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SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data

  • Ni, Y.Q. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Xia, Y. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Lin, W. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Chen, W.H. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Ko, J.M. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University)
  • Received : 2011.12.21
  • Accepted : 2012.05.21
  • Published : 2012.10.25

Abstract

The Canton Tower (formerly named Guangzhou New TV Tower) of 610 m high has been instrumented with a long-term structural health monitoring (SHM) system consisting of over 700 sensors of sixteen types. Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), an SHM benchmark problem for high-rise structures has been developed by taking the instrumented Canton Tower as a host structure. This benchmark problem aims to provide an international platform for direct comparison of various SHM-related methodologies and algorithms with the use of real-world monitoring data from a large-scale structure, and to narrow the gap that currently exists between the research and the practice of SHM. This paper first briefs the SHM system deployed on the Canton Tower, and the development of an elaborate three-dimensional (3D) full-scale finite element model (FEM) and the validation of the model using the measured modal data of the structure. In succession comes the formulation of an equivalent reduced-order FEM which is developed specifically for the benchmark study. The reduced-order FEM, which comprises 37 beam elements and a total of 185 degrees-of-freedom (DOFs), has been elaborately tuned to coincide well with the full-scale FEM in terms of both modal frequencies and mode shapes. The field measurement data (including those obtained from 20 accelerometers, one anemometer and one temperature sensor) from the Canton Tower, which are available for the benchmark study, are subsequently presented together with a description of the sensor deployment locations and the sensor specifications.

Keywords

References

  1. Adewuyi, A.P., Wu, Z.S. and Serker, N.H.M.K. (2009), "Assessment of vibration-based damage identification methods using displacement and distributed strain measurements", Struct. Health Monit., 8, 443-461. https://doi.org/10.1177/1475921709340964
  2. Aktan, A.E., Chase, S., Inman, D. and Pines, D.D. (2001), "Monitoring and managing the health of infrastructure systems", in: Health Monitoring and Management of Civil Infrastructure Systems, (Eds. Chase, S.B. and Aktan, A.E.), Proceedings of the SPIE Vol. 4337, SPIE, Bellingham, Washington, USA (CD-ROM).
  3. Brownjohn, J.M.W. (2007), "Structural health monitoring of civil infrastructure", Philos. T. R. Soc. A., 365(1851), 589-622. https://doi.org/10.1098/rsta.2006.1925
  4. Carden, E.P. and Fanning, P. (2004), "Vibration based condition monitoring: a review", Struct. Health Monit., 3(4), 355-377. https://doi.org/10.1177/1475921704047500
  5. Catbas, F.N. and Aktan, A.E. (2002), "Condition and damage assessment: issues and some promising indices", J. Struct. Eng.- ASCE, 128(8), 1026-1036. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:8(1026)
  6. Chang, P.C., Flatau, A. and Liu, S.C. (2003), "Health monitoring of civil infrastructure", Struct. Health Monit., 2, 257-267. https://doi.org/10.1177/1475921703036169
  7. DeWolf, J.T., Lauzon, R.G. and Culmo, M.P. (2002), "Monitoring bridge performance", Struct. Health Monit., 1(2), 129-138. https://doi.org/10.1177/1475921702001002001
  8. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib., 30, 91-105. https://doi.org/10.1177/058310249803000201
  9. Fujino, Y., Siringoringo, D.M. and Abe, M. (2009), "The needs for advanced sensor technologies in risk assessment of civil infrastructures", Smart Struct. Syst., 5(2), 173-191. https://doi.org/10.12989/sss.2009.5.2.173
  10. Glisic, B., Inaudi, D. and Casanova, N. (2009), "SHM process lessons learned in 250 SHM projects", in: Proceedings of the 4th International Conference on Structural Health Monitoring and Intelligent Infrastructure, Zurich, Switzerland (CD-ROM).
  11. Gorl, E. and Link, M. (2003), "Damage identification using changes of eigenfrequencies and mode shapes", Mech. Syst. Signal Pr., 17(1), 103-110. https://doi.org/10.1006/mssp.2002.1545
  12. Hao, H. and Xia, Y. (2002), "Vibration-based damage detection of structures by genetic algorithm", J. Comput. Civil Eng., 16(3), 222-229. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(222)
  13. Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  14. Lu, Z.R. and Law, S.S. (2007), "Features of dynamic response sensitivity and its application in damage detection", J. Sound Vib., 303(1-2), 305-329. https://doi.org/10.1016/j.jsv.2007.01.021
  15. Ni, Y.Q., Wong, K.Y. and Xia, Y. (2011), "Health checks through landmark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. https://doi.org/10.1260/1369-4332.14.1.103
  16. Ni, Y.Q., Xia, Y., Liao, W.Y. and Ko, J.M. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower", Struct. Control Health Monit., 16(1), 73-98. https://doi.org/10.1002/stc.303
  17. Ou, J. and Li, H. (2009), Structural health monitoring research in China: trends and applications, in: Structural Health Monitoring of Civil Infrastructure Systems, (Eds., Karbhari, V.M. and Ansari, F.), Woodhead Publishing, Cambridge, UK, 463-516.
  18. Rolander, D.D., Phares, B.M., Graybeal, B.A., Moore, M.E. and Washer, G.A. (2001), "Highway bridge inspection: state-of-the-practice survey", Transport. Res. Record, 1749, 73-81. https://doi.org/10.3141/1749-12
  19. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R. and Czarnecki, J.J. (2004), A review of structural health monitoring literature: 1996-2001, Los Alamos National Laboratory, Report LA-13976-MS, Los Alamos, USA.
  20. Wang, M.L. and Yim, J. (2010), "Sensor enriched infrastructure system", Smart Struct. Syst., 6(3), 309-333. https://doi.org/10.12989/sss.2010.6.3.309
  21. Wong, K.Y. (2004), "Instrumentation and health monitoring of cable-supported bridges", Struct. Control Health Monit., 11(2), 91-124. https://doi.org/10.1002/stc.33
  22. Xia, Y., Ni, Y.Q., Zhang, P, Liao, W.Y. and Ko, J.M. (2011), "Stress development of a super-tall structure during construction: field monitoring and numerical analysis", Comput. Aided Civil Infrastruct. Eng., 26(7), 542-559. https://doi.org/10.1111/j.1467-8667.2010.00714.x
  23. Yun, C.B., Lee, J.J. and Koo, K.Y. (2011), "Smart structure technologies for civil infrastructures in Korea: recent research and applications", Struct. Infrastruct. Eng., 7(9), 673-688. https://doi.org/10.1080/15732470902720109

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