Smart Structures and Systems

  • 제31권2호
  • /
  • Pages.155-169
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  • 2023
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Extraction of quasi-static component from vehicle-induced dynamic response using improved variational mode decomposition

  • Zhiwei Chen (Department of Civil Engineering, Xiamen University) ;
  • Long Zhao (Department of Civil Engineering, Xiamen University) ;
  • Yigui Zhou (Department of Civil Engineering, Xiamen University) ;
  • Wen-Yu He (Department of Civil Engineering, Hefei University of Technology) ;
  • Wei-Xin Ren (College of Civil and Transportation Engineering, Shenzhen University)
  • 투고 : 2022.05.30
  • 심사 : 2022.11.07
  • 발행 : 2023.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

The quasi-static component of the moving vehicle-induced dynamic response is promising in damage detection as it is sensitive to bridge damage but insensitive to environmental changes. However, accurate extraction of quasi-static component from the dynamic response is challenging especially when the vehicle velocity is high. This paper proposes an adaptive quasi-static component extraction method based on the modified variational mode decomposition (VMD) algorithm. Firstly the analytical solutions of the frequency components caused by road surface roughness, high-frequency dynamic components controlled by bridge natural frequency and quasi-static components in the vehicle-induced bridge response are derived. Then a modified VMD algorithm based on particle swarm algorithm (PSO) and mutual information entropy (MIE) criterion is proposed to adaptively extract the quasi-static components from the vehicle-induced bridge dynamic response. Numerical simulations and real bridge tests are conducted to demonstrate the feasibility of the proposed extraction method. The results indicate that the improved VMD algorithm could extract the quasi-static component of the vehicle-induced bridge dynamic response with high accuracy in the presence of the road surface roughness and measurement noise.

키워드

과제정보

The authors wish to acknowledge the financial supports from the National Natural Science Foundation of China (52278319, 51878234 and 51778204), Natural Science Foundation for Distinguished Young Scholars of Anhui Province (2208085J20), and Shenzhen Science and Technology Program (No. KQTD20180412181337494). Any opinions and concluding remarks presented in this paper are entirely those of the authors.

