DOI QR코드

DOI QR Code

Application of power spectral density function for damage diagnosis of bridge piers

  • Bayat, Mahmoud (Department of Civil Engineering, Roudehen Branch, Islamic Azad University) ;
  • Ahmadi, Hamid Reza (Department of Civil Engineering, Faculty of Engineering, University of Maragheh) ;
  • Mahdavi, Navideh (Department of Civil Engineering, Marand Branch, Islamic Azad University)
  • Received : 2019.01.11
  • Accepted : 2019.03.20
  • Published : 2019.07.10

Abstract

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

Keywords

References

  1. Ahmadi, H.R. and Anvari, D. (2018), "New damage index based on least squares distance for damage diagnosis in steel girder of bridge's deck", Struct. Control Health Monitoring, 25(10). https://doi.org/10.1002/stc.2232.
  2. Ahmadi, H.R., Daneshjoo, F. and Khaji, N. (2015), "New damage indices and algorithm based on square time-frequency distribution for damage detection in concrete piers of railroad bridges", Struct. Control Health Monitoring, 22, 91-106. https://doi.org/10.1002/stc.1662.
  3. Aviram, A., Mackie, K.R. and Stojadinovic, B. (2008), "Guidelines for nonlinear analysis of bridge structures in California", Pacific Earthquake Engineering Research Center, California, USA.
  4. Bayat, M., Daneshjoo, F. and Nistico, N. (2015), "A novel proficient and sufficient intensity measure for probabilistic analysis of skewed highway bridges", Struct. Eng. Mech., 55(6), 1177-1202. https://doi.org/10.12989/sem.2015.55.6.1177.
  5. Bernal, D. (2002), "Load vectors for damage localization", J. Eng. Mech., 128, 7-14. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:1(7).
  6. Cornwell, P., Doebling, S.W. and Farrar, C.R. (1999), "Application of the strain energy damage detection method to plate-like structures", J. Sound Vib., 224(2), 359-374. https://doi.org/10.1006/jsvi.1999.2163.
  7. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), "Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review", LA-13070-MS; Los Alamos National Lab., NM, USA.
  8. Du, A., Padgett, J.E. and Shafieezadeh, A. (2019), "A posteriori optimal intensity measures for probabilistic seismic demand modelling", Bullet. Earthq. Eng., 17(2), 681-706. https://doi.org/10.1007/s10518-018-0484-8.
  9. Duran, B., Tunaboyu, O., Kaplan, O. and Avsar, O. (2018), "Effectiveness of seismic repairing stages with CFRPs on the seismic performance of damaged RC frames", Struct. Eng. Mech., 67(3), 233-244. http://doi.org/10.12989/sem.2018.67.3.233.
  10. Fang, S.E. and Perera, R. (2009), "Power mode shapes for early damage detection in linear structures", J. Sound Vib., 324)1-2(:40-56. https://doi.org/10.1016/j.jsv.2009.02.002.
  11. Fereshtehnejad, E., Hur, J., Shafieezadeh, A., Brokaw, M., Backs, J., Noll, B. and Waheed, A. (2018), "A bridge performance index with objective incorporation of safety risks", Proceedings of Transportation Research Board 97th Annual Meeting, Washington DC, USA, January.
  12. Gallego, A., Benavent-Climent, A. and Romo-Melo, L. (2015), "Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures", Mech. Syst. Signal Process., 60, 90-105. https://doi.org/10.1016/j.ymssp.2015.01.030
  13. Havelock, D., Kuwano, S. and Vorlander, M. (2008), "Handbook of signal processing in acoustics", Springer Science and Business Media, Berlin, Germany.
  14. Jorquera Lucerga, J.J., Lozano Galant, J.A. and Turmo Coderque, J. (2016), "Structural behavior of non-symmetrical steel cablestayed bridges", Steel Compos. Struct., 20(2), 447-468. http://dx.doi.org/10.12989/SCS.2016.20.2.447.
  15. Kia, M. and Banazadeh, M. (2016), "Closed-form fragility analysis of the steel moment resisting frames", Steel Compos. Struct., 21(1), 93-107. https://doi.org/10.12989/scs.2016.21.1.093
  16. Kia, M. and Banazadeh, M. (2017), "Probabilistic seismic hazard analysis using reliability methods", Scientia Iranica, 24(3), 933-941. https://doi.org/10.24200/sci.2017.4077
  17. Kim, J.T., Ryu, Y.S., Cho, H.M. and Stubbs, N. (2003), "Damage identification in beam-type structures: frequency-based method vs mode-shape-based method", Eng. Struct., 25(1) 57-67. https://doi.org/10.1016/S0141-0296(02)00118-9
