Structural Engineering and Mechanics

  • 제82권6호
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  • Pages.771-783
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Structural damage identification based on transmissibility assurance criterion and weighted Schatten-p regularization

  • Zhong, Xian (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)
  • 투고 : 2021.12.30
  • 심사 : 2022.04.05
  • 발행 : 2022.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Structural damage identification (SDI) methods have been proposed to monitor the safety of structures. However, the traditional SDI methods using modal parameters, such as natural frequencies and mode shapes, are not sensitive enough to structural damage. To tackle this problem, this paper proposes a new SDI method based on transmissibility assurance criterion (TAC) and weighted Schatten-p norm regularization. Firstly, the transmissibility function (TF) has been proved a useful damage index, which can effectively detect structural damage under unknown excitations. Inspired by the modal assurance criterion (MAC), TF and MAC are combined to construct a new damage index, so called as TAC, which is introduced into the objective function together with modal parameters. In addition, the weighted Schatten-p norm regularization method is adopted to improve the ill-posedness of the SDI inverse problem. To evaluate the effectiveness of the proposed method, some numerical simulations and experimental studies in laboratory are carried out. The results show that the proposed method has a high SDI accuracy, especially for weak damages of structures, it can precisely achieve damage locations and quantifications with a good robustness.

키워드

과제정보

This work was jointly sponsored by the National Natural Science Foundation of China (grant nos. 52178290 and 51678278).

