Structural Engineering and Mechanics

  • 제25권1호
  • /
  • Pages.39-52
  • /
  • 2007
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Experimental study on identification of stiffness change in a concrete frame experiencing damage and retrofit

  • Zhou, X.T. (Department Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Ko, J.M. (Department Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Y.Q. (Department Civil and Structural Engineering, The Hong Kong Polytechnic University)
  • 투고 : 2005.11.03
  • 심사 : 2006.08.11
  • 발행 : 2007.01.10
⟨ 이전 논문 다음 논문 ⟩

초록

This paper describes an experimental study on structural health monitoring of a 1:3-scaled one-story concrete frame subjected to seismic damage and retrofit. The structure is tested on a shaking table by exerting successively enhanced earthquake excitations until severe damage, and then retrofitted using fiber-reinforced polymers (FRP). The modal properties of the tested structure at trifling, moderate, severe damage and strengthening stages are measured by subjecting it to a small-amplitude white-noise excitation after each earthquake attack. Making use of the measured global modal frequencies and a validated finite element model of the tested structure, a neural network method is developed to quantitatively identify the stiffness reduction due to damage and the stiffness enhancement due to strengthening. The identification results are compared with 'true' damage severities that are defined and determined based on visual inspection and local impact testing. It is shown that by the use of FRP retrofit, the stiffness of the severely damaged structure can be recovered to the level as in the trifling damage stage.