  18. Kutanaei, S.S. and Choobbasti, A.J. (2015), "Mesh-free modeling of liquefaction around a pipeline under the influence of trench layer", Acta Geotechnica, 10(3), 343-355. https://doi.org/10.1007/s11440-015-0381-0
  19. Kutanaei, S.S. and Choobbasti, A.J. (2016), "Experimental study of combined effects of fibers and nanosilica on mechanical properties of cemented sand", J. Mater. Civil Eng., 28(6), https://doi.org/10.1061/(ASCE)MT.1943-5533.0001521.
  20. Kutanaei, S.S. and Choobbasti, A.J. (2019), "Prediction of liquefaction potential of sandy soil around a submarine pipeline under earthquake loading", J. Pipeline Syst. Eng. Practice, 10(2), https://doi.org/10.1061/(ASCE)PS.1949-1204.0000349.
  21. Lee, Eun-Taik and Hee-Chang Eun (2016), "Structural damage detection by power spectral density estimation using outputonly measurement", Shock Vib., 2016, https://doi.org/10.1155/2016/8761249.
  22. Li, H., Wang, J. and Hu, S.L.J. (2008), "Using incomplete modal data for damage detection in offshore jacket structures", Ocean Eng., 35(17-18), 1793-1799. https://doi.org/10.1016/j.oceaneng.2008.08.020.
  23. Liu, F., Li, H., Li, W. and Wang, B. (2014), "Experimental study of improved modal strain energy method for damage localisation in jacket-type offshore wind turbines", Renewable Energy, 72, 174-181. https://doi.org/10.1016/j.renene.2014.07.007.
  24. Liu, L., Su, H. and Lei, Y. (2017), "Probabilistic damage detection of structures with uncertainties under unknown excitations based on Parametric Kalman filter with unknown Input", Struct. Eng. Mech., 63(6), 779-788. http://dx.doi.org/10.12989/sem.2017.63.6.779.
  25. Masciotta, M.G., Ramos, L.F., Lourenco, P.B., Vasta, M. and De Roeck, G. (2016), "A spectrum-driven damage identification technique: Application and validation through the numerical simulation of the Z24 Bridge", Mech. Syst. Signal Process., 70, 578-600. https://doi.org/10.1016/j.ymssp.2015.08.027.
  26. Meymian, N.Z., Clark, N.N., Musho, T., Darzi, M., Johnson, D. and Famouri, P. (2018), "An optimization method for flexural bearing design for high-stroke high-frequency applications". Cryogenics, 95, 82-94. https://doi.org/10.1016/j.cryogenics.2018.09.008.
  27. Nabizadeh, A., Tabatabai, H. and Tabatabai, M.A. (2018), "Survival analysis of bridge superstructures in Wisconsin", Appl. Sci., 8, 2079. https://doi.org/10.3390/app8112079.
  28. Nabizadehdarabi, A. (2015), "Reliability of bridge superstructures in Wisconsin", M.Sc. Dissertation, University of Wisconsin Milwaukee, USA.
  29. Najafabadi, A.A., Daneshjoo, F. and Bayat, M., (2017), "A novel index for damage detection of deck and dynamic behavior of horizontally curved bridges under moving load", J. Vibroeng., 19(7), 5421-5433. https://doi.org/10.21595/jve.2017.19370.
  30. Pau, A., Greco, A. and Vestroni, F. (2011), "Numerical and experimental detection of concentrated damage in a parabolic arch by measured frequency variations", J. Vib. Control, 17(4), 605-614. https://doi.org/10.1177/1077546310362861.
  31. Pradhan, S. and Modak, S.V. (2012), "Normal response function method for mass and stiffness matrix updating using complex FRFs", Mech. Syst. Signal Process., 32, 232-250. https://doi.org/10.1016/j.ymssp.2012.04.019.
  32. Qiao, L. (2009), "Structural damage detection using signal-based pattern recognition", Ph.D. Dissertation, Kansas State University, USA.
  33. Qiao, L., Esmaeily, A. and Melhem, H.G. (2012), "Signal pattern recognition for damage diagnosis in structures", Comput. Aid Civil Infrastruct. Eng., 27(9), 699-710. https://doi.org/10.1111/j.1467-8667.2012.00766.x.
  34. Rahmatalla, S., Eun, H.C. and Lee, E.T. (2012), "Damage detection from the variation of parameter matrices estimated by incomplete FRF data", Smart Struct. Syst., 9(1), 55-70. http://dx.doi.org/10.12989/sss.2012.9.1.055.
  35. Sakka, Z.I., Assakkaf, I.A. and Qazweeni, J.S. (2018), "Reliability-based assessment of damaged concrete buildings", Struct. Eng. Mech., 65(6), 751-760. http://dx.doi.org/10.12989/sem.2018.65.6.751.
