Improving the Applicability of RDM in Identification of Multimode Structural Damping with Reduced RD Signature Quality
Publication: Journal of Structural Engineering
Volume 149, Issue 5
Abstract
The random decrement method (RDM) is often used in the experimental modal identification of existing structures. The aim of this study was to improve the applicability of RDM in the identification of multimodal structural damping, taking into account the reduced quality of the random decrement (RD) signature. This goal was achieved by introducing a new approach (Approach 2) to the classic RDM procedure based on linear regression fitting of the extreme values of the RD signature within its limited range in which the stability of this signature is not less than an acceptable threshold level. To quantify the quality of the RD signature, the concept of a stability ratio of the RD signature is introduced. In a case study analyzed in this paper, it was determined that the required value of the stability ratio is at least 92%. The effectiveness of Approach 2 was compared with the classic approach, Approach 1, based on the least-squares curve fitting technique. For this purpose, experimental modal identification of a 292-m steel cable-stayed bridge excited by daily traffic flow was carried out. The results showed improved accuracy of modal damping estimation using Approach 2 for all 11 identified bridge modes, from approximately 1% to 26%, depending on the mode number. To ensure the effectiveness of the RDM analysis, it was recommended to set the parameters of the filtration process, the number of time segments included in the RD signature averaging process, and the triggering level of these time segments. The present study can be applied to the performance of similar modal analyses of complex engineering structures, the response of which is represented by multimode vibrations.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This research was supported by statutory funds of the Polish Ministry of Education and Science, Grant No. TETA 007/22.
References
Ahmadi, E., C. Caprani, S. Živanović, and A. Heidarpour. 2019. “Assessment of human-structure interaction on a lively lightweight GFRP footbridge.” Eng. Struct. 199 (Nov): 109687. https://doi.org/10.1016/j.engstruct.2019.109687.
Asmussen, J. C., and R. Brincker. 1996. “Estimation of frequency response functions by random decrement.” In Proc., 14th Int. Modal Analysis Conf. (IMAC XIV), 246–252. Bethel, CT: Society for Experimental Mechanics.
Bendat, J. S., and A. G. Piersol. 2013. Engineering applications of correlation and spectral analysis. Chichester, UK: Wiley.
Caballol, D., Á. P. Raposo, F. G. Carrillo, and M. Morales-Segura. 2022. “Measurement of ambient vibration in empty buildings and relation to external noise.” Appl. Acoust. 186 (Jan): 108431. https://doi.org/10.1016/j.apacoust.2021.108431.
Clough, R. W., and J. Penzien. 1975. Dynamics of structures. New York: McGraw–Hill.
Cole, H. A. 1968. “On-the-line analysis of random vibrations.” In Proc., 9th Structural Dynamics and Materials Conf. (AIAA). Reston, VA: American Institute of Aeronautics and Astronautics.
Feng, Z., W. Shen, and Z. Chen. 2007. “Consistent multilevel RDT-ERA for output-only ambient modal identification of structures.” Int. J. Struct. Stab. Dyn. 17 (9): 1750106. https://doi.org/10.1142/S0219455417501061.
Friedmann, A., D. Mayer, and M. Kauba. 2010. “An approach for decentralized mode estimation based on the random decrement method.” Shock Vib. 17 (4–5): 579–588. https://doi.org/10.1155/2010/327426.
Ghorbani, E., O. Buyukozturk, and Y.-J. Cha. 2020. “Hybrid output-only structural system identification using random decrement and Kalman filter.” Mech. Syst. Signal Process. 144 (Oct): 106977. https://doi.org/10.1016/j.ymssp.2020.106977.
Huang, Z., Y. Li, X. Hua, Z. Chen, and Q. Wen. 2019. “Automatic identification of bridge vortex-induced vibration using random decrement method.” Appl. Sci.-Basel 9 (10): 2049. https://doi.org/10.3390/app9102049.
Huang, Z. F., and M. Gu. 2016. “Envelope random decrement technique for identification of nonlinear damping of tall buildings.” J. Struct. Eng. 142 (11): 04016101 https://doi.org/10.1061/(ASCE)ST.1943-541X.0001582.
Ibrahim, S. R. 1977. “Random decrement technique for modal identification of structures.” J. Spacecraft Rockets 14 (11): 696–700. https://doi.org/10.2514/3.57251.
Ibrahim, S. R., and E. C. Mikulcik. 1977. “A method for the direct identification of vibration parameters from the free response.” Shock Vib. Bull. 47 (4): 183–198.
Jeary, A. 1992. “Establishing non-linear damping characteristics of structures from non-stationary response time-histories.” Struct. Eng. 70 (Feb): 61–66.
Kim, S., and H. K. Kim. 2017. “Damping identification of bridges under nonstationary ambient vibration.” Engineering 3 (6): 839–844. https://doi.org/10.1016/j.eng.2017.11.002.
Kordestani, H., Y. Q. Xiang, X. W. Ye, and Y. K. Jia. 2018. “Application of the random decrement technique in damage detection under moving load.” Appl. Sci.-Basel 8 (5): 1–17. https://doi.org/10.3390/app8050753.
