Smart Structures and Systems

  • 제30권3호
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  • Pages.273-286
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Identification of structural systems and excitations using vision-based displacement measurements and substructure approach

  • Lei, Ying (School of Architecture and Civil Engineering, Xiamen University) ;
  • Qi, Chengkai (School of Architecture and Civil Engineering, Xiamen University)
  • 투고 : 2022.06.26
  • 심사 : 2022.07.10
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

키워드

과제정보

The research in this paper is supported by the National Natural Science Foundation of China via the grant No. 52178304.

참고문헌

  1. Busca, G., Cigada, A., Mazzoleni, P. and Zappa, E. (2014), "Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system", Experim. Mech., 54(2), 255-271. https://doi.org/10.1007/s11340-013-9784-8
  2. Chen, J. and Li, J. (2001), "Study on wind load inverse of tall building", Chinese Quarterly of Mechanics, (01), 72-77. https://doi.org/10.15959/j.cnki.0254-0053.2001.01.010
  3. Dong, C.Z., Ye, X.W. and Jin, T. (2018), "Identification of structural dynamic characteristics based on machine vision technology", Measurement, 126, 405-416. https://doi.org/10.1016/j.measurement.2017.09.043
  4. Feng, D.M. and Feng, M.Q. (2016), "Vision-based multipoint displacement measurement for structural health monitoring", Struct. Control Health Monitor., 23(5), 876-890. https://doi.org/10.1002/stc.1819
  5. Feng, D.M. and Feng, M.Q. (2017), "Identification of structural stiffness and excitation forces in time domain using noncontact vision-based displacement measurement", J. Sound Vib., 406, 15-28. https://doi.org/10.1016/j.jsv.2017.06.008
  6. Feng, D.M. and Feng, M.Q. (2018), "Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection - A review", Eng. Struct., 156, 105-117. https://doi.org/10.1016/j.engstruct.2017.11.018
  7. Feng, W., Li, Q. and Lu, Q. (2020), "Force localization and reconstruction based on a novel sparse Kalman filter", Mech. Syst. Signal Process., 144, 106890. https://doi.org/10.1016/j.ymssp.2020.106890
  8. Gillijns, S. and De Moor, B. (2007), "Unbiased minimum-variance input and state estimation for linear discrete-time systems", Automatica, 43(1), 111-116. https://doi.org/10.1016/j.automatica.2006.08.002
  9. Hassanabadi, M.E., Heidarpour, A., Azam, S.E. and Arashpour, M. (2022), "A Bayesian smoothing for input-state estimation of structural systems", Comput.-Aided Civil Infrastr. Eng., 37(3), 317-334. https://doi.org/10.1111/mice.12733
  10. Huang, J.S., Li, X.Z., Zhang, F.B. and Lei, Y. (2021), "Identification of joint structural state and earthquake input based on a generalized Kalman filter with unknown input", Mech. Syst. Signal Process., 151(9), 107362. https://doi.org/10.1016/j.ymssp.2020.107362
  11. Koh, C.G., Hong, B. and Liaw, C.Y. (2003), "Substructural and progressive structural identification methods", Eng. Struct., 25(12), 1551-1563. https://doi.org/10.1016/S0141-0296(03)00122-6
  12. Kuddus, M.A., Li, J., Hao, H., Li, C. and Bi, K. (2019), "Targetfree vision-based technique for vibration measurements of structures subjected to out-of-plane movements", Eng. Struct., 190, 210-222. https://doi.org/10.1016/j.engstruct.2019.04.019
  13. Lagerblad, U., Wentzel, H. and Kulachenko, A. (2021), "Study of a fixed-lag Kalman smoother for input and state estimation in vibrating structures", Inverse Problems Sci. Eng., 29(9), 1260-1281. https://doi.org/10.1080/17415977.2020.1845669
  14. Lei, Y., Yang, N. and Xia, D.D. (2017), "Probabilistic structural damage detection approaches based on structural dynamic response moments", Smart Struct. Syst., Int. J., 20(2), 207-217. https://doi.org/10.12989/sss.2017.20.2.207
  15. Lei, Y., Lu, J.B. and Huang, J.S. (2020), "Synthesize identification and control for smart structures with time-varying parameters under unknown earthquake excitation", Struct. Control Health Monitor., 27(10), e2512. https://doi.org/10.1002/stc.2512
  16. Lei, Y., Huang, J.S., Qi, C.K., Zhang, X. and Li, X.Z. (2022), "Parallel substructure identification of linear and nonlinear structures using only partial output measurements", J. Eng. Mech., 148(7), 04022033. https://doi.org/10.1061/(asce)em.1943-7889.0002117
  17. Li, J. and Hao, H. (2016), "Substructural interface force identification with limited vibration measurements", J. Civil Struct. Health Monitor., 6(3), 395-410. https://doi.org/10.1007/s13349-016-0157-8
  18. Liu, L.J., Su, Y., Zhu, J.J. and Lei, Y. (2016), "Data fusion based EKF-UI for real-time simultaneous identification of structural systems and unknown external inputs", Measurement, 88, 456-467. https://doi.org/10.1016/j.measurement.2016.02.002