  36. Sazonov, E. and Klinkhachorn, P. (2005), "Optimal spatial sampling interval for damage detection by curvature or strain energy mode shapes", J. Sound Vib., 285(4-5), 783-801. https://doi.org/10.1016/j.jsv.2004.08.021.
  37. Shao, C., J.W. Ju, Han, G. and Qian, Y. (2017), "Seismic applicability of a long-span railway concrete upper-deck arch bridge with CFST rigid skeleton rib", Struct. Eng. Mech., 61(5), 645-655. http://dx.doi.org/10.12989/sem.2017.61.5.645.
  38. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D. W., Nadler, B.R. and Czarnecki, J.J. (2003), "A review of structural health monitoring literature: 1996-2001", LA-UR-02-2095; Los Alamos National Laboratory, USA.
  39. Stoica, P. and Moses, R.L. (2005), Spectral Analysis of Signals, Prentice Hall, Inc., New Jersey, USA.
  40. Tabatabai, H., Sobolev, K., Ghorbanpoor, A., Nabizadeh, A., Lee, C. and Lind, M. (2016), Evaluation of Thin Polymer Overlays for Bridge Decks. Wisconsin Highway Research Program, Madison, USA.
  41. Tang, J.P., Chiou, D.J., Chen, C.W., Chiang, W.L., Hsu, W.K., Chen, C.Y. and Liu, T.Y. (2011), "RETRACTED: A case study of damage detection in benchmark buildings using a Hilbert-Huang Transform-based method", J. Vib. Control, 17(4), 623-636. https://doi.org/10.1177/1077546309360053.
  42. Wahab, M.A. (2001), "Effect of modal curvatures on damage detection using model updating", Mech. Syst. Signal Process., 15(2), 439-445. https://doi.org/10.1006/mssp.2000.1340.
  43. Walia, S.K., Vinayak, H.K., Kumar, A. and Parti, R. (2015), "Modal parametric changes in a steel bridge with retrofitting", Steel Compos. Struct., 19(2), 385-403. http://dx.doi.org/10.12989/scs.2015.19.2.385.
  44. Yan, W.J. and Ren, W.X. (2012), "Operational modal parameter identification from power spectrum density transmissibility", Comput. Aid Civil Infrastruct. Eng., 27(3), 202-217. https://doi.org/10.1111/j.1467-8667.2011.00735.x.
  45. Yan, W.J., Ren, W.X. and Huang, T.L. (2012), "Statistic structural damage detection based on the closed-form of element modal strain energy sensitivity", Mech. Syst. Signal Process., 28, 183-194. https://doi.org/10.1016/j.ymssp.2011.04.011.
  46. Yan, Y.J., Cheng, L., Wu, Z.Y. and Yam, L.H. (2007), "Development in vibration-based structural damage detection technique", Mech. Syst. Signal Process., 21(5), 2198-2211. https://doi.org/10.1016/j.ymssp.2006.10.002
  47. Yang, Y.B. and Yang, J.P. (2018), "State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles", J. Struct. Stability Dynam., 18(02), https://doi.org/10.1142/S0219455418500256.
  48. Yin, X., Liu, Y. and Kong, B. (2016), "Vibration behaviors of a damaged bridge under moving vehicular loads", Struct. Eng. Mech., 58(2), 199-216. http://dx.doi.org/10.12989/sem.2016.58.2.199.
  49. Zhang, D. and Johnson, E.A. (2012), "Substructure identification for shear structures: cross-power spectral density method", Smart Mater. Struct., 21(5), https://doi.org/10.1088/0964-1726/21/5/055006.
  50. Zhang, D. and Johnson, E.A. (2014), "Substructure identification for plane frame building structures", Eng. Struct., 60, 276-286. https://doi.org/10.1016/j.engstruct.2013.12.008.
  51. Zheng, Z.D., Lu, Z.R., Chen, W.H. and Liu, J.K. (2015), "Structural damage identification based on power spectral density sensitivity analysis of dynamic responses", Comput. Struct., 146, 176-184. https://doi.org/10.1016/j.compstruc.2014.10.011.

Cited by

  1. Damage Detection Applied to a Full-Scale Steel Bridge Using Temporal Moments vol.2020, 2019, https://doi.org/10.1155/2020/3083752
  2. Earthquake Influence on the Rail Irregularity on High-Speed Railway Bridge vol.2020, 2020, https://doi.org/10.1155/2020/4315304
  3. A New Vehicle-Bridge Coupling Analysis Method Based on Model Polycondensation vol.25, pp.1, 2019, https://doi.org/10.1007/s12205-020-0383-9
  4. Dynamic vulnerability assessment and damage prediction of RC columns subjected to severe impulsive loading vol.77, pp.4, 2021, https://doi.org/10.12989/sem.2021.77.4.441