Structural Monitoring and Maintenance

  • 제7권2호
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  • Pages.149-166
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  • 2020
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Damage detection of railway bridges using operational vibration data: theory and experimental verifications

  • Azim, Md Riasat (Department of Civil & Environmental Engineering, University of Alberta) ;
  • Zhang, Haiyang (Department of Civil & Environmental Engineering, University of Alberta) ;
  • Gul, Mustafa (Department of Civil & Environmental Engineering, University of Alberta)
  • 투고 : 2020.04.02
  • 심사 : 2020.06.02
  • 발행 : 2020.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.

키워드

과제정보

The study is funded by IC-IMPACTS (the India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability), established through the Networks of Centres of Excellence of Canada.

참고문헌

  1. Azim, M.R. and Gul, M. (2020a), "Damage detection framework for truss railway bridges utilizing statistical analysis of operational strain response", Struct. Control Health Monit., e2573. https://doi.org/10.1002/stc.2573.
  2. Azim, M.R. and Gul, M. (2020b), "Data-driven damage identification technique for truss railroad bridges utilizing principal component analysis of strain response", Struct. Infrastruct. Eng., https://doi.org/10.1080/15732479.2020.1785512.
  3. Azim, M.R. and Gul, M. (2020c), "Damage detection of steel truss railway bridges using operational vibration data", J. Struct. Eng. - ASCE, 146(3), 04020008. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002547.
  4. Azim, M.R. and Gul. M. (2019), "Damage detection of steel girder railway bridges utilizing operational vibration response", Struct. Control Health Monit., 26(11), e2447. https://doi.org/10.1002/stc.2447.
  5. Beskhyroun, S., Oshima, T. and Mikami, S. (2010), "Wavelet-based technique for structural damage detection", Struct. Control Health Monit., 17, 473-494. https://doi.org/10.1002/stc.316
  6. Bowe, C., Quirke, P., Cantero, D. and O'Brien, E.J. (2015), "Drive-by structural health monitoring of railway bridges using train mounted accelerometers", Proceedings of the 5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering. Greece.
  7. Brownjohn, J.M.W., Tjin, S.C., Tan, G.H. and Tan, B.L. (2004), "A structural health monitoring paradigm for civil infrastructure", Proceedings of the1st FIG International Symposium on Engineering Surveys for Construction Works and Structural Engineering, Nottingham, UK.
  8. Celik, O., Terrell, T., Necati, C.F. and Gul, M. (2018), "Sensor clustering technique for practical structural monitoring and maintenance", Struct. Monit. Maint., 5(2), 273-295. https://doi.org/10.12989/SMM.2018.5.2.273
  9. CWC. (2020), "Visual grading of dimension lumber", Canadian Wood Council, Ottawa, Canada. https://cwc.ca/how-to-build-with-wood/wood-products/lumber/grades/
  10. Do, N.T., Mei, Q. and Gul, M. (2019), "Damage assessment of shear-type structures under varying mass effects", Struct. Monit. Maint., 6(3), 237-254. https://doi.org/10.12989/smm.2019.6.3.237.
  11. Farahani, R.V. and Penumadu, D. (2016), "Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data", Eng. Struct., 115, 129-139. https://doi.org/10.1016/j.engstruct.2016.02.008
  12. George, R.C., Posey, J., Gupta, A., Mukhopadhyay, S. and Mishra, S.K. (2017), "Damage detection in railway bridges under moving train load", Proceedings of the Society for Experimental Mechanics Series. Model Validation and Uncertainty Quantification, 3, 349-354.
  13. Gonzalez, I. and Karoumi, R. (2015), "BWIM aided damage detection in bridges using machine learning", J. Civil Struct. Health Monit., 5, 715-725. https://doi.org/10.1007/s13349-015-0137-4
  14. Gu, J., Gül, M. and Wu, X. (2017), "Damage detection under varying temperature using Artificial Neural Networks", J. Struct. Control Health Monit., 24(11), e1998. https://doi.org/10.1002/stc.1998
  15. Kim, C.W., Kitauchi, S., Chang, K.C., Mcgetrick, P.J., Sugiura, K. and Kawatani, M. (2014), "Structural damage diagnosis of steel truss bridges by outlier detection", Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR, 4631-4638.
  16. Kopsaftopoulos, F.P. and Fassois. S.D. (2010), "Vibration based health monitoring for a lightweight truss structure: Experimental assessment of several statistical time series methods", Mech. Syst. Signal Pr., 24(7), 1977-1997. https://doi.org/10.1016/j.ymssp.2010.05.013