참고문헌

  1. Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein, J. (2010), "Distributed optimization and statistical learning via the alternating direction method of multipliers", Found. Trends Mach. Learn., 3, 1-122. https://doi.org/10.1561/2200000016
  2. Castiglione, R., Antoni, J. and Garibaldi, L. (2018), "Separation and identification of structural modes in largely underdetermined scenarios using frequency banding", J. Sound Vib., 414, 192-217. https://doi.org/10.1016/j.jsv.2017.10.033
  3. Chen, Z., Zhou, Y., He, W.Y. and Liu, M. (2020), "Bridge damage detection using quasi-static component of moving vehicle-induced dynamic response", Int. J. Computat. Methods, 18(06), 2042001. https://doi.org/10.1142/S0219876220420013
  4. Cheng, C.C., Yu, C.P., Ke, Y.T. and Hsu, K.T. (2012), "Evaluation of bridge span by recovered stiffness data obtained with moving vehicle loadings", Proceedings of Health Monitoring of Structural and Biological Systems 2012, San Diego, CA, USA, Vol. 8348, pp. 635-641. https://doi.org/10.1117/12.915142
  5. Clerc, M. and Kennedy, J. (2002), "The particle swarm-explosion, stability, and convergence in a multidimensional complex space", IEEE Transact. Evolut. Computat., 20, 1671-1676. https://doi.org/10.1109/4235.985692
  6. Cover, T. and Thomas, J. (2005), "Data compression", In: Elements of Information Theory, (T.M. Cover and J.A. Thomas Eds.). https://doi.org/10.1002/047174882X.ch5
  7. Fu, Z., Zeng, C. and Li, Y. (2018), "A Novel Methodology of Inter-Harmonic Detection Based on VMD", Proceedings of 2018 10th International Conference on Modelling, Identification and Control (ICMIC), Guiyang, China, July. https://doi.org/10.1109/ICMIC.2018.8529924
  8. Gonzalez, A. and Hester, D. (2013), "An investigation into the acceleration response of a damaged beam-type structure to a moving force", J. Sound Vib., 332, 3201-3217. https://doi.org/10.1016/j.jsv.2013.01.024
  9. Hausser, J. and Strimmer, K. (2014), "Estimation of entropy, mutual information and related quantities. R package version 1.2. 1".
  10. He, J. and Zhou, Y. (2019), "A novel mode shape reconstruction method for damage diagnosis of cracked beam", Mech. Syst. Signal Process., 122, 433-447. https://doi.org/10.1016/j.ymssp.2018.12.045
  11. He, W.Y., Ren, W.X. and Zhu, S. (2017), "Damage detection of beam structures using quasi-static moving load induced displacement response", Eng. Struct., 145, 70-82. https://doi.org/10.1016/j.engstruct.2017.05.009
  12. Hester, D. and Gonzalez, A. (2017), "A discussion on the merits and limitations of using drive-by monitoring to detect localised damage in a bridge", Mech. Syst. Signal Process., 90, 234-253. https://doi.org/10.1016/j.ymssp.2016.12.012
  13. Jie, L.I. and Zhao, X. (2006), "A super-element approach for structural identification in time domain", Front. Mech. Eng., 1(2), 215-221. https://doi.org/10.1007/s11465-006-0004-4
  14. Kennedy, J., Eberhart, R.C. and Shi, Y. (2001), "Ch. 7: The Particle Swarm", In: Swarm Intelligence, pp. 287-325. https://doi.org/10.1016/B978-155860595-4/50007-3
  15. Kim, C.W. and Kawatani, M. (2008), "Pseudo-static approach for damage identification of bridges based on coupling vibration with a moving vehicle", Struct. Infrastr. Eng., 4, 371-379. https://doi.org/10.1080/15732470701270082
  16. Kunwar, A., Jha, R., Whelan, M. and Janoyan, K. (2013), "Damage detection in an experimental bridge model using Hilbert-Huang transform of transient vibrations", Struct. Control Health Monitor., 20, 1-15. https://doi.org/10.1002/stc.466
  17. Hester, D. and Gonzalez, A. (2012), "A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle", Mech. Syst. Signal Process., 28, 145-166. https://doi.org/10.1016/j.ymssp.2011.06.007
  18. Isham, M.F., Leong, M.S., Meng, H.L. and Ahmad, Z.A. (2018), "Variational mode decomposition for rotating machinery condition monitoring using vibration signals", Transact. Nanjing Univ. Aeronaut. Astronaut., 35(1), 38-50. https://doi.org/10.16356/j.1005-1120.2018.01.038
  19. Jaishi, B. and Ren, W.X. (2006), "Damage detection by finite element model updating using modal flexibility residual - ScienceDirect", J. Sound Vib., 290, 369-387. https://doi.org/10.1016/j.jsv.2005.04.006
  20. Kourehli, S.S. (2016), "Structural damage diagnosis using incomplete static responses and LS-SVM", Inverse Probl. Sci. Eng., 25(3), 418-433. https://doi.org/10.1080/17415977.2016.1169277
  21. Li, Z. and Au, F. (2014), "Damage detection of a continuous bridge from response of a moving vehicle", Shock Vib., 2014, 1-7. https://doi.org/10.1155/2014/146802
