Identification of modal parameters of long-span bridges under various wind velocities

Advances in Bridge Engineering - Tập 3 - Trang 1-21 - 2022
Siying Lu1, Lei Yan1,2,3, Xuhui He1,2,3, Hui Guo4
1School of Civil Engineering, Central South University, Changsha, China
2National Engineering Research Center for High-Speed Railway Construction, Changsha, China
3Hunan Provincial Key Laboratory for Disaster Prevention and Mitigation of Rail Transit Engineering Structures, Changsha, China
4Railway Engineering Research Institute, China Academy of Railway Sciences, Beijing, China

Tóm tắt

The modal parameters identification of bridges under non-stationary environmental excitation has caught the attention of researchers. This paper studies the non-stationarity of wind velocity, and extracts the time-varying mean wind velocity based on a discrete wavelet transform and recursive quantitative analysis. The calculated turbulence intensity and turbulence integral scale under the non-stationary model are smaller than those under the stationary model, especially the turbulence integral scale. The empirical wavelet transform is used to identify the modal parameters of long-span bridges, and the power spectral density spectrum is proposed as a replacement for the Fourier spectrum as the basis of the frequency band selection. The bridge modal parameters are then compared using the covariance-driven stochastic subspace system identification method (SSI-COV) and the Hilbert transform method based on an improved empirical wavelet transform (EWT-HT). Both methods can accurately identify the modal frequency, and the absolute difference between these two methods is equal to 0.003 Hz. The wind velocity results in a change of less than 1% in the modal frequency. The absolute difference between the modal damping ratios identified using SSI-COV and EWT-HT is significant and can reach 0.587%. The modal damping ratios are positively correlated with the mean wind velocities, which aligns with the quasi-steady assumption. In addition, the applicability of SSI-COV and EWT-HT is also evaluated using the standard deviation, coefficient of variation, and range dispersion indicators. The results show that the EWT-HT is more applicable to the identification of the modal parameters of long-span bridges under non-stationary wind velocities.

Tài liệu tham khảo

Amezquita-Sanchez JP, Park HS, Hojjat A (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 Cai K, Li X, Zhi LH, Han XL (2021) Extraction of optimal time-varying mean of non-stationary wind speeds based on empirical mode decomposition. Struct Eng Mech 77(3):355–368 Chang Y, Zhao L, Zou Y, Ge YJ (2022) A revised Scruton number on rain-wind-induced vibration of stay cables. J Wind Eng Ind Aerodyn 230:105166 Chen Y, Yang H (2012) Multiscale recurrence analysis of long-term nonlinear and non-stationary time series. Chaos, Solitons Fractals 45(7):978–987 Comanducci G, Ubertini F, Materazzi AL (2015) Structural health monitoring of suspension bridges with features affected by changing wind speed. J Wind Eng Ind Aerodn 141:12–26 Cross EJ, Koo KY, Brownjohn JMW, Worden K (2013) Long-term monitoring and data analysis of the Tamar Bridge. MSSP 35(1):16–34 Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010 Hsu TY, Chien CC, Shiao SY, Chen CC (2020) Analysis of environmental and typhoon effects on modal frequencies of a power transmission tower. Sensors 20(18):5169 Ibrahim SR (2001) Efficient random decrement computation for identification of ambient responses. Proceedings of the 19th International Modal Analysis Conference (IMAC), Florida Jiang F, Zhang M, Li Y, Yan T, Zhang J (2022) Field measurement analysis of wind parameters and non-stationary characteristics in mountainous terrain: focusing on cooling windstorms. J Wind Eng Ind Aerodyn 230:105175 Kareem A, Gurley K (1996) Damping in structures: Its evaluation and treatment of uncertainty. J Wind Eng Ind Aerodn 59:131–157 Kim S, Jung H, Kong MJ, Lee DK, An YK (2019) (2019) In-situ data-driven buffeting response analysis of a cable-stayed bridge. Sensors 19:3048 Li H, Li SL, Ou JP, Li HW (2010) Modal identification of bridges under varying environmental conditions: temperature and wind effects. Struct Control Health Moni 17(5):495–512 Li H, Laima SJ, Ou JP, Zhou WS, Yu Y, Li N, Liu ZQ (2011) Investigation of vortex-induced vibration of a suspension bridge with two separated steel box girders based on field measurements. Eng Struct 33(6):1894–1907 Mallat S (1989) Multiresolution approximations and wavelet orthonormal bases of L2(R). Trans Am Math Soc 315(1):69–88 Mao JX, Wang H, Xun ZX, Zou ZQ (2017) Variability analysis on modal parameters of Runyang Bridge during Typhoon Masta. Smart Struct Syst 19(6):653–663 Meng X, Nguyen DT, Owen JS, Xie Y, Psimoulis P, Ye G (2019) Application of GeoSHM system in monitoring extreme wind events at the Forth Road Bridge. Remote Sensing 11(23):2799 Ministry of Transportation and Communication of PRC (2018) JTG/T 3360–01–2018 Wind-Resistant Design Specification for Highway Bridges. China Communications Press Co., Ltd., Beijing Simiu E, Yeo DH (2019) Wind Effects on Structures: Modern Structural Design for Winds, 4th edn. John Wiley & Sons, Inc, New York Tubino F, Solari G (2020) Time varying mean extraction for stationary and non-stationary winds. J Wind Eng Ind Aerodyn 117:104187 Ubertini F (2013) On damage detection by continuous dynamic monitoring in wind-excited suspension bridges. Mecc 48(5):1031–1051 Van Overschee P, De Moor B (1997) Subspace Identification for Linear Systems: Theory-Implementation-Applications. Springer Science & Business Media, Berlin Wang H, Mao JX, Huang JH, Li AQ (2016) Modal identification of Sutong cable-sstayed bridge during Typhoon Haikui using Wavelet transform method. J Perform Constr Facil 30(5):04016001 Wang SQ, Zhao L, Cao SY, Zhang YF, Yin F, Ge YJ (2018) (2018) A comparison study on gust factor considering non-stationary effects under typhoon and monsoon conditions. Adv Struct Eng 21(12):1853–1864 Wang H, Mao JX, Xu ZD (2020) Investigation of dynamic properties of a long-span cable-stayed bridge during typhoon events based on structural health monitoring. J Wind Eng Ind Aerodn 201:104172 Webber CL Jr, Zbilut JP (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76(2):965–973 Yan L, Ren L, He XH, Lu SY, Guo H, Wu T (2020) Strong wind characteristics and buffeting response of a cable-stayed bridge under construction. Sensors 20:1228 Yang H (2011) Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals. ITBE 58(2):339–347 Zhang YM (2019) Experimental determination of aerodynamic admittance functions of a truss girder. School of Civil Engineering, Central South Univerisity, Changsha Zou YF, Lei X, Yan L, He XH, Nie M, Xie WP, Luo XY (2020) Full-scale measurements of wind structure and dynamic behaviour of a transmission tower during a typhoon. Struct Infrastruct E 16(5):820–830