  19. Liu, L.J., Mi, J.N., Zhang, Y.X. and Lei, Y. (2021), "Damage detection of bridge structures under unknown seismic excitations using support vector machine based on transmissibility function and wavelet packet energy", Smart Struct. Syst., Int. J., 27(2), 257-266. https://doi.org/10.12989/sss.2021.27.2.257
  20. Maes, K., Gillijns, S. and Lombaert, G. (2018), "A smoothing algorithm for joint input-state estimation in structural dynamics", Mech. Syst. Signal Process., 98, 292-309. https://doi.org/10.1016/j.ymssp.2017.04.047
  21. Maes, K., Karlsson, F. and Lombaert, G. (2019), "Tracking of inputs, states and parameters of linear structural dynamic systems", Mech. Syst. Signal Process., 130, 755-775. https://doi.org/10.1016/j.ymssp.2019.04.048
  22. Pan, S.W., Su, H.Y., Wang, H., Chu, J. and Lu, R.Q. (2009), "Input and state estimation for linear systems: A least squares estimation approach", In: 2009 7th Asian Control Conference, pp. 378-383.
  23. Pan, S.W., Xiao, D., Xing, S.T., Law, S.S., Du, P.Y. and Li, Y.J. (2016), "A general extended Kalman filter for simultaneous estimation of system and unknown inputs", Eng. Struct., 109(2), 85-98. https://doi.org/10.1016/j.engstruct.2015.11.014
  24. Rostami, P., Mahsuli, M., Ghahari, S.F. and Taciroglu, E. (2021), "Bayesian joint state-parameter-input estimation of flexiblebase buildings from sparse measurements using Timoshenko beam models", J. Struct. Eng., 147(10), 04021151. https://doi.org/10.1061/(asce)st.1943-541x.0003095
  25. Spencer, B.F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Eng., 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
  26. Tian, Y.D., Zhang, J. and Yu, S.S. (2019), "Rapid impact testing and system identification of footbridges using particle image velocimetry", Comput.-Aided Civil Infrastr. Eng., 34(2), 130-145. https://doi.org/10.1111/mice.12390
  27. Wan, Z.M., Wang, T., Li, L. and Xu, Z.C. (2018), "A novel coupled state/input/parameter identification method for linear structural systems", Shock Vib., 2018. https://doi.org/10.1155/2018/7691721
  28. Wang, W.Z., Mottershead, J.E., Siebert, T. and Pipino, A. (2012), "Frequency response functions of shape features from full-field vibration measurements using digital image correlation", Mech. Syst. Signal Process., 28, 333-347. https://doi.org/10.1016/j.ymssp.2011.11.023
  29. Wang, R.H., Li, J., Chencho, An, S.J., Hao, H., Liu, W.Q. and Li, L. (2021), "Densely connected convolutional networks for vibration based structural damage identification", Eng. Struct., 245, 112871. https://doi.org/10.1016/j.engstruct.2021.112871
  30. Weng, S., Zhu, H.P., Xia, Y., Li, J.J. and Tian, W. (2020), "A review on dynamic substructuring methods for model updating and damage detection of large-scale structures", Adv. Struct. Eng., 23(3), 584-600. https://doi.org/10.1177/1369433219872429
  31. Yang, N., Lei, Y., Li, J., Hao, H. and Huang, J.S. (2022), "A substructural and wavelet multiresolution approach for identifying time-varying physical parameters by partial measurements", J. Sound Vib., 523. https://doi.org/10.1016/j.jsv.2021.116737
  32. Ye, X.W., Dong, C.Z. and Liu, T. (2016), "Force monitoring of steel cables using vision-based sensing technology: methodology and experimental verification", Smart Struct. Syst., Int. J., 18(3), 585-599. https://doi.org/10.12989/sss.2016.18.3.585
  33. Ye, X.W., Li, Z.X. and Jin, T. (2022), "Smartphone-based structural crack detection using pruned fully convolutional networks and edge computing", Smart Struct. Syst., Int. J., 29(1), 141-151. https://doi.org/10.12989/sss.2022.29.1.141
  34. Ying, Z.G., Wang, Y.W., Ni, Y.Q. and Xu, C. (2021), "Model-free identification of multiple periodic excitations and detection of structural anomaly using noisy response measurements", Smart Struct. Syst., Int. J., 28(3), 407-423. https://doi.org/10.12989/sss.2021.28.3.407
  35. Yoon, H., Elanwar, H., Choi, H.J., Golparvar-Fard, M. and Spencer, B.F. (2016), "Target-free approach for vision-based structural system identification using consumer-grade cameras", Struct. Control Health Monitor., 23(12), 1405-1416. https://doi.org/10.1002/stc.1850
  36. Yuen, K.V. and Huang, K. (2018a), "Real-time substructural identification by boundary force modeling", Struct. Control Health Monitor., 25(5), e2151. https://doi.org/10.1002/stc.2151
  37. Yuen, K.V. and Huang, K. (2018b), "Identifiability-enhanced Bayesian frequency-domain substructure identification", Comput.-Aided Civil Infrastr. Eng., 33(9), 800-812. https://doi.org/10.1111/mice.12377
  38. Zhang, J. and Moon, F.L. (2012), "A new impact testing method for efficient structural flexibility identification", Smart Mater. Struct., 21(5). https://doi.org/10.1088/0964-1726/21/5/055016
  39. Zhang, J., Guo, S.L. and Zhang, Q.Q. (2015), "Mobile impact testing for structural flexibility identification with only a single reference", Comput.-Aided Civil Infrastr. Eng., 30(9), 703-714. https://doi.org/10.1111/mice.12112