Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A structural damage detection approach using train-bridge interaction analysis and soft computing methods

  • He, Xingwen (Faculty of Engineering, Hokkaido University) ;
  • Kawatani, Mitsuo (Graduate School of Engineering, Kobe University) ;
  • Hayashikawa, Toshiro (Faculty of Engineering, Hokkaido University) ;
  • Kim, Chul-Woo (Graduate School of Engineering, Kobe University) ;
  • Catbas, F. Necati (Department of Civil, Environ. and Constr. Eng., University of Central Florida) ;
  • Furuta, Hitoshi (Faculty of Informatics, Kansai University)
  • Received : 2012.12.01
  • Accepted : 2013.08.30
  • Published : 2014.05.25
⟨ Previous Next ⟩

Abstract

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.

Keywords

References

  1. Agabein, M.E. (1971), "The effect of various damping assumptions on the dynamic response of structure", Bull. Int. Inst. Seismology Earthq. Eng., 8, 217-236.
  2. Alvandi, A. and Cremona, C. (2006), "Assessment of vibration-based damage identification techniques", J. Sound Vib., 292, 179-202. https://doi.org/10.1016/j.jsv.2005.07.036
  3. Bathe, K.J. (1982), Finite element procedures in engineering analysis, Prentice-Hall, Englewood Cliffs, N. J.
  4. Clough R.W. and Penzien J. (1975), Dynamics of structures, McGraw-Hill, Inc.
  5. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib. Digest, 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  6. Goldberg, D.E. (1989), Genetic algorithms in search, optimization, and machine learning, Addison Wesley longman, Inc.
  7. He, X., Kawatani, M., Hayashikawa, T. and Matsumoto, T. (2011), "Numerical analysis on seismic response of Shinkansen bridge-train interaction system under moderate earthquakes", Earthq. Eng. Eng. Vib., 10(1), 85-97. https://doi.org/10.1007/s11803-011-0049-1
  8. He, X., Noda, Y., Hayashikawa, T., Kawatani, M. and Matsumoto, T. (2011), "An analytical approach to coupled vibration of curved rationalized girder bridges and running vehicles", Proceedings of the 12th East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-12), Procedia Engineering (Elsevier Ltd.).
  9. Kartam, N., Flood, I., Garrett, J.H. and Agrawal, G. (1997), Artificial neural networks for civil engineers: fundamentals and applications, American Society of Civil Engineers.
  10. Kawatani, M., KIM, C.W., Doi, H. and Toshinami, T. (2012), "Damage identification of bridges using traffic-induced vibration data", Proceedings of the 2012 World Congress on Advances in Civil, Environmental, and Materials Research (ACEM'12), 2103-2113, CD-ROM.
  11. Kim, C.W., Kawatani, M. and Kim, K.B. (2005), "Three-dimensional dynamic analysis for Bridge-vehicle Interaction with Roadway roughness", Comput. Struct., 83, 1627-1645. https://doi.org/10.1016/j.compstruc.2004.12.004
  12. Kim, C.W. and Kawatani, M. (2008), "Pseudo-static approach for damage identification of bridges based on coupling vibration with a moving vehicle", Struct. Infrastruct. E., 4(5), 371-379. https://doi.org/10.1080/15732470701270082
  13. Kim, C.W., Isemoto, R., Sugiura, K. and Kawatani, M. (2012), "Structural diagnosis of bridges using traffic-induced vibration measurements", Proceedings of the 6th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2012), 1-8, CD-ROM.
  14. Kim, C.W., Kawatani, M. and Hao, J. (2012), "Modal parameter identification of short span bridges under a moving vehicle by means of multivariate AR model", Struct. Infrastruct. E., 8(5), 459-472. https://doi.org/10.1080/15732479.2010.539061
  15. Koh, C.G. and Perry, M.J. (2009), Structural identification and damage detection using genetic algorithms, CrcPr I Llc.
  16. Matsuura, A. (1976), "A study of dynamic behavior of bridge girder for high speed railway", Proceedings of the Japan Society of Civil Engineers, 256, 35-47, (In Japanese).
  17. Newmark, N.M. (1959), "A method of computation for structural dynamics", J. Eng. Mech. - ASCE, 85(3), 67-94.
  18. Ni, Y.Q., Zhou, H.F., Chan, K.C. and Ko, J.M. (2008), "Modal flexibility analysis of cable-stayed bridge Ting Kau bridge for damage identification", Comput. Aided Civil. Infrastruct. E., 23 (3), 223-236. https://doi.org/10.1111/j.1467-8667.2008.00521.x
  19. Perry, M.J., Koh, C.G. and Choo, Y.S. (2006), "Modified genetic algorithm strategy for structural identification", Comput. Struct., 84, 529-540. https://doi.org/10.1016/j.compstruc.2005.11.008
  20. Siringoringo, D.M. and Fujino, Y. (2006), "Observed dynamic performance of the Yokohama Bay Bridge from system identification using seismic records", Struct. Control Health Monit., 13, 226-244. https://doi.org/10.1002/stc.135
  21. Wakui, H., Matsumoto, N., Matsuura, A. and Tanabe M. (1995), "Dynamic interaction analysis for railway vehicles and structures", J. Struct. Earthq. Eng. - JSCE, 31(513), 129-138, (in Japanese).
  22. Weaver, W. Jr. and Johnston, P.R. (1987), Structural dynamics by finite elements, Prentice-Hall, Inc.
  23. Yoshida, I., Kim, C.W. and Kawatani, M. (2006), "Simulation of damage identification for bridges based on coupling vibration with moving vehicles", Proceedings of the 4th International Symposium on Steel Structures (ISSS '06).
  24. Yoshida, I., Kim, C.W. and Kawatani, M. (2008), "Regularization of inverse problem for damage detection", Proceedings of the 4th International Conference on Bridge Maintenance, Safety and Management (IABMAS2008).
  25. Yun, C.B. and Bahng, E.Y. (2000), "Substructural identification using neural networks", Comput. Struct., 77, 41-52. https://doi.org/10.1016/S0045-7949(99)00199-6
  26. Zhan, J.W., Xia, H., Chen, S.Y. and De Roeck, G. (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

Cited by

  1. Smart monitoring system with multi-criteria decision using a feature based computer vision technique vol.15, pp.6, 2015, https://doi.org/10.12989/sss.2015.15.6.1583
  2. Railway bridge damage detection using vehicle-based inertial measurements and apparent profile vol.153, 2017, https://doi.org/10.1016/j.engstruct.2017.10.023
  3. Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis vol.119, pp.None, 2014, https://doi.org/10.1016/j.ymssp.2018.10.006
  4. A crowdsourcing-based methodology using smartphones for bridge health monitoring vol.18, pp.5, 2019, https://doi.org/10.1177/1475921718815457
  5. Effect of random structural damage on vehicle-track-bridge coupled response vol.29, pp.1, 2014, https://doi.org/10.1177/1056789519860203
  6. Extraction of bridge information based on the double-pass double-vehicle technique vol.25, pp.6, 2014, https://doi.org/10.12989/sss.2020.25.6.679