Abstract
The accurate identification of modal parameters is a critical issue in the determination of features of civil structures. In this paper, a novel method based on the multisynchrosqueezing transform (MSST) is proposed to identify modal parameters, including natural frequencies, damping ratios and mode shapes of civil structures. The MSST consists of multiple operations of a synchrosqueezing transform so that the time-frequency representation of an analyzed signal becomes more concentrated, which allows more accurate decomposition of the signal. To identify modal parameters based on the MSST, first, the natural extraction technique is used to obtain a free vibration response from a measured ambient vibration response. Second, the free vibration response is decomposed into several modes by using the MSST, and mode shape vectors can be obtained from the decomposed modes for all measurements. Then, instantaneous phases and instantaneous amplitudes of the modes are obtained by using the Hilbert transform. Finally, a least-squares curve fitting technique is performed on the instantaneous phases and instantaneous amplitudes to extract natural frequencies and damping ratios. Two numerical examples, a 3-degree-of-freedom free vibration response signal and a four-story frame steel structure subjected to environmental vibration, are used to demonstrate the applicability of the MSST-based method. In addition, an experimental validation based on a pedestrian overpass, located in Tufts University, United States, is conducted. Case analyses indicate that the MSST-based method can easily identify high-quality natural frequencies and damping ratios from measurements of structures.
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References
Amezquita-Sanchez JP, Park HS, Adeli H (2017) A novel methodology for modal parameters identification of large smart structures using music, empirical wavelet transform, and hilbert transform. Eng Struct 147:148–159
Bagheri A, Ozbulut OE, Harris DK (2018) Structural system identification based on variational mode decomposition. J Sound Vib 417:182–197
Behmanesh Iman, Moaveni Babak (2016) Accounting for environmental variability, modeling errors, and parameter estimation uncertainties in structural identification. J Sound Vib 374:92–110
Caicedo JM, Dyke SJ, Johnson EA (2004) Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: simulated data. J Eng Mech 130(1):49–60
Daubechies I, Jianfeng L, Hau-Tieng W (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmonic Anal 30(2):243–261
Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544
Johnson EA, Lam HF, Katafygiotis LS, Beck JL (2004) Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. J Eng Mech 130(1):3–15
Feldman M (2014) Hilbert transform methods for nonparametric identification of nonlinear time varying vibration systems. Mech Syst Signal Proces 47(1–2):66–77
Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010
He XH, Hua XG, Chen ZQ, Huang FL (2011) EMD-based random decrement technique for modal parameter identification of an existing railway bridge. Eng Struct 33(4):1348–1356
Hermans L, Van der Auweraer H (1999) Modal testing and analysis of structures under operational conditions: industrial applications. Mech Syst Signal Process 13(2):193–216
Hilbert D (1912) Begründung der kinetischen gastheorie. Math Ann 72(4):562–577
James GH, Carne TG, Lauffer JP et al (1995) The natural excitation technique (next) for modal parameter extraction from operating structures. Modal Anal Int J Anal Exp Modal Anal 10(4):260
Jin H, Lin J, Chen X, Yi C (2019) Modal parameters identification method based on symplectic geometry model decomposition. Shock Vib 2019(12):1–26
Keyhani A, Mohammadi S (2018) Structural modal parameter identification using local mean decomposition. Meas Sci Technol 29(2):025003
Lazhari M, Sadhu A (2019) Decentralized modal identification of structures using an adaptive empirical mode decomposition method. J Sound Vib 447:20–41
Li H, Li Z, Mo W (2017) A time varying filter approach for empirical mode decomposition. Signal Process 138:146–158
Luo Z, Liu T, Yan S, Qian M (2018) Revised empirical wavelet transform based on auto-regressive power spectrum and its application to the mode decomposition of deployable structure. J Sound Vib 431:70–87
McNeill SI (2016) Decomposing a signal into short-time narrow-banded modes. J Sound Vib 373:325–339
Moaveni B, Behmanesh I (2012) Effects of changing ambient temperature on finite element model updating of the Dowling Hall footbridge. Eng Struct 43:58–68
Pan H, Yang Y, Li X, Zheng J, Cheng J (2019) Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis. Mech Syst Signal Process 114:189–211
Perez-Ramirez CA, Amezquita-Sanchez JP, Adeli H, Valtierra-Rodriguez M, Camarena-Martinez D, Romero-Troncoso RJ (2016) New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform. Eng Appl Artif Intell 48:1–12
Rainieri C, Gargaro D, Fabbrocino G, Maddaloni G, Di Sarno L, Prota A, Manfredi G (2018) Shaking table tests for the experimental verification of the effectiveness of an automated modal parameter monitoring system for existing bridges in seismic areas. Struct Control Health Monit 25(7):e2165
Xin Yu, Hao H, Li J (2019) Operational modal identification of structures based on improved empirical wavelet transform. Struct Control Health Monit 26(3):e2323
Yan B, Miyamoto A (2006) A comparative study of modal parameter identification based on wavelet and Hilbert–Huang transforms. Comput-Aided Civ Infrastruct Eng 21(1):9–23
Yang JN, Lei Y, Pan S, Huang N (2003) System identification of linear structures based on Hilbert-Huang spectral analysis. Part 1: normal modes. Earthq Eng Struct Dyn 32(9):1443–1467
Yang JN, Lei Y, Pan S, Huang N (2003) System identification of linear structures based on Hilbert-Huang spectral analysis. Part 2: Complex modes. Earthq Eng Struct Dyn 32(10):1533–1554
Yang J, Li P, Yang Y, Dian X (2018) An improved EMD method for modal identification and a combined static-dynamic method for damage detection. J Sound Vib 420:242–260
Gang Yu, Wang Z, Zhao P (2018) Multisynchrosqueezing transform. IEEE Trans Ind Electron 66(7):5441–5455
Zhou W, Feng Z, Liu D, Wang X, Chen B (2020) Modal parameter identification of structures based on short-time narrow-banded mode decomposition. Adv Struct Eng 23(14):3062–3074
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The authors are grateful for the financial support from the National Natural Science Foundation of China through Grant no. 51868045 and from Shaanxi Institute of Technology through Grant no. Gfy20-03.
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Sun, H., Di, S., Du, Z. et al. Application of multisynchrosqueezing transform for structural modal parameter identification. J Civil Struct Health Monit 11, 1175–1188 (2021). https://doi.org/10.1007/s13349-021-00500-0
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DOI: https://doi.org/10.1007/s13349-021-00500-0