DOI QR코드

DOI QR Code

Accuracy and Stability Evaluation of Modal Damping Ratio Identification Techniques for High-rise Buildings

고층건물의 모달 감쇠비 식별 기법들의 정확도 및 안정성 평가

  • Received : 2020.05.26
  • Accepted : 2020.08.05
  • Published : 2020.08.30

Abstract

Structural health monitoring (SHM) technology has been developed and applied to ensure the safety of building structures. For the safety assessment through SHM, it is necessary to identify the modal parameters of the structure through the structural responses obtained using various sensors. System identification (SI) techniques for identifying modal parameters of building structures have been developed in various ways. Although various SI techniques have been developed, The study comparing the accuracy and stability of estimation results for modal damping ratio from currently representative SI techniques has not been conducted. However, It is important to assess the accuracy and stability of SI techniques, because the exact solution of the modal damping ratio cannot be known. In this study, The accuracy and stability for modal damping ratio of SI techniques have been identified using enhanced frequency domain decomposition (EFDD), stochastic subspace identification covariance-driven (SSI-COV), SSI data-driven (SSI-DATA), and numerical algorithms for subspace state-space system identification (N4SID) methods, which are currently representative SI methods for high-rise buildings in the field. The accuracy and stability of each method were compared and analyzed through the results of the probability density function of the identified modal damping ratio. As a result, there was a difference in accuracy and stability identified in each method. Furthermore, it was confirmed that each method's difference also occurred in the computational time required for analysis.

Keywords

Acknowledgement

본 연구는 한국연구재단의 지원을 받아 수행된 연구임(No. 2018R1A5A1025137)

