, S., Thomas, R.J. and Maguire, M. (2018), "Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete", Constr. Build. Mater., 186, 1031-1045. https://doi.org/10.1016/j.conbuildmat.2018.08.011.
  13. Dorvash, S. and Pakzad, S.N. (2012), "Effects of measurement noise on modal parameter identification", Smart Mater. Struct., 21(6), 065008. https://doi.org/10.1088/0964-1726/21/6/065008.
  14. Farreras-Alcover, I., Chryssanthopoulos, M.K. and Andersen, J.E. (2015), "Regression models for structural health monitoring of welded bridge joints based on temperature, traffic and strain measurements", Struct. Health Monit., 14(6), 648-662. https://doi.org/10.1177%2F1475921715609801. https://doi.org/10.1177%2F1475921715609801
  15. Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M. and Mohammadhassani, M. (2011), "Application of artificial neural networks to predict compressive strength of high strength concrete", Int. J. Phys. Sci., 6(5), 975-981. https://doi.org/10.5897/IJPS11.023.
  16. Hakim, S.J.S. and Razak, H.A. (2014), "Modal parameters based structural damage detection using artificial neural networks-a review", Smart Struct. Sys., 14(2), 159-189. http://dx.doi.org/10.12989/sss.2014.14.2.159.
  17. Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization algorithm for modal parameter identification and model reduction", J. Guid., Control, Dyn., 8(5), 620-627. https://doi.org/10.2514/3.20031.
  18. Ko, J.M., Sun, Z.G. and Ni, Y.Q. (2002), "Multi-stage identification scheme for detecting damage in cable-stayed Kap Shui Mun Bridge", Eng. Struct., 24(7), 857-868. https://doi.org/10.1016/S0141-0296(02)00024-X.
  19. Kondo, I. and Hamamoto, T. (1994), "Local damage detection of flexible offshore platforms using ambient vibration measurement", Int. Soc. Offshore Polar Eng., 4, 400-407.
  20. Lam, H.F., Ko, J.M. and Wong, C.W. (1998), "Localization of damaged structural connections based on experimental modal and sensitivity analysis", J. Sound Vib., 210(1), 91-115. https://doi.org/10.1006/jsvi.1997.1302.
  21. Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B. and Jung, H.Y. (2005), "Neural networks-based damage detection for bridges considering errors in baseline finite element models", J. Sound Vib., 280(3-5), 555-578. https://doi.org/10.1016/j.jsv.2004.01.003
  22. Levin, R.I. and Lieven, N.A.J. (1998), "Dynamic finite element model updating using neural networks", J. Sound Vib., 210(5), 593-607. https://doi.org/10.1006/jsvi.1997.1364.
  23. Limongelli, M.P. (2010), "Frequency response function interpolation for damage detection under changing environment", Mech. Sys. Signal Process., 24(8), 2898-2913. https://doi.org/10.1016/j.ymssp.2010.03.004.
  24. Magalhaes, F., Cunha, A. and Caetano, E. (2012), "Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection", Mech. Sys. Signal Process., 28, 212-228. https://doi.org/10.1016/j.ymssp.2011.06.011.
  25. Maguire, M., Roberts-Wollmann, C. and Cousins, T. (2018), "Live-load testing and long-term monitoring of the Varina-Enon Bridge: Investigating thermal distress", J. Bridg. Eng., 23(3), 04018003. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001200.
  26. Majumdar, A., De, A., Maity, D. and Maiti, D.K. (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.
  27. Nguyen, V.H., Schommer, S., Maas, S. and Zurbes, A. (2016), "Static load testing with temperature compensation for structural health monitoring of bridges", Eng. Struct., 127, 700-718. https://doi.org/10.1016/j.engstruct.2016.09.018.
  28. Pandey, A.K., Biswas, M. and Samman, M.M. (1991), "Damage detection from changes in curvature mode shapes", J. Sound Vib., 145(2), 321-332. https://doi.org/10.1016/0022-460X(91)90595-B.
