DOI QR코드

DOI QR Code

Damage detection for a beam under transient excitation via three different algorithms

  • Zhao, Ying (International Institute for Urban Systems Engineering, Southeast University) ;
  • Noori, Mohammad (International Institute for Urban Systems Engineering, Southeast University) ;
  • Altabey, Wael A. (International Institute for Urban Systems Engineering, Southeast University)
  • 투고 : 2016.12.27
  • 심사 : 2017.10.11
  • 발행 : 2017.12.25

초록

Structural health monitoring has increasingly been a focus within the civil engineering research community over the last few decades. With increasing application of sensor networks in large structures and infrastructure systems, effective use and development of robust algorithms to analyze large volumes of data and to extract the desired features has become a challenging problem. In this paper, we grasp some precautions and key points of the signal processing approach, wavelet, establish a relative reliable framework, and analyze three problems that require attention when applying wavelet based damage detection approach. The cases studies how to use optimal scales for extracting mode shapes and modal curvatures in a reinforced concrete beam and how to effectively identify damages using maximum curves of wavelet coefficient differences. Moreover, how to make a recognition based on the wavelet multi-resolution analysis, wavelet packet energy, and fuzzy sets is a meaningful topic that has been addressed in this work. The relative systematic work that compasses algorithms, structures and evaluation paves a way to a framework regarding effective structural health monitoring, orientation, decision and action.