  17. Kostic, B. and Gul, M. (2017), "Vibration based damage detection of bridges under varying temperature effects using time series analysis and artificial neural networks", J. Bridge Eng. - ASCE, 22(10), 04017065. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001085
  18. Lord Sensing Microstrain. (2019), https://www.microstrain.com/wireless/g-link-200-oem
  19. Lu, Z.R. and Liu, J.K. (2011), "Identification of both structural damages in bridge deck and vehicular parameters using measured dynamic responses", Comput. Struct., 89, 1397-1405. https://doi.org/10.1016/j.compstruc.2011.03.008
  20. Mehrjou. M., Khaji, N., Moharrami, H. and Bahreininejad, A. (2008), "Damage detection of truss bridge joints using Artificial Neural Networks", J. Exp. Syst. with Appl., 35(3), 1122-1131. https://doi.org/10.1016/j.eswa.2007.08.008
  21. Mei, Q. and Gul, M. (2014), "Novel sensor clustering-based approach for simultaneous detection of stiffness and mass changes using output-only data", J. Struct. Eng., 141(10), 04014237. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001218.
  22. Moreu, F., LaFave, J. and Spencer, B. (2012), "Structural health monitoring of railroad bridges - Research needs and preliminary results", ASCE Structural Congress, 2141-2152.
  23. Moreu, F., Jo, H., Li, J., Kim, R.E., Cho. S., Kimmle, A., Scola, S., Le, H., Spencer Jr., B.F. and LaFave, J.M. (2015), "Dynamic assessment of timber railroad bridges using displacements", J. Bridge Eng. - ASCE, 20(10), 04014114. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000726
  24. Moaveni, B., Hurlebus, S. and Moon, F. (2013), "Special issue on real-world applications of structural identification and health monitoring methodologies", J. Struct. Eng., 139(10), 1637-1638. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000779
  25. PCB Piezotronics. (2019), https://www.pcb.com/products?model=393a03
  26. Rytter, A. (1993), "Vibration Based Inspection of Civil Engineering Structures", Ph. D. dissertation; Aalborg University, Denmark.
  27. Sadhu, A., Goldack, A. and Narasimhan, S. (2015), "Ambient modal identification using multirank parallel factor decomposition", Struct. Control Health Monit., 22(4), 595-614. https://doi.org/10.1002/stc.1706
  28. Scianna A.M. and Christenson R. (2009), "Probabilistic Structural Health Monitoring Method Applied to the Bridge Health Monitoring Benchmark Problem", Transportation Research Record: Journal of Transportation Research Board, 2131, 92-97. https://doi.org/10.3141/2131-09
  29. Scott, R.H., Banerji, P., Chikermane, S., Srinivasan, S., Basheer, P.A.M., Surre, F., Sun, T. and Grattan, K.T. V. (2013), "Commissioning and evaluation of a fiber-optic sensor system for bridge monitoring", IEEE Sensors J., 13 (7), 2555-2562. https://doi.org/10.1109/JSEN.2013.2256599
  30. Siriwardane, S.C. (2015), "Vibration measurement-based simple technique for damage detection of truss bridges: a case study", J. Case Studies Eng. Fail. Anal., 4, 50-58. https://doi.org/10.1016/j.csefa.2015.08.001
  31. Wang, L., Chan, T.H.T., Thambiratnam, D.P., Tan, A.C.C. and Cowled, C.J.L. (2012), "Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms", Adv. Struct. Eng., 15(5), 693-706. https://doi.org/10.1260/1369-4332.15.5.693
  32. You, T., Gardoni, P., and Hurlebaus, S. (2014), "Iterative damage index method for structural health monitoring", Struct. Monit. Maint., 1(1), 89-110. https://doi.org/10.12989/smm.2014.1.1.089
  33. Zhan, J.W., Xia, H., Chen, S.Y. and Roeck, G.D. (2011), "Structural damage identification for railway bridges based on train-induced bridge responses and sensitivity analysis", J. Sound Vib., 330, 757-770. https://doi.org/10.1016/j.jsv.2010.08.031
  34. Zhang, H., Gül, M. and Kostic, B. (2019), "Eliminating temperature effects in damage detection for civil infrastructures using times series analysis and auto-associative neural networks", J. Aerosp. Eng., 32(2), 04019001. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000987

피인용 문헌

  1. A Data-Driven Damage Assessment Tool for Truss-Type Railroad Bridges Using Train Induced Strain Time-History Response vol.22, pp.2, 2020, https://doi.org/10.1080/13287982.2021.1908710
  2. Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response vol.4, pp.2, 2021, https://doi.org/10.3390/vibration4020028
  3. Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response vol.17, pp.8, 2020, https://doi.org/10.1080/15732479.2020.1785512