  29. Park, J.H., Kim, J.T., Hong, D.S., Ho, D.D. and Yi, J.H. (2009), "Sequential damage detection approaches for beams using timemodal features and artificial neural networks", J. Sound Vib., 323(1-2), 451-474. https://doi.org/10.1016/j.jsv.2008.12.023.
  30. Qu, W.L., Chen, W., and Xiao, Y.Q. (2003), "A two-step approach for joint damage diagnosis of framed structures using artificial neural networks", Struct. Eng. Mech., 16(5), 581-595. http://doi.org/10.12989/sem.2003.16.5.581.
  31. Radzienski, M., Krawczuk, M. and Palacz, M. (2011), "Improvement of damage detection methods based on experimental modal parameters", Mech. Sys. Signal Process., 25(6), 2169-2190. https://doi.org/10.1016/j.ymssp.2011.01.007.
  32. 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.
  33. Salawu, O.S. (1997), "Detection of structural damage through changes in frequency: A review", Eng. Struct., 19(9), 718-723. https://doi.org/10.1016/S0141-0296(96)00149-6.
  34. Shahidi, S.G. and Pakzad, S.N. (2013), "Generalized response surface model updating using time domain data", J. Struct. Eng., 140(8), A4014001. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000915.
  35. Sohn, H. (2007), "Effects of environmental and operational variability on structural health monitoring", Philos. Trans. R. Soc. Lond. A: Math., Phys. Eng. Sci., 365(1851), 539-560. https://doi.org/10.1098/rsta.2006.1935.
  36. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R. and Czarnecki, J.J. (2003), "A review of structural health monitoring literature: 1996-2001", LA-UR-02-2095; Los Alamos National Laboratory, USA.
  37. Torres, V., Zolghadri, N., Maguire, M., Barr, P. and Halling, M. (2018), "Experimental and analytical investigation of live-load distribution factors for double tee bridges", J. Perform. Constr. Facil., 33(1), 04018107. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001259.
  38. Van Ooyen, A. and Nienhuis, B. (1992), "Improving the convergence of the back-propagation algorithm", Neural Netw., 5(3), 465-471. https://doi.org/10.1016/0893-6080(92)90008-7.
  39. Worden, K. and Manson, G. (2006), "The application of machine learning to structural health monitoring", Philos. Trans. Royal Soc. A: Math., Phys. Eng. Sci., 365(1851), 515-537. https://doi.org/10.1098/rsta.2006.1938.
  40. Wu, X., Ghaboussi, J. and Garrett Jr, J.H. (1992), "Use of neural networks in detection of structural damage", Comput. Struct., 42(4), 649-659. https://doi.org/10.1016/0045-7949(92)90132-J.
  41. Yazdanpanah, O., Seyedpoor, S.M., and Akbarzadeh Bengar, H. (2015). "A new damage detection indicator for beams based on mode shape data", Struct. Eng. Mech., 53(4), 725-744. http://doi.org/10.12989/sem.2015.53.4.725.
  42. Yoon, M.K., Heider, D., Gillespie Jr, J.W., Ratcliffe, C.P. and Crane, R.M. (2005), "Local damage detection using the twodimensional gapped smoothing method", J Sound Vib., 279(1-2), 119-139. https://doi.org/10.1016/j.jsv.2003.10.058.
  43. Yun, C.B., Yi, J.H. and Bahng, E.Y. (2001), "Joint damage assessment of framed structures using a neural networks technique", Eng. Struct., 23(5), 425-435. https://doi.org/10.1016/S0141-0296(00)00067-5.
  44. Zeiger, H.P. and McEwen, A. (1974), "Approximate linear realizations of given dimension via Ho's algorithm", IEEE Trans. on Autom. Control, 19(2), 153-153. http://doi.org/https://doi.org/10.1109/TAC.1974.1100525.
  45. Zolghadri, N., Halling, M.W. and Barr, P.J. (2016), "Effects of temperature variations on structural vibration properties", Geotechnical and Structural Engineering Congress 2016, 1032-1043, Phoenix, USA, February. https://doi.org/10.1061/9780784479742.087.

Cited by

  1. Damage identification using deep learning and long-gauge fiber Bragg grating sensors vol.59, pp.33, 2019, https://doi.org/10.1364/ao.405110