키워드

과제정보

연구 과제 주관 기관 : The Hong Kong Polytechnic University

참고문헌

  1. Buildings Department (2004), Code of Practice for Structural Use of Concrete, The Government of the Hong Kong Special Administrative Region, Hong Kong
  2. Cabanas, L., Benito, B. and Herraiz, M. (1997), 'An approach to the measurement of the potential structural damage of earthquake ground motions', Earthq. Eng. Struct. Dyn., 26, 79-92 https://doi.org/10.1002/(SICI)1096-9845(199701)26:1<79::AID-EQE624>3.0.CO;2-Y
  3. Cosenza, E. and Manfredi, G. (2000), 'Damage indices and damage measures', Progress in Structural Engineering and Materials, 2, 50-59 https://doi.org/10.1002/(SICI)1528-2716(200001/03)2:1<50::AID-PSE7>3.0.CO;2-S
  4. DiPasquale, E., Ju, J.-W., Askar, A. and Cakmak, A.S. (1990), 'Relation between global damage indices and local stiffness degradation', J. Struct. Eng., ASCE, 116, 1440-1456 https://doi.org/10.1061/(ASCE)0733-9445(1990)116:5(1440)
  5. Elenas, A. and Meskouris, K. (2001), 'Correlation study between seismic acceleration parameters and damage indices of structures', Eng. Struct., 23, 698-704 https://doi.org/10.1016/S0141-0296(00)00074-2
  6. Elkordy, M.F., Chang, K.C. and Lee, G.C. (1994), 'A structural damage neural network monitoring system', Microcomputers in Civil Engineering, 9, 83-96 https://doi.org/10.1111/j.1467-8667.1994.tb00364.x
  7. Escobar, J.A., Sosa, J.J. and Gomez, R. (2001), 'Damage detection in framed buildings', Canadian J. Civil Eng, 28, 35-47 https://doi.org/10.1139/cjce-28-1-35
  8. Ge, M. and Lui, E.M. (2005), 'Structural damage identification using system dynamic properties', Comput. Struct., 83, 2185-2196 https://doi.org/10.1016/j.compstruc.2005.05.002
  9. Ghobarah, A., Abou-Elfath, H. and Biddah, A. (1999), 'Response-based damage assessment of structures', Earthq. Eng. Struct. Dyn., 28, 79-104 https://doi.org/10.1002/(SICI)1096-9845(199901)28:1<79::AID-EQE805>3.0.CO;2-J
  10. Hassiotis, S. and Jeong, G.D. (1995), 'Identification of stiffness reductions using natural frequencies', J. Eng. Mech., ASCE, 121, 1106-1113 https://doi.org/10.1061/(ASCE)0733-9399(1995)121:10(1106)
  11. Hjelmstad, K.D. and Shin, S. (1996), 'Crack identification in a cantilever beam from modal response', J. Sound Vib., 198, 527-545 https://doi.org/10.1006/jsvi.1996.0587
  12. Huang, C.S., Hung, S.L., Wen, C.M. and Tu, T.T. (2003), 'A neural network approach for structural identification and diagnosis of a building from seismic response data', Earthq. Eng. Struct. Dyn., 32, 187-206 https://doi.org/10.1002/eqe.219
  13. Kim, J.-T. and Stubbs, N. (2003), 'Nondestructive crack detection algorithm for full-scale bridges', J. Struct. Eng, ASCE, 129, 1358-1366 https://doi.org/10.1061/(ASCE)0733-9445(2003)129:10(1358)
  14. Kim, S.-H., Yoon, C. and Kim, B.-J. (2000), 'Structural monitoring system based on sensitivity analysis and a neural network', Computer-Aided Civil and Infrastructure Engineering, 15, 309-318
  15. Koh, C.G., See, L.M. and Balendra, T. (1995), 'Damage detection of buildings: Numerical and experimental studies', J. Struct. Eng., ASCE, 121, 1155-1160 https://doi.org/10.1061/(ASCE)0733-9445(1995)121:8(1155)
  16. Liang, R.Y., Hu, J. and Choy, F. (1992), 'Theoretical study of crack-induced eigenfrequency changes on beam structures', J. Eng. Mech., ASCE, 118, 384-396 https://doi.org/10.1061/(ASCE)0733-9399(1992)118:2(384)
  17. Morassi, A. (2001), 'Identification of a crack in a rod based on changes in a pair of natural frequencies', J. Sound Vib., 242, 577-596 https://doi.org/10.1006/jsvi.2000.3380
  18. Ni, Y.Q., Wang, B.S. and Ko, J.M. (2002), 'Constructing input vectors to neural networks for structural damage identification', Smart Materials and Structures, 11, 825-833 https://doi.org/10.1088/0964-1726/11/6/301
  19. Park, Y.J. and Ang, A.H.-S. (1985), 'Mechanistic seismic damage model of reinforced concrete', J. Struct. Eng., ASCE, 111, 722-739 https://doi.org/10.1061/(ASCE)0733-9445(1985)111:4(722)
  20. Qian, G.L., Gu S.N. and Jiang, J.S. (1990), 'The dynamic behaviour and crack detection of a beam with a crack', J. Sound Vib., 138, 233-243 https://doi.org/10.1016/0022-460X(90)90540-G
  21. Skjerbeek, P.S., Nielsen, S.R.K., Kirkegaard, P.H. and Cakmak, A.S. (1998), 'Damage localization and quantification of earthquake excited RC-frames', Earthq. Eng. Struct. Dyn., 27, 903-916 https://doi.org/10.1002/(SICI)1096-9845(199809)27:9<903::AID-EQE757>3.0.CO;2-C
  22. Stephens, J.E. and Yao, J.P.T. (1987), 'Damage assessment using response measurements', J. Struct. Eng., ASCE, 113, 787-801 https://doi.org/10.1061/(ASCE)0733-9445(1987)113:4(787)
  23. Wu, X., Ghaboussi, J. and Garrett, J.H., Jr. (1992), 'Use of neural networks in detection of structural damage', Comput. Struct., 42, 649-659 https://doi.org/10.1016/0045-7949(92)90132-J
  24. Yun, C.-B., Yi, J.-H. and Bahng, E.Y. (2001), 'Joint damage assessment of framed structures using neural networks technique', Eng. Struct., 23, 425-435 https://doi.org/10.1016/S0141-0296(00)00067-5
  25. Zhao, J., Ivan, J.N. and DeWolf, J.T. (1998), 'Structural damage detection using artificial neural networks', Journal of Infrastructure Systems, ASCE, 4, 93-101 https://doi.org/10.1061/(ASCE)1076-0342(1998)4:3(93)

피인용 문헌

  1. Finite-Element Model Updating for Assessment of Progressive Damage in a 3-Story Infilled RC Frame vol.139, pp.10, 2013, https://doi.org/10.1061/(ASCE)ST.1943-541X.0000586
  2. Finite Modeling Updating Effects on the Dynamic Response of Building Models vol.45, pp.5, 2017, https://doi.org/10.1520/JTE20150515
  3. Strengthening of deficient RC frames with high strength concrete panels: an experimental study vol.37, pp.2, 2007, https://doi.org/10.12989/sem.2011.37.2.177
  4. Debonding failure analysis of FRP-retrofitted concrete panel under blast loading vol.38, pp.4, 2007, https://doi.org/10.12989/sem.2011.38.4.479
  5. Ambient vibration based structural evaluation of reinforced concrete building model vol.15, pp.3, 2007, https://doi.org/10.12989/eas.2018.15.3.335
  6. Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter vol.12, pp.6, 2007, https://doi.org/10.1177/1687814020930460