참고문헌

  1. Albini, P., Toigo, A. and Umanita, V. (2008), "Relations between convergence rates in Schatten p-norms", J. Math. Phys., 49(1), 013504. https://doi.org/10.1063/1.2830521.
  2. Allemang, R.J. (2003), "The Modal Assurance Criterion (MAC): Twenty years of use and Abuse", J. Sound Vib., 37(8), 14-23. https://doi.org/10.1093/qjmam/56.3.455.
  3. Araujo, I.G., Sanchez, J.A.G. and Andersen, P, (2018), "Modal parameter identification based on combining transmissibility functions and blind source separation techniques", Mech. Syst. Signal Pr., 105, 276-293. https://doi.org/10.1016/j.ymssp.2017.12.016.
  4. 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.
  5. Behmanesh, I., Yousefianmoghadam, S., Nozari, A., Moaveni, B., and Stavridis, A. (2018), "Uncertainty quantification and propagation in dynamic models using ambient vibration measurements application to a 10-story building", Mech. Syst. Signal Pr., 107, 502-514. https://doi.org/10.1016/j.ymssp.2018.01.033.
  6. 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.
  7. Chen, C.B. and Yu, L. (2020), "A hybrid ant lion optimizer with improved Nelder-Mead algorithm for structural damage detection by improving weighted trace lasso regularization", Adv. Struct. Eng., 23(3), 468-484. https://doi.org/10.1177/1369433219872434.
  8. Chen, C.B., Pan C.D., Chen, Z.P. and Yu, L. (2018), "Structural damage detection via combining weighted strategy with trace Lasso", Adv. Struct. Eng., 22(3), 597-612. https://doi.org/10.1177/1369433218795310.
  9. Chen, Z.P. and Yu, L. (2017), "A novel PSO-based algorithm for structural damage detection using Bayesian multi-sample objective function", Struct. Eng. Mech., 63(6), 825-835. https://doi.org/10.12989/sem.2017.63.6.825.
  10. Chen, Z.P. and Yu, L. (2018), "A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications", Measure., 122, 658-669. https://doi.org/10.1016/j.measurement.2018.01.068.
  11. Chen, Z.X. and Yu, L. (2020), "A novel WOA based structural damage identification using weighted modal data and flexibility assurance criterion", Struct. Eng. Mech., 75(4), 445-454. https://doi.org/10.12989/sem.2020.75.4.445.
  12. Ding, Z.H., Huang, M. and Lu, Z.R. (2016), "Structural damage detection using artificial bee colony algorithm with hybrid search strategy", Swarm Evol. Comput., 28, 1-13. https://doi.org/10.1016/j.swevo.2015.10.010.
  13. 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.
  14. Friswell, M.I.. (2008), "Damage identification using inverse methods", Philos. Tran. A, 365(1851), 393-410. https://doi.org/10.1098/rsta.2006.1930.
  15. Fu, C.Y., Shan, D.S. and Li, Q. (2011), "Time domain method for bridge structural damage alarming when a train is running", J. Vib. Shock, 30(1), 27-32. https://doi.org/10.1080/0144929X.2011.553739.
  16. Grave, E., Obozinski, G.R. and Bach, F.R. (2011), "Trace Lasso: A trace norm regularization for correlated designs", Adv. Neur. Inform. Proc. Syst., 24, 2187-2195.
  17. Ha, T., Kim, J., Cho, B.H., Kim, D.J., Jung, J.E., Shin, S.H. and Kim, H. (2017), "Finite element model updating of multi-span greenhouses based on ambient vibration measurements", Biosyst. Eng., 161, 145-156. https://doi.org/10.1016/j.biosystemseng.2017.06.019.
  18. 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.
  19. 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.
  20. Lei, Y., Chen, F. and Zhou, H. (2015), "An algorithm based on two-step Kalman filter for intelligent structural damage detection". Struct. Control Hlth. Monit., 22(4), 694-706. https://doi.org/10.1002/stc.1712.
  21. Li, J., Hao, H., Xia, Y. and Zhu, H.P. (2014), "Damage detection of shear connectors in bridge structures with transmissibility in frequency domain", Int. J. Struct. Stab. Dyn., 14(02), 1350061. https://doi.org/10.1142/S0219455413500612.
  22. Li, X.Y. and Law, S.S. (2010), "Adaptive Tikhonov regularization for damage detection based on nonlinear model updating", Mech. Syst. Signal Pr., 24(6), 1646-1664. https://doi.org/10.1016/j.ymssp.2010.02.006.
  23. 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.
  24. Liao, Y., Kiremidjian, A.S., Rajagopal, R. and Loh, C.H. (2019), "Structural damage detection and localization with unknown postdamage feature distribution using sequential change-point detection method", J. Aerosp. Eng., 32(2), 04018149. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000979.
  25. Liu, H., Yu, L., Luo, Z., and Chen, Z. (2020), "Multi-strategy structural damage detection based on included angle of vectors and sparse regularization", Struct. Eng. Mech., 75(4), 415-424. https://doi.org/10.12989/sem.2020.75.4.415.
  26. Luo, Z.W., Liu, H.L. and Yu, L. (2020), "Weighted transmissibility assurance criterion for structural damage detection", J. Aerosp. Eng., 34(3), 04021016. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001251.
  27. Maia, N.M., Almeida, R.A., Urgueira, A.P. and Sampaio, R.P. (2011), "Damage detection and quantification using transmissibility", Mech. Syst. Signal Pr., 25(7), 2475-2483. https://doi.org/10.1016/j.ymssp.2011.04.002.
  28. Majumdar, A. (2012), "FOCUSS based Schatten-p norm minimization for real-time reconstruction of dynamic contrast enhanced MRI", IEEE Signal Pr. Lett., 19(5), 315-318. https://doi.org/10.1109/LSP.2012.2191776.
  29. Pan, C.D. and Yu, L. (2019), "Identification of external forces via truncated response sparse decomposition under unknown initial conditions", Adv. Struct. Eng., 22(15), 3161-3175. https://doi.org/10.1177/1369433219859479.
  30. Pan, C.D., Yu, L., Chen, Z.P., Luo, W.F. and Liu, H.L. (2016), "A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection", Smart Struct. Syst., 17(6), 957-980. https://doi.org/10.12989/sss.2016.17.6.957.
  31. Pandey, A.K. and Biswas, M. (1995), "Experimental verification of flexibility difference method for locating damage in structures", J. Sound Vib., 184(2), 311-328. https://doi.org/10.1006/jsvi.1995.0319.
  32. Ribeiro, A.M.R., Silva, J.M.M., and Maia, N.M.M. (2000), "On the generalisation of the transmissibility concept", Mech. Syst. Signal Pr., 14(1), 29-35. https://doi.org/10.1006/mssp.1999.1268.
  33. Salawu, O.S. (1997), "Detection of structural damage through changes in frequency: a review", Eng. Struct., 19, 718-723. https://doi.org/10.1007/BF02832469.
  34. 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.
  35. Teughels, A. and De Roeck, G. (2005), "Damage detection and parameter identification by finite element model updating", Arch. Comput. Meth. E, 12(2), 123-164. https://doi.org/10.1080/17747120.2005.9692748.
  36. 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.
  37. Xu, J., Cheng, Y., and Ma, Y. (2021), "Weighted schatten p-Norm low rank error constraint for image denoising", Entropy, 23(2), 158. https://doi.org/10.3390/e23020158.
  38. Yan, W.J. and Ren, W.X. (2012), "Operational modal parameter identification from power Spectrum density transmissibility", Comput.-Aid. Civil Infrastr. Eng., 27(3), 202-217. https://doi.org/10.1111/j.1467-8667.2011.00735.x.
  39. 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(2), 453-482. https://doi.org/10.1016/j.ymssp.2018.06.053.
  40. Ye, X.W., Jin, T., and Yun, C.B. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., 24(5), 567-586. https://doi.org/10.12989/sss.2019.24.5.567.
  41. Yu, L. and Xu, P. (2011), "Structural health monitoring based on continuous ACO method", Microelectron. Reliab., 51(2), 270-278. https://doi.org/10.1016/j.microrel.2010.09.011.
  42. Yu, L., Wan, Z.Y. and Zhu, H.P. (2008), "An improved PSO algorithm for structural damage detection based on macroeconomic control strategy", Chin. J. Comput. Mech., 25(1), 14-19. https://doi.org/10.1016/S1872-5791(08)60058-5.
  43. Yuen, K.V. (2010), "Efficient model correction method with modal measurement", J. Struct. Eng., ASCE, 136(1), 91-99. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000068.
  44. Zhang, H., Gul, M. and Kostic, B. (2019), "Eliminating temperature effects in damage detection for civil infrastructure using time series analysis and autoassociative neural networks", J. Aerosp. Eng., 32(2), 04019001. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000987.
  45. Zhang, J., Wu, D.J., Li, Q. and Zhang, Y. (2019), "Experimental and numerical investigation of trk-bridge interaction for a long-span bridge", Struct. Eng. Mech., 70(6), 723-735. https://doi.org/10.12989/sem.2019.70.6.723.
  46. Zhang, K. and Guo, X.H. (2016), "Adaptive Tikhonov regularization for simultaneous identification of structural parameters and input", International Conference on Material Science and Civil Engineering, Guilin, China, August. https://doi.org/10.12783/dtmse/msce2016/10511.
  47. Zhu, D., Yi, X. and Wang, Y. (2016), "A local excitation and measurement approach for decentralized damage detection using transmissibility functions", Struct. Control Hlth. Monit., 23(3), 487-502. https://doi.org/10.1002/stc.1781.