  22. Li, H.J., Ou, J., Zhao, X., Zhou, W., H. Li, Zhou, Z. and Yang, Y. (2006), "Structural health monitoring system for the Shandong Binzhou Yellow River highway bridge", Comput.-Aided Civil Infrastr. Eng., 21, 306-317. https://doi.org/10.1111/j.1467-8667.2006.00437.x
  23. Li, J., Zhu, X., Law, S.S. and Samali, B. (2020), "Time-varying characteristics of bridges under the passage of vehicles using synchroextracting transform", Mech. Syst. Signal Process., 140, p. 106727. https://doi.org/10.1016/j.ymssp.2020.106727
  24. Link, M. and Weiland, M. (2009), "Damage identification by multi-model updating in the modal and in the time domain", Mech. Syst. Signal Process., 23, 1734-1746. https://doi.org/10.1016/j.ymssp.2008.11.009
  25. Liu, S., Tang, G., Wang, X. and He, Y. (2016), "Time-frequency analysis based on improved variational mode decomposition and teager energy operator for rotor system fault diagnosis", Mathe. Probl. Eng., 2016. https://doi.org/10.1155/2016/1713046
  26. Liu, J., Chen, S., Berges, M., Bielak, J. and Noh, H.Y. (2020), "Diagnosis algorithms for indirect structural health monitoring of a bridge model via dimensionality reduction", Mech. Syst. Signal Process., 136, 106454. https://doi.org/10.1016/j.ymssp.2019.106454
  27. Meredith, J., Gonzalez, A. and Hester, D. (2012), "Empirical mode decomposition of the acceleration response of a prismatic beam subject to a moving load to identify multiple damage locations", Shock Vib., 19, 845-856. https://doi.org/10.1155/2012/804590
  28. Morelli, C., Gantar, C., Honkasalo, T., Mcconnell, R.K. and Wahlen, C.T. (1974), "The International Organization for Standardization (ISO)", New Political Economy.
  29. Narkis, Y. (1994), "Identification of crack location in vibrating simply supported beams", J. Sound Vib., 172, 549-558. https://doi.org/10.1006/jsvi.1994.1195
  30. Niu, J., Zong, Z.H. and Chu, F.P. (2015), "Damage identification method of girder bridges based on finite element model updating and modal strain energy", Sci. China Technol. Sci., 58, 701-711. https://doi.org/10.1007/s11431-014-5763-2
  31. Rilling, G. and Flandrin, P. (2007), "One or two frequencies? The empirical mode decomposition answers", IEEE Transact. Signal Process., 56, 85-95. https://doi.org/10.1109/TSP.2007.906771
  32. Roveri, N. and Carcaterra, A. (2012), "Damage detection in structures under traveling loads by Hilbert-Huang transform", Mech. Syst. Signal Process., 28, 128-144. https://doi.org/10.1016/j.ymssp.2011.06.018
  33. Sadhu, A., Narasimhan, S. and Antoni, J. (2017), "A review of output-only structural mode identification literature employing blind source separation methods", Mech. Syst. Signal Process., 94, 415-431. https://doi.org/10.1016/j.ymssp.2017.03.001
  34. Sun, Z., Nagayama, T., Su, D. and Fujino, Y. (2016), "A damage detection algorithm utilizing dynamic displacement of bridge under moving vehicle", Shock Vib., 2016, 8454567. https://doi.org/10.1155/2016/8454567
  35. Tsakalozos, N., Drakakis, K. and Rickard, S. (2012), "A formal study of the nonlinearity and consistency of the Empirical Mode Decomposition", Signal Process., 92, 1961-1969. https://doi.org/10.1016/j.sigpro.2011.09.014
  36. Tuzikov, A. and Sheynin, S. (2002), "Symmetry measure computation for convex polyhedra", J. Mathe. Imag. Vis., 16, 41-56. https://doi.org/10.1023/A:1013986402264
  37. Wang, C.Y., Huang, C.K. and Chen, C.S. (2011), "Damage assessment of beam by a quasi-static moving vehicular load", Adv. Adapt. Data Anal., 03, 417-445. https://doi.org/10.1142/S1793536911000908
  38. Wang, Y., Markert, R., Xiang, J. and Zheng, W. (2015), "Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system", Mech. Syst. Signal Process., 60, 243-251. https://doi.org/10.1016/j.ymssp.2015.02.020.
  39. Yang, Y.B. and Lin, C.W. (2005), "Vehicle-bridge interaction dynamics and potential applications", J. Sound Vib., 284(1-2), 205-226. https://doi.org/10.1016/j.jsv.2004.06.032
  40. Yu, Z., Xia, H., Yin, Y.G. and Sun, D.H. (2015), "Bridge damage identification based on wavelet transform and Lipschitz exponent", Zhendong yu Chongji/J. Vib. Shock, 34, 65-69. https://doi.org/10.13465/j.cnki.jvs.2015.14.012
  41. Zhu, X. and Law, S. (2006), "Wavelet-based crack identification of bridge beam from operational deflection time history", Int. J. Solids Struct., 43, 2299-2317. https://doi.org/10.1016/j.ijsolstr.2005.07.024
  42. Zosso, D., and Dragomiretskiy, K. (2014), "Variational mode decomposition", IEEE Transact. Signal Process., 62(3), 531-544. https://doi.org/10.1109/TSP.2013.2288675
  43. Zweig, D. and Hufnagel, R. (1990), "Hilbert transform algorithm for fringe-pattern analysis", In: Advanced Optical Manufacturing and Testing, Vol. 1333, pp. 295-302. https://doi.org/ 10.1117/12.22815