Structural Engineering and Mechanics

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Structural damage detection based on residual force vector and imperialist competitive algorithm

  • Ding, Z.H. (School of Engineering, Sun Yat-sen University) ;
  • Yao, R.Z. (School of Engineering, Sun Yat-sen University) ;
  • Huang, J.L. (School of Engineering, Sun Yat-sen University) ;
  • Huang, M. (School of Engineering, Sun Yat-sen University) ;
  • Lu, Z.R. (School of Engineering, Sun Yat-sen University)
  • Received : 2016.03.30
  • Accepted : 2017.06.02
  • Published : 2017.06.25
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Abstract

This paper develops a two-stage method for structural damage identification by using modal data. First, the Residual Force Vector (RFV) is introduced to detect any potentially damaged elements of structures. Second, data of the frequency domain are used to build up the objective function, and then the Imperialist Competitive Algorithm (ICA) is utilized to estimate damaged extents. ICA is a heuristic algorithm with simple structure, which is easy to be implemented and it is effective to deal with high-dimension nonlinear optimization problem. The advantages of this present method are: (1) Calculation complexity can be decreased greatly after eliminating many intact elements in the first step. (2) Robustness, ICA ensures the robustness of the proposed method. Various damaged cases and different structures are investigated in numerical simulations. From these results, anyone can point out that the present algorithm is effective and robust for structural damage identification and is also better than many other heuristic algorithms.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Guangdong Province Natural Science Foundation, China Scholarship Council (CSC)

References

  1. Atashpaz-Gargari, E. and Lucas, C. (2007), "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, 4661-4667.
  2. Au, F.T.K., Cheng, Y.S., Tham, L.G. and Bai, Z.Z. (2003), "Structural damage detection based on a micro-genetic algorithm using incomplete and noisy modal test data", J. Sound Vib., 259(5), 1081-1094. https://doi.org/10.1006/jsvi.2002.5116
  3. Dackermann, U.I., Smith, W.A. and Randall, R.B. (2014), "Damage identification based on response-only measurements using cepstrum analysis and artificial neural network", Struct. Hlth. Monit., 13(4), 430-444. https://doi.org/10.1177/1475921714542890
  4. Ding, Z.H., Lu, Z.R. and Huang, M. (2016), "Structural damage detection using artificial bee colony algorithm with hybrid search strategy", Swarm Evolut. Comput., 28, 1-13. https://doi.org/10.1016/j.swevo.2015.10.010
  5. Friswell, M.I. and Penny, J.E.T. (1998), "A combined genetic and eigensensitivity algorithm for the location of damage in structures", Comput. Struct., 6(9), 547-556.
  6. Gomes, H.M. and Silva, N.R.S. (2008), "Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method", Appl. Math. Model., 32(11), 2216-2232. https://doi.org/10.1016/j.apm.2007.07.002
  7. Gokdag, H. (2014), "A crack identification approach for beam-like structures under moving vehicle using particle swarm optimization", Mater. Test., 55(2), 114-120.
  8. Hassiotis, S. and Jeong, G.D. (1993), "Assessment of structural damage from natural frequency measurements", Comput. Struct., 49(4), 679-691. https://doi.org/10.1016/0045-7949(93)90071-K
  9. Kang, F., Li, J.J. and Xu, Q. (2012), "Damage detection based on improved particle swarm optimization using vibration data", Appl. Soft Comput., 12(8), 2329-2335. https://doi.org/10.1016/j.asoc.2012.03.050
  10. Khaled, A.A. and Hosseini, S. (2015), "Fuzzy adaptive imperialist competitive algorithm for global optimization", Neural Comput. Appl., 26(4), 813-825. https://doi.org/10.1007/s00521-014-1752-4
  11. Li, C. and Smith, W. (1995), "Hybrid approach for damage detection in flexible structure", J. Guidance, Control, D., 18(3), 419-425. https://doi.org/10.2514/3.21404
  12. Li, J., Hao, H. and Zhu, H.P. (2014), "Dynamic assessment of shear connectors in composite bridges with ambient vibration measurements", Adv. Struct. Eng., 17(5), 617-638. https://doi.org/10.1260/1369-4332.17.5.617
  13. Li, J., Hao, H. and Lo, J.V. (2015), "Structural damage identification with power spectral density transmissibility: numerical and experimental studies", Smart Struct. Syst., 15(1), 15-40. https://doi.org/10.12989/sss.2015.15.1.015
  14. Li, J., Hao, H. and Zhu, H.P. (2014), "Damage detection of shear connectors in bridge structures with transmissibility in frequency domain", Int. J. Struct. Stab. D., 14(1), 1350061. https://doi.org/10.1142/S0219455413500612
  15. Li, Z., Xia, S.M., Wang, J. and Su, X.Y. (2006), "Damage detection of cracked beams based on wavelet transform", Int. J. Impact Eng., 32(7), 1190-1200. https://doi.org/10.1016/j.ijimpeng.2004.09.012
  16. Lim., T.W. (1994), "Structural damage detection of space truss structures using best achievable eigenvectors", AIAA J., 32(5), 1049-1057. https://doi.org/10.2514/3.12093
  17. Lu, Z.R. and Law, S.S. (2007), "Features of dynamic response sensitivity and its application in damage detection", J. Sound Vib., 303(1),305-329. https://doi.org/10.1016/j.jsv.2007.01.021
  18. Ovanesova, A.V. and Suarez, L.E. (2004), "Applications of wavelet transforms to damage detection in frame structures", Eng. Struct., 26(1), 29-39.
  19. Parloo, E., Guillaume. P. and Overmeire, M.V. (2003), "Damage assessment using mode shape sensitivities", Mech. Syst. Sign. Proc., 17(3), 499-518. https://doi.org/10.1006/mssp.2001.1429
  20. Perry, M.J., Koh, C.G. and Choo, Y.S. (2006), "Modified genetic algorithm strategy for structural identification", Comput. Struct., 48(8-9), 529-540. https://doi.org/10.1016/j.compstruc.2005.11.008
  21. Ricles, J.M. and Kosmatka, J.B. (1992), "Damage detection in elastic structures using vibration residual forces and weighted sensitivity", AIAA J., 30(9), 2310-2316. https://doi.org/10.2514/3.11219
  22. Satpal, S.B., Guha, A. and Banerjee, S. (2016), "Damage identification in aluminum beams using supported vector machine: Numerical and experimental studies", Struct. Control Hlth. Monit., 23(3), 446-457. https://doi.org/10.1002/stc.1773
  23. Sheinman, I. (1996), "Damage detection and updating of stiffness and mass matrices using mode data", Comput. Struct., 59(1), 199-205.
  24. Xu, H.J., Ding, Z.H., Lu, Z.R. and Liu, J.K. (2015), "Structural damage detection based on chaotic artificial bee colony algorithm", Struct. Eng. Mech., 55(6), 1223-1239. https://doi.org/10.12989/sem.2015.55.6.1223
  25. Yang, Q.W. and Liu, J.K. (2006), "Structural damage identification based on residual force vector", J. Sound Vib., 305(1), 298-307. https://doi.org/10.1016/j.jsv.2007.03.033
  26. Zimmerman, D.C. and Kaouk, M. (1994), "Structure damage detection using a minimum rank updating theory", J. Vib. Acoust., 116, 222-253. https://doi.org/10.1115/1.2930416