References

  1. Brincker, R., Zhang, L., & Andersen, P. (2000, February). Modal identification from ambient responses using frequency domain decomposition. In Proc. of the 18*'International Modal Analysis Conference (IMAC), San Antonio, Texas.
  2. Brincker, R., Ventura, C., & Andersen, P. (2001, February). Damping estimation by frequency domain decomposition. In Proceedings of the 19th international modal analysis conference (IMAC), 5-8.
  3. Brincker, R., Ventura, C., & Andersen, P. (2003). Why output only modal analysis is a desirable tool for a wide range of practical applications. In Proceedings of IMAC-21: A Conference on Structural Dynamics, February 3-6.
  4. Brincker, R., & Zhang, L. (2009, May). Frequency domain decomposition revisited. In Proc. 3rd Int. Operational Modal Analysis Conf.(IOMAC'09), 615-626.
  5. Cho, S. (2004). Modal Identification Using FDD and SSI for Dynamic Properties of Buildings. Proceeding of Annual Conference of the Architectural Institute of Korea, 24(2), 293-296.
  6. Cho, S. (2019). FE Model Updating of Tall Buildings Using Output-only Modal Data. Transactions of the Korean Society for Noise and Vibration Engineering, 29(1), 131-140. https://doi.org/10.5050/KSNVE.2019.29.1.131
  7. Golub, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). JHU press.
  8. Gorski, P. (2017). Dynamic characteristic of tall industrial chimney estimated from GPS measurement and frequency domain decomposition. Engineering Structures, 148, 277-292. https://doi.org/10.1016/j.engstruct.2017.06.066
  9. Hasan, M. D. A., Ahmad, Z. A. B., Leong, M. S., & Hee, L. M. (2018). Enhanced frequency domain decomposition algorithm: a review of a recent development for unbiased damping ratio estimates. Journal of Vibroengineering, 20(5). 1919-1936. https://doi.org/10.21595/jve.2018.19058
  10. Hu, X., Wang, B., & Ji, H. (2013). A wireless sensor network-based structural health monitoring system for highway bridges. Computer-Aided Civil and Infrastructure Engineering, 28(3), 193-209. https://doi.org/10.1111/j.1467-8667.2012.00781.x
  11. Jung, H., Kim, H., & Choi, S. (2015). Evaluation of Amplitude-Dependent Damping Ratio and Natural Frequency of a Tall RC Building based on Long Term Measurement Data. Journal of the Architectural Institute of Korea, 31(1), 11-18.
  12. Kang, F., Li, J., Zhao, S., & Wang, Y. (2019). Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. Engineering Structures, 180, 642-653. https://doi.org/10.1016/j.engstruct.2018.11.065
  13. Kim, D., Oh, B. K., Park, H. S., Shim, H. B., & Kim, J. (2017). Modal identification for high-rise building structures using orthogonality of filtered response vectors. Computer-Aided Civil and Infrastructure Engineering, 32(12), 1064-1084. https://doi.org/10.1111/mice.12310
  14. Koo, H., & Kim, H. (2015). Natural Period and Damping Ratio of RC Buildings for Serviceability Design. Journal of the Architectural Institute of Korea, 31(2), 37-44.
  15. Larimore, W.E. "Canonical variateanalysis in identification, filtering and adaptive control." Proceedings of the 29th IEEE Conference on Decision and Control, 1990, 596-604.
  16. Loh, C. H., Liu, Y. C., & Ni, Y. Q. (2012). SSA-based stochastic subspace identification of structures from output-only vibration measurements. Smart Structures and Systems, 10(4_5), 331-351. https://doi.org/10.12989/sss.2012.10.4_5.331
  17. Magalhaes, F., Cunha, A., Caetano, E., & Brincker, R. (2010). Damping estimation using free decays and ambient vibration tests. Mechanical Systems and Signal Processing, 24(5), 1274-1290. https://doi.org/10.1016/j.ymssp.2009.02.011
  18. Ma, H., Pan, J., Lv, L., Xu, G., Ding, F., Alsaedi, A., & Hayat, T. (2019). Recursive algorithms for multivariable output-error-like ARMA systems. Mathematics, 7(6), 558. https://doi.org/10.3390/math7060558
  19. Makki Alamdari, M., Anaissi, A., Khoa, N. L., & Mustapha, S. (2019). Frequency domain decompositionbased multisensor data fusion for assessment of progressive damage in structures. Structural Control and Health Monitoring, 26(2), e2299. https://doi.org/10.1002/stc.2299
  20. Ni, Y. Q., Xia, Y., Lin, W., Chen, W. H., & Ko, J. M. (2012). SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data. Smart Structures and Systems.
  21. Oh, B. K., Hwang, J. W., Kim, Y., Cho, T., & Park, H. S. (2015). Vision-based system identification technique for building structures using a motion capture system. Journal of Sound and Vibration, 356, 72-85. https://doi.org/10.1016/j.jsv.2015.07.011
  22. Oh, B. K., Kim, K. J., Kim, Y., Park, H. S., & Adeli, H. (2017). Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings. Applied Soft Computing, 58, 576-585. https://doi.org/10.1016/j.asoc.2017.05.029
  23. Park, S., Min, K., & Choi, J. (2011). Ambient Vibration Analysis of Heunginjimun. Journal of the Architectural Institute of Korea, 27(5), 19-26.
  24. Park, H., Moon, D., & Lee, S. (2017). Estimation of Wind-induced Responses of a Tall Building Structure for Designing Active Controller. Journal of the computational structural engineering institute of Korea, 30(2), 159-167. https://doi.org/10.7734/COSEIK.2017.30.2.159
  25. Park, H. S., & Oh, B. K. (2018). Real-time structural health monitoring of a supertall building under construction based on visual modal identification strategy. Automation in Construction, 85, 273-289. https://doi.org/10.1016/j.autcon.2017.10.025
  26. Peeters, B., & De Roeck, G. (1999). Reference-based stochastic subspace identification for output-only modal analysis. Mechanical systems and signal processing, 13(6), 855-878. https://doi.org/10.1006/mssp.1999.1249
  27. Peeters, B., & De Roeck, G. (2001). Stochastic system identification for operational modal analysis: a review. J. Dyn. Sys., Meas., Control, 123(4), 659-667. https://doi.org/10.1115/1.1410370
  28. Pioldi, F., Ferrari, R., & Rizzi, E. (2016). Output-only modal dynamic identification of frames by a refined FDD algorithm at seismic input and high damping. Mechanical Systems and Signal Processing, 68, 265-291 https://doi.org/10.1016/j.ymssp.2015.07.004
  29. Qin, S., Kang, J., & Wang, Q. (2016). Operational modal analysis based on subspace algorithm with an improved stabilization diagram method. Shock and Vibration, 2016.
  30. Raghu, M., Gilmer, J., Yosinski, J., & Sohl-Dickstein, J. (2017). Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability. In Advances in Neural Information Processing Systems, 6076-6085
  31. Reynders, E., Pintelon, R., & De Roeck, G. (2008). Uncertainty bounds on modal parameters obtained from stochastic subspace identification. Mechanical Systems and Signal Processing, 22(4), 948-969. https://doi.org/10.1016/j.ymssp.2007.10.009
  32. Reynders, E. (2012). System identification methods for (operational) modal analysis: review and comparison. Archives of Computational Methods in Engineering, 19(1), 51-124. https://doi.org/10.1007/s11831-012-9069-x
  33. Rodrigues, J., Brincker, R., & Andersen, P. (2004, January). Improvement of frequency domain output-only modal identification from the application of the random decrement technique. In Proc. 23rd Int. Modal Analysis Conference, Deaborn, MI (pp. 92-100).
  34. Sarlo, R., & Tarazaga, P. A. (2019). Modal parameter uncertainty estimates as a tool for automated operational modal analysis: Applications to a smart building. In Dynamics of Civil Structures, Volume 2 (pp. 177-182). Springer, Cham.
  35. Tamura, Y., Zhang, L., Yoshida, A., Nakata, S., & Itoh, T. (2002, October). Ambient vibration tests and modal identification of structures by FDD and 2DOF-RD technique. In Structural Engineers World Congress.
  36. Van Overschee, P., & De Moor, B. (1993). Subspace algorithms for the stochastic identification problem. Automatica, 29(3), 649-660. https://doi.org/10.1016/0005-1098(93)90061-W
  37. Van Overschee, P., & De Moor, B. L. (1994). N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30(1), 75-93. https://doi.org/10.1016/0005-1098(94)90230-5
  38. Van Overschee, P., & De Moor, B. L. (2012). Subspace identification for linear systems: Theory-Implementation-Applications. Springer Science & Business Media.