The application of auto–regressive time series modelling for the time–frequency analysis of civil engineering structures

Abstract The traditional approach for producing spectral estimates from experimental data is the Fast Fourier Transform. However, in certain circumstances this approach is inappropriate due to factors such as non-stationarity or non-linearity. In this paper, the authors apply the auto–regressive time series modelling approach to produce spectral estimates of two such problems — non-stationary data obtained from the large amplitude response of a cable stayed bridge to wind excitation and non-linear data obtained from modal testing of cracked reinforced concrete beams. Although the auto–regressive approach is very sensitive to the model parameters chosen and so should always be used with caution, the results of these studies have produced time varying spectral data that give insight into the problems addressed.

[1]  T. Ulrych,et al.  Time series modeling and maximum entropy , 1976 .

[2]  Ahsan Kareem,et al.  Applications of wavelet transforms in earthquake, wind and ocean engineering , 1999 .

[3]  A. A. Cullen Wallace Wind influence on Kessock bridge , 1985 .

[4]  J. D. Littler,et al.  The use of the maximum entropy method for the spectral analysis of wind-induced data recorded on buildings , 1997 .

[5]  S. Cheng,et al.  VIBRATIONAL RESPONSE OF A BEAM WITH A BREATHING CRACK , 1999 .

[6]  G. R. Stegen,et al.  Experiments with maximum entropy power spectra of sinusoids , 1974 .

[7]  John S. Owen,et al.  The Prototype Testing of Kessock Bridge: Response to Vortex Shedding , 1996 .

[8]  Hideaki Sakai,et al.  Statistical properties of AR spectral analysis , 1979 .

[9]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[10]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[11]  John Shaw Owen A power spectral approach to the analysis of the dynamic response of cable stayed bridges to spatially varying excitation , 1994 .

[12]  J. P. Burg,et al.  Maximum entropy spectral analysis. , 1967 .

[13]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[14]  George R. Cooper,et al.  An empirical investigation of the properties of the autoregressive spectral estimator , 1976, IEEE Trans. Inf. Theory.

[15]  William H. Press,et al.  Numerical recipes in C , 2002 .

[16]  H. Akaike Fitting autoregressive models for prediction , 1969 .

[17]  John S. Owen,et al.  The Prototype Testing of Kessock Bridge: Long term monitoring of Response to Wind Excitation , 1994 .

[18]  Nobuyuki Sasaki,et al.  FIELD VIBRATION TEST OF A LONG-SPAN CABLE-STAYED BRIDGE BY LARGE EXCITERS , 1992 .

[19]  C. Ventura,et al.  Joint time-frequency analysis of a 20 story instrumented building during two earthquakes , 1999 .

[20]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[21]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[22]  M. Ventura,et al.  A brief history of concentrated hydrogen peroxide uses , 1999 .

[23]  J. A. Brandon,et al.  Complex oscillatory behaviour in a cracked beam under sinusoidal excitation , 1995 .

[24]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[25]  Poul Henning Kirkegaard,et al.  Identification of a Maximum Softening Damage Indicator of RC-Structures Using Time-Frequency Techniques , 1995 .

[26]  John S. Owen,et al.  Role of Dynamic Testing in Assessment of Bridges , 1997 .