Li, X., and Q.-S. Li. 2020. “Prediction models for modal parameters of supertall buildings based on field measurements.” J. Struct. Eng. 146 (2): 06019004. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002527.
Liao, Y., and V. Wells. 2011. “Modal parameter identification using the log decrement method and band-pass filters.” J. Sound Vib. 330 (21): 5014–5023. https://doi.org/10.1016/j.jsv.2011.05.017.
Lin, C. S., and D. Y. Chiang. 2013. “Modal identification from nonstationary ambient response data using extended random decrement algorithm.” Comput. Struct. 119 (Apr): 104–114. https://doi.org/10.1016/j.compstruc.2013.01.010.
Magalhães, F., Á. Cunha, E. Caetano, and R. Brincker. 2010. “Damping estimation using free decays and ambient vibration tests.” Mech. Syst. Signal Process. 24 (5): 1274–1290. https://doi.org/10.1016/j.ymssp.2009.02.011.
Pham-Bao, T., N. Ngo-Kieu, L. Vuong-Cong, and T. Nguyen-Nhat. 2022. “Energy dissipation-based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City, Vietnam.” Struct. Control Health Monit. 29 (7): e2956. https://doi.org/10.1002/stc.2956.
Qiao, W., G. Liu, and R. Li. 2017. “Development of a modal identification method using the random decrement technique combined with HHT.” In Proc., 6th Int. Conf. on Applied Mechanics and Civil Engineering, 35–40. Boca Raton, FL: CRC Press/Balkema.
Qu, C. X., T. H. Yi, and H. N. Li. 2019. “Mode identification by eigensystem realization algorithm through virtual frequency response function.” Struct. Control Health Monit. 26 (10): e2429. https://doi.org/10.1002/stc.2429.
Qu, Y., D. He, J. Yoon, B. V. Hecke, E. Bechhoefer, and J. Zhu. 2014. “Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors—A comparative study.” Sensors 14 (1): 1372–1393. https://doi.org/10.3390/s140101372.
Sá, M. F., L. Guerreiro, A. M. Gomes, J. R. Correia, and N. Silvestre. 2017. “Dynamic behaviour of a GFRP-steel hybrid pedestrian bridge in serviceability conditions. Part 1: Experimental study.” Thin-Walled Struct. 117 (Aug): 332–342. https://doi.org/10.1016/j.tws.2017.05.013.
Stoica, P., and R. Moses. 2005. Spectral analysis of signals. Upper Saddle River, NJ: Prentice Hall.
Tamura, Y., A. Yoshida, and L. Zhang. 2005. “Damping in buildings and estimation techniques.” In Proc., 6th Asia–Pacific Conf. on Wind Engineering, 193–214. Daejeon, Korea: Techno-Press.
Tamura, Y., L. Zhang, A. Yoshida, S. Nakata, and T. Itoh. 2002. “Ambient vibration tests and modal identification of structures by FDD and 2DOF-RD technique.” In Proc., Structural Engineers World Congress. Tokyo: Shiba 2-chome Center.
Vandiver, J. K., A. B. Dunwoody, R. B. Campbell, and M. F. Cook. 1982. “A mathematical basis for the random decrement vibration signature analysis technique.” J. Mech. Des. 104 (2): 307–313. https://doi.org/10.1115/1.3256341.
Wang, H., T. Tao, Y. Gao, and F. Xu. 2018. “Measurement of wind effects on a kilometer-level cable-stayed bridge during typhoon Haikui.” J. Struct. Eng. 144 (9): 04018142. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002138.
Wen, Q., X. G. Hua, Z. Q. Chen, H. W. Niu, and X. Y. Wang. 2018. “AMD-based random decrement technique for modal identification of structures with close modes.” J. Aerosp. Eng. 31 (5): 04018057. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000882.
Wu, W. H., C. C. Chen, and J. A. Liau. 2012. “A multiple random decrement method for modal parameter identification of stay cables based on ambient vibration signals.” Adv. Struct. Eng. 15 (6): 969–982. https://doi.org/10.1260/1369-4332.15.6.969.
Yang, X. M., T. H. Yi, C. X. Qu, H. N. Li, and H. Liu. 2020. “Modal identification of high-speed railway bridges through free-vibration detection.” J. Eng. Mech. 146 (9): 04020107. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001847.
Zhou, K., and Q. S. Li. 2021. “Reliability analysis of damping estimation by random decrement technique for high-rise buildings.” Earthquake Eng. Struct. Dyn. 50 (5): 1251–1270. https://doi.org/10.1002/eqe.3396.
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Published In
Journal of Structural Engineering
Volume 149 • Issue 5 • May 2023
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© 2023 American Society of Civil Engineers.
History
Received: Apr 29, 2022
Accepted: Dec 16, 2022
Published online: Feb 16, 2023
Published in print: May 1, 2023
Discussion open until: Jul 16, 2023
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