TIME–FREQUENCY ANALYSIS OF A SUSPENSION BRIDGE BASED ON GPS

Abstract This paper describes the results obtained from full-scale measurements of Humen bridge, which is the second longest suspension bridge in China. A real-time kinematic (RTK) global positioning system (GPS) has been developed and installed on the Humen bridge for on-line monitoring of bridge deck movements. The field wind-induced vibration data were measured by this monitoring system. Three system identification techniques are then adopted in the modal analysis of the wind-induced vibration response: the time–frequency Wigner distribution (WD) technique, the frequency-domain fast Fourier transform (FFT) technique and the time-domain auto-regressive moving average vector (ARMAV) technique. The WD technique can recognize close modal coupling and non-stationary response. The FFT technique can on site verify the quality of the measurements, but its frequency resolution is low and damping estimates are unreliable. The ARMAV method allows for gaining high-frequency resolution. However, it is strictly related to the stationary hypothesis. It is a general conclusion that we can improve the quality of the analysis and get more precise characteristics of the signal by these three methods. In addition, the WD combined with ARMAV seems to be the best case in quantitative analysis of fast-changing vibration signals.

[1]  N. Yen,et al.  Time and frequency representation of acoustic signals by means of the Wigner distribution function: Implementation and interpretation , 1987 .

[2]  J. S. Bolton,et al.  The Application of the Wigner Distribution to the Identification of Structure-borne Noise Components , 1993 .

[3]  Yeong-Bin Yang,et al.  Dynamic Testing and System Identification of a Multi-Span Highway Bridge , 1999 .

[4]  A. M. Abdel-Ghaffar,et al.  Ambient Vibration Studies of Golden Gate Bridge , 1985 .

[5]  G. Reinsel Elements of Multivariate Time Series Analysis , 1995 .

[6]  H. Akaike Maximum likelihood identification of Gaussian autoregressive moving average models , 1973 .

[7]  Rosario Ceravolo,et al.  The use of wind excitation in structural identification , 1998 .

[8]  A. M. Abdel-Ghaffar,et al.  Ambient Vibration Studies of Golden Gate Bridge: I. Suspended Structure , 1985 .

[9]  A. Benveniste,et al.  STATE SPACE FORMULATION : A SOLUTION TO MODAL PARAMETER ESTIMATION , 1991 .

[10]  Helmut Lütkepohl,et al.  Introduction to multiple time series analysis , 1991 .

[11]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[12]  Gethin Wyn Roberts,et al.  MONITORING OF STRUCTURES USING THE GLOBAL POSITIONING SYSTEM. , 1999 .

[13]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[14]  W. Gersch,et al.  Time Series Methods for the Synthesis of Random Vibration Systems , 1976 .

[15]  B. A. D. Piombo,et al.  ARMAV techniques for traffic excited bridges , 1998 .

[16]  S. Pandit,et al.  Data Dependent Systems Approach to Modal Analysis Via State Space , 1985 .