Structural Engineering and Mechanics

  • 제75권4호
  • /
  • Pages.415-424
  • /
  • 2020
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Multi-strategy structural damage detection based on included angle of vectors and sparse regularization

  • Liu, Huanlin (MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University) ;
  • Yu, Ling (MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University) ;
  • Luo, Ziwei (MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University) ;
  • Chen, Zexiang (MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University)
  • 투고 : 2020.02.27
  • 심사 : 2020.05.30
  • 발행 : 2020.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, many structural damage detection (SDD) methods have been proposed to monitor the safety of structures. As an important modal parameter, mode shape has been widely used in SDD, and the difference of vectors was adopted based on sensitivity analysis and mode shapes in the existing studies. However, amplitudes of mode shapes in different measured points are relative values. Therefore, the difference of mode shapes will be influenced by their amplitudes, and the SDD results may be inaccurate. Focus on this deficiency, a multi-strategy SDD method is proposed based on the included angle of vectors and sparse regularization in this study. Firstly, inspired by modal assurance criterion (MAC), a relationship between mode shapes and changes in damage coefficients is established based on the included angle of vectors. Then, frequencies are introduced for multi-strategy SDD by a weighted coefficient. Meanwhile, sparse regularization is applied to improve the ill-posedness of the SDD problem. As a result, a novel convex optimization problem is proposed for effective SDD. To evaluate the effectiveness of the proposed method, numerical simulations in a planar truss and experimental studies in a six-story aluminum alloy frame in laboratory are conducted. The identified results indicate that the proposed method can effectively reduce the influence of noises, and it has good ability in locating structural damages and quantifying damage degrees.

키워드

과제정보

The project is jointly supported by the National Natural Science Foundation of China with Grant Numbers 51678278 and 51278226.