키워드

참고문헌

  1. Bedworth, M. and O'Brien, J. (2000), "The omnibus model: a new model of data fusion?", IEEE Aerosp. Elec. Syst. Mag., 15(4), 30-6. https://doi.org/10.1109/62.839632
  2. Beheshti-Aval, S.B., Taherinasab, M. and Noori, M. (2013), "Some precautions to consider in using wavelet transformation for damage detection analysis of plates", Smart Struct. Syst., 11(1), 35-51. https://doi.org/10.12989/sss.2013.11.1.035
  3. Cao, M., Radzienski, M., Xu, W. and Ostachowicz, W. (2014), "Identification of Multiple Damage in Beams Based on Robust Curvature Mode Shapes", J. Mech. Syst. Signal Pr., 46(2), 468-480. https://doi.org/10.1016/j.ymssp.2014.01.004
  4. Cao, M.S., Xu, W., Ren, W.X., Ostachowicz, W., Sha, G.G. and Pan, L.X. (2016), "A concept of complex-wavelet modal curvature for detecting multiple cracks in beams under noisy conditions", J. Mech. Syst. Signal Pr., 76, 555-575.
  5. Chang, P.C., Flatau, A. and Liu, S.C. (2003), "Health monitoring of civil infrastructure", J. Struct. Hlth. Monit., 2(3), 257-67. https://doi.org/10.1177/1475921703036169
  6. Daubechies, I. (1990), "The wavelet transform, time-frequency localization and signal analysis", IEEE Tran. Inform. Theory, 36(5), 961-1005. https://doi.org/10.1109/18.57199
  7. Daubechies, I. (1992), Ten Lectures on Wavelets. Philadelphia: Society for Industrial and Applied Mathematics, Rutgers University.
  8. Dawari, V.B. and Vesmawala, G.R. (2013), "Structural damage identification using modal curvature differences", J. Mech. Civil Eng., 4, 33-38.
  9. Douka, E., Loutridis, S. and Trochidis, A. (2003), "Crack identification in beams using wavelet analysis", J. Solid. Struct., 40(13), 3557-3569. https://doi.org/10.1016/S0020-7683(03)00147-1
  10. Escamilla-Ambrosio, P.J., Liu, X., Lieven, N.A. and Ramirez-Cortes JM. (2011), "ANFIS-2D wavelet transform approach to structural damage identification", Fuzzy Information Processing Society (NAFIPS), IEEE, 2011 Annual Meeting of the North American, March..
  11. Farouk, M.H. (2014), "Wavelets, wavelet filters, and wavelet transforms", Appl. Wavel. Speech Pr., https://doi.org/10.1007/978-3-319-02732-6_3.
  12. Guo, H., Lin, J. and Li, Z. (2012), "Structural damage localization of steel arch bridge based on wavelet packet transform", Software Engineering and Knowledge Engineering: Theory and Practice 2012, Springer Berlin Heidelberg.
  13. Hera, A. and Hou, Z. (2004), "Application of wavelet approach for ASCE structural health monitoring benchmark studies", J. Eng. Mech., 130(1), 96-104. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(96)
  14. Hong, J.C., Kim, Y.Y., Lee, H.C. and Lee, Y.W. (2002), "Damage detection using the Lipschitz exponent estimated by the wavelet transform: applications to vibration modes of a beam", J. Solid. Struct., 39(7), 1803-1816. https://doi.org/10.1016/S0020-7683(01)00279-7
  15. Hou, Z., Noori, M. and Amand, R.S. (2000), "Wavelet-based approach for structural damage detection", J. Eng. Mech., 126(7), 677-683. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(677)
  16. Huang, Y., Meyer, D. and Nemat-Nasser S. (2009), "Damage detection with spatially distributed 2D continuous wavelet transform", J. Mech. Mater., 41(10), 1096-1107. https://doi.org/10.1016/j.mechmat.2009.05.006
  17. Jiang, X. and Mahadevan, S. (2008), "Bayesian probabilistic inference for nonparametric damage detection of structures", J. Eng. Mech., 134(10), 820-831. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:10(820)
  18. Jiang, X. and Mahadevan, S. (2008), "Bayesian wavelet methodology for structural damage detection", J. Struct. Control Hlth. Monit., 15(7), 974-991. https://doi.org/10.1002/stc.230
  19. Jiang, X., Mahadevan, S. and Adeli, H. (2007), "Bayesian wavelet packet denoising for structural system identification", J. Struct. Control Hlth. Monit., 14(2), 333-356. https://doi.org/10.1002/stc.161
  20. Lee, D.T. (1994), "Yamamoto a. wavelet analysis: theory and applications", Hewlett Pack. J., 1, 45-44.
  21. Loutridis, S., Douka, E. and Trochidis, A. (2004), "Crack identification in double-cracked beams using wavelet analysis", J. Sound Vib., 277(4), 1025-1039. https://doi.org/10.1016/j.jsv.2003.09.035
  22. Loutridis, S., Douka, E., Hadjileontiadis, L.J. and Trochidis, A.A. (2005), "Two-dimensional wavelet transform for detection of cracks in plates", J. Eng. Struct., 27(9), 1327-38. https://doi.org/10.1016/j.engstruct.2005.03.006
  23. Lucero, J. and Taha, M.R. (2005), "A wavelet aided fuzzy damage detection algorithm for structural health monitoring", Proceedings of the 23rd International. Modal Analysis Conference (IMAC), Orlando, Florida.
  24. Mallat, S. and Hwang, W.L. (1992), "Singularity detection and processing with wavelets", IEEE Tran. Inform. Theory, 38(2), 617-643. https://doi.org/10.1109/18.119727
  25. Mallat, S.A. (1999), Wavelet Tour of Signal Processing. Academic Press, 2nd Edition, Elsevier Inc.
  26. Masoumi, M.A. and Ashory, M.R. (2014), "Damage identification from uniform load surface using continuous and stationary wavelet transforms", Latin Am. J. Solid. Struct., 11(5), 738-754. https://doi.org/10.1590/S1679-78252014000500001
  27. Nikravesh, S.M. and Chegini, S.N. (2013), "Crack identification in double-cracked plates using wavelet analysis", Meccanica, 48(9), 2075-2098. https://doi.org/10.1007/s11012-013-9726-7
  28. Prasad, B.R., Lakshmanan, N., Muthumani, K. and Gopalakrishnan, N. (2006), "Enhancement of damage indicators in wavelet and curvature analysis", J. Sadhana, 31(4), 463-486. https://doi.org/10.1007/BF02716787
  29. Quek, S.T., Wang, Q., Zhang, L. and Ang, K.K. (2001), "Sensitivity analysis of crack detection in beams by wavelet technique", J. Mech. Sci., 43(12), 2899-2910. https://doi.org/10.1016/S0020-7403(01)00064-9
  30. Reda Taha, M.M. (2010), "A neural-wavelet technique for damage identification in the ASCE benchmark structure using phase II experimental data", J. Adv. Civil Eng., 2010, ID 675927, 13.
  31. Rucka, M. and Wilde, K. (2006), "Crack identification using wavelets on experimental static deflection profiles", J. Eng. Struct., 28(2), 279-288. https://doi.org/10.1016/j.engstruct.2005.07.009
  32. Rucka, M.A. and Wilde, K.R. (2006), "Application of continuous wavelet transform in vibration based damage detection method for beams and plates", J. Sound Vib., 297(3), 536-50. https://doi.org/10.1016/j.jsv.2006.04.015
  33. Taha, M.R. and Lucero, J. (2005), "Damage identification for structural health monitoring using fuzzy pattern recognition", J. Eng. Struct., 27(12), 1774-1783. https://doi.org/10.1016/j.engstruct.2005.04.018
  34. Walia, S.K., Patel, R.K., Vinayak, H.K. and Parti, R. (2015), "Time-frequency and wavelet-based study of an old steel truss bridge before and after retrofitting", J. Civil Struct. Hlth. Monit., 5(4), 397-414. https://doi.org/10.1007/s13349-015-0116-9
  35. Wang, Q. and Deng, X. (1999), "Damage detection with spatial wavelets", , J. Solid. Struct., 36(23), 3443-3468. https://doi.org/10.1016/S0020-7683(98)00152-8
  36. Xu, W., Cao, M., Ostachowicz, W., Radzienski, M. and Xia, N. (2015), "Two-dimensional curvature mode shape method based on wavelets and teager energy for damage detection in plates", J. Sound Vib., 347, 266-78. https://doi.org/10.1016/j.jsv.2015.02.038
  37. Xu, Y.F., Zhu, W.D., Liu, J. and Shao, Y.M. (2014), "Identification of embedded horizontal cracks in beams using measured mode shapes", J. Sound Vib., 333(23), 6273-6294. https://doi.org/10.1016/j.jsv.2014.04.046
  38. Zhao, Y. and Noori, M. (2017), "Mode shape-based damage identification for a reinforced concrete beam using wavelet coefficient differences and multiresolution analysis", J. Struct. Control Hlth. Monit., DOI: 10.1002/stc.2041
  39. Zhu, F., Deng, Z. and Zhang, J. (2013), "An integrated approach for structural damage identification using wavelet neuro-fuzzy model", J. Expert Syst. Appl., 40(18), 7415-7427. https://doi.org/10.1016/j.eswa.2013.06.078

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