Structural Engineering and Mechanics

  • 제46권3호
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  • Pages.433-445
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  • 2013
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Automated data interpretation for practical bridge identification

  • Zhang, J. (Key Laboratory of C&RC Structures of the Ministry of Education, Southeast University) ;
  • Moon, F.L. (Drexel University) ;
  • Sato, T. (International Institute for Urban Systems Engineering, Southeast University)
  • 투고 : 2012.04.02
  • 심사 : 2013.04.24
  • 발행 : 2013.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

Vibration-based structural identification has become an important tool for structural health monitoring and safety evaluation. However, various kinds of uncertainties (e.g., observation noise) involved in the field test data obstruct automation system identification for accurate and fast structural safety evaluation. A practical way including a data preprocessing procedure and a vector backward auto-regressive (VBAR) method has been investigated for practical bridge identification. The data preprocessing procedure serves to improve the data quality, which consists of multi-level uncertainty mitigation techniques. The VBAR method provides a determinative way to automatically distinguish structural modes from extraneous modes arising from uncertainty. Ambient test data of a cantilever beam is investigated to demonstrate how the proposed method automatically interprets vibration data for structural modal estimation. Especially, structural identification of a truss bridge using field test data is also performed to study the effectiveness of the proposed method for real bridge identification.

키워드

과제정보

연구 과제 주관 기관 : National Science Foundation of China

참고문헌

  1. Ali, M.R. and Okabayashi, T. (2011), "System identification of highway bridges from ambient vibration using subspace stochastic realization theories", Earthquakes and Structures, 2(2), 189-206. https://doi.org/10.12989/eas.2011.2.2.189
  2. American Society of Civil Engineers (ASCE) (2012), Structural Identification of Constructed Facilities: Approaches, Methods and Technologies for Effective Practice of St-Id, A State-of-the-Art Report, ASCE SEI Committee on Structural Identification of Constructed Systems.
  3. Brownjohn, J.M.W., Magalhaes, F., Caetano, E., Cunha, A., Au, I. and Lam, P. (2009), "Dynamic testing of the Humber Suspension Bridge", International Conference of Experimental Vibration Analysis for Civil Engineering Structures, Wroclaw, Poland, 93-102.
  4. Chu, W.C. (2003), Speech Coding Algorithms: Foundation and Evolution of Standardized Coders, John Wiley & Sons, Inc.
  5. Fukuda, Y., Feng, M.Q. and Shinozuka, M. (2010), "Cost-effective vision-based system for monitoring dynamic response of civil engineering structures", Structural Control and Health Monitoring, 17, 918-936. https://doi.org/10.1002/stc.360
  6. Heylen, W., Lammens, S. and Sas, P. (1997), Modal Analysis Theory and Testing, KUL Press, Leuven, Belgium.
  7. Ku, C.J., Cermak, J.E. and Chou, L.S. (2007), "Biased modal estimates from random decrement signatures of forced acceleration responses", Journal of Structuring Engineering, 133, 1180-1186. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:8(1180)
  8. Loh, C.H. and Liu, Y.C. (2013), "Application of recursive SSA as data pre-processing filter for stochastic subspace identification", Smart Structures and Systems, 11(1), 19-34. https://doi.org/10.12989/sss.2013.11.1.019
  9. Magalhaes, H., Cunha, A. and Caetano, E. (2009), "Online automatic identification of the modal parameters of a long span arch bridge", Mechanical Systems and Signal Processing, 23, 316-329. https://doi.org/10.1016/j.ymssp.2008.05.003
  10. Moon, F.L. and Aktan, E. (2006), "Impacts of epistemic (bias) uncertainty on structural identification of constructed (civil) systems", The Shock and Vibration Digest, 38, 399-420. https://doi.org/10.1177/0583102406068068
  11. Pakzad, S.N. and Fenves, G.L. (2009), "Statistical analysis of vibration modes of a suspension bridge using spatially dense wireless sensor network", Journal of Structural Engineering, 135(7), 863-872. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000033
  12. Pappa, R.R., James, G. and Zimmerman, D. (1998), "Autonomous modal identification of the space shuttle tail rudder", Journal of Spacecraft and Rockets, 35, 163-169. https://doi.org/10.2514/2.3324
  13. Pan, Q. (2007), "System identification of constructed civil engineering structures and uncertainty", PhD Dissertation, Drexel University, Philadelphia, PA.
  14. Pan, Q., Grimmelsman, K., Moon, F. and Aktan, E. (2013), "Mitigating epistemic uncertainty in structural identification", Journal of Structural Engineering. (In press)
  15. Samali, B., Li, J. and Choi, F.C. (2010) "Application of the damage index method for plate-like structures to timber bridges", Structural Control and Health Monitoring, 17, 849-871. https://doi.org/10.1002/stc.347
  16. Yang, Y.B., Li, Y.C. and Chang, K.C. (2012), "Effect of road surface roughness on the response of a moving vehicle for identification of bridge frequencies", Interaction and Multiscale Mechanics, 5(4), 347-368. https://doi.org/10.12989/imm.2012.5.4.347
  17. Zhang, J., Yan, R., Gao, R. and Feng, Z. (2010), "Performance enhancement of ensemble empirical mode decomposition", Mechanical Systems and Signal Processing, 24, 2104-2123. https://doi.org/10.1016/j.ymssp.2010.03.003
  18. Zhang, J., Prader, J., Grimmelsman, K.A., Moon, F.L., Aktan, E. and Shama, A. (2013), "Experimental vibration analysis for structural identification of a long span suspension bridge", ASCE Journal of Mechanical Engineering. (In press)

피인용 문헌

  1. An algorithm based on two-step Kalman filter for intelligent structural damage detection vol.22, pp.4, 2015, https://doi.org/10.1002/stc.1712