참고문헌

  1. Alkayem, N.F., Cao, M., Zhang, Y., Bayat, M. and Su, Z. (2018), "Structural damage detection using finite element model updating with evolutionary algorithms: a survey", Neural Comput. Appl., 30(2), 389-411. https://doi.org/10.1007/s00521-017-3284-1.
  2. Allemang, R.J. (2003), "The modal assurance criterion - Twenty years of use and abuse", Sound Vib., 37(8), 14-23.
  3. Bagherahmadi, S.A. and Seyedpoor, S.M. (2018), "Structural damage detection using a damage probability index based on frequency response function and strain energy concept", Struct. Eng. Mech., 67(4), 327-336. https://doi.org/10.12989/sem.2018.67.4.327.
  4. Beck, A. and Teboulle, M. (2009), "A fast iterative shrinkage-thresholding algorithm for linear inverse problems", SIAM J. Imaging Sci., 2(1), 183-202. https://doi.org/10.1137/080716542.
  5. Cawley, P. and Adams, R.D. (1979), "The location of defects in structures from measurements of natural frequencies", J. Strain Anal. Eng. Des., 14(2), 49-57. https://doi.org/10.1243/03093247V142049.
  6. Cha, Y.J. and Buyukozturk, O. (2015), "Structural damage detection using modal strain energy and hybrid multiobjective optimization", Comput.-Aided Civ. Inf., 30(5), 347-358. https://doi.org/10.1111/mice.12122.
  7. Chang, M., Kim, J.K. and Lee, J. (2019), "Hierarchical neural network for damage detection using modal parameters", Struct. Eng. Mech., 70(4), 457-466. https://doi.org/10.12989/sem.2019.70.4.457.
  8. Esfandiari, A., Chaei, M.G. and Rofooei, F.R. (2018), "A structural model updating method using incomplete power spectral density function and modal data", Struct. Eng. Mech., 68(1), 39-51. https://doi.org/10.12989/sem.2018.68.1.039.
  9. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: A review and comparative study", Struct. Hlth. Monit., 10(1), 83-111. https://doi.org/10.1177/1475921710365419.
  10. Friswell, M.I. (2007), "Damage identification using inverse methods", Philos. T. R. Soc. A, 365(1851), 393-410. https://doi.org/10.1098/rsta.2006.1930.
  11. Hou, R., Xia, Y. and Zhou, X. (2018), "Structural damage detection based on l1 regularization using natural frequencies and mode shapes", Struct. Control Hlth. Monit., 25, e2107. https://doi.org/10.1002/stc.2107.
  12. Huang, Q., Xu, Y.L., Li, J.C., Su, Z.Q. and Liu, H.J. (2012), "Structural damage detection of controlled building structures using frequency response functions", J. Sound Vib., 331(15), 3476-3492. https://doi.org/10.1016/j.jsv.2012.03.001.
  13. Koutsovasilis, P. and Beitelschmidt, M. (2007), "Model reduction comparison for the elastic crankshaft mechanism". Proceedings of the 2nd International Operational Modal Analysis Conference, Copenhagen, Denmark, April.
  14. Lee, U. and Shin, J. (2002), "A frequency response function-based structural damage identification method", Comput. Struct., 80(2), 117-132. https://doi.org/10.1016/S0045-7949(01)00170-5.
  15. Li, X.Y., Wang, L.X., Law, S.S. and Nie, Z.H. (2017), "Covariance of dynamic strain responses for structural damage detection", Mech. Syst. Signal Pr., 95, 90-105. https://doi.org/10.1016/j.ymssp.2017.03.020.
  16. Li, Y. and Chen, Y. (2013), "A review on recent development of vibration-based structural robust damage detection", Struct. Eng. Mech., 45(2), 159-168. https://doi.org/10.12989/sem.2013.45.2.159.
  17. Liddle, A.R. (2007), "Information criteria for astrophysical model selection", Mon. Not. R. Astron. Soc., 377(1), L74-L78. https://doi.org/10.1111/j.1745-3933.2007.00306.x.
  18. Liu, H.L., Yu, L., Luo, Z.W. and Pan, C.D. (2020) "Compressed sensing for moving force identification using redundant dictionaries", Mech. Syst. Signal Pr., 138, 106535. https://doi.org/10.1016/j.ymssp.2019.106535.
  19. Liu, L., Hua, W. and Lei, Y. (2018), "Real-time simultaneous identification of structural systems and unknown inputs without collocated acceleration measurements based on MEKF-UI", Measurement., 122, 545-553. https://doi.org/10.1016/j.measurement.2017.07.001.
  20. Moughty, J.J. and Casas, J.R. (2017), "A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions", Appl. Sci., 7(5), 510. https://doi.org/10.3390/app7050510.
  21. O'Callahan, J. (1989), "System equivalent reduction expansion process (SEREP)". Proceedings of the 7th International Modal Analysis Conference, Las Vegas, U.S.A., February.
  22. 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.
  23. 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.
  24. Shi, Z.Y., Law, S.S. and Zhang, L.M. (1998), "Structural damage localization from modal strain energy change", J. Sound Vib., 218(5), 825-844. https://doi.org/10.1006/jsvi.1998.1878.
  25. Vahidi, M., Vahdani, S., Rahimian, M., Jamshidi, N. and Kanee, A.T. (2019), "Evolutionary-base finite element model updating and damage detection using modal testing results", Struct. Eng. Mech., 70(3), 339-350. https://doi.org/10.12989/sem.2019.70.3.339.
  26. Yan, W.J. and Katafygiotis, L.S. (2019), "An analytical investigation into the propagation properties of uncertainty in a two-stage fast Bayesian spectral density approach for ambient modal analysis", Mech. Syst. Signal Pr., 118, 503-533. https://doi.org/10.1016/j.ymssp.2018.08.047.
  27. Yan, W.J., Zhao, M.Y., Sun, Q. and Ren, W.X. (2019), "Transmissibility-based system identification for structural health monitoring: Fundamentals, approaches, and applications", Mech. Syst. Signal Pr., 117, 453-482. https://doi.org/10.1016/j.ymssp.2018.06.053.
  28. Yan, Y.J., Cheng, L., Wu, Z.Y. and Yam, L.H. (2007), "Development in vibration-based structural damage detection technique", Mech. Syst. Signal Pr., 21(5), 2198-2211. https://doi.org/10.1016/j.ymssp.2006.10.002.
  29. Zhang, C.D. and Xu, Y.L. (2016), "Comparative studies on damage identification with Tikhonov regularization and sparse regularization", Struct. Control Hlth. Monit., 23(3), 560-579. https://doi.org/10.1002/stc.1785.
  30. Zhao, J. and DeWolf, J.T. (1999), "Sensitivity study for vibrational parameters used in damage detection", J. Struct. Eng., 125(4), 410-416. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:4(410).
  31. Zhou, X.Q., Xia, Y. and Weng, S. (2015), "$L_1$ regularization approach to structural damage detection using frequency data", Struct. Hlth. Monit., 14(6), 571-582. https://doi.org/10.1177/1475921715604386.
  32. Zhou, Y.L. and Wahab, M.A. (2017), "Cosine based and extended transmissibility damage indicators for structural damage detection", Eng. Struct., 141, 175-183. https://doi.org/10.1016/j.engstruct.2017.03.030.