A study on Gabor frame for estimating instantaneous dynamic characteristics of structures

A structure under damage normally exhibits nonlinear dynamic behaviors and time-dependent stiffness and damping. By estimating instantaneous dynamic characteristics of a structures is generally very useful when assessing structural damage in real application. This study presents a novel and effective approach that accurately estimating instantaneous dynamic characteristic of a structure using time-varying autoregressive exogenous (TVARX) model on Gabor frame. To evaluate the time-varying coefficient matrices of a time-series model, the proposed procedure applies Gaussian-based weighted function and Fourier transform on time series model, then the Gabor-transform-based time series formula could be obtained. The dynamic characteristics of a structure are determined from the coefficient matrices. Numerical analyses demonstrate that the proposed approach is superior to traditional time-frequency analysis in accurately estimating instantaneous dynamic characteristics of a structure. Finally, the proposed approach is applied to process measured data for a steel frame specimen subjected to a series of base excitations in shaking table tests.

[1]  T. Ozaki,et al.  Modelling nonlinear random vibrations using an amplitude-dependent autoregressive time series model , 1981 .

[2]  Shu Hung Leung,et al.  Gradient-based variable forgetting factor RLS algorithm in time-varying environments , 2005, IEEE Transactions on Signal Processing.

[3]  C. H. Chen,et al.  Identifying the Modal Parameters of a Structure from Ambient Vibration Data via the Stationary Wavelet Packet , 2014, Comput. Aided Civ. Infrastructure Eng..

[4]  C. M. Wen,et al.  A neural network approach for structural identification and diagnosis of a building from seismic response data , 2003 .

[5]  Maciej Niedzwiecki,et al.  Identification of Time-Varying Processes , 2000 .

[6]  K. Chon,et al.  A Robust Time-Varying Identification Algorithm Using Basis Functions , 2003, Annals of Biomedical Engineering.

[7]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[8]  T. Fang,et al.  A Time-Domain Method for Identifying Modal Parameters , 1986 .

[9]  Joseph Lardies,et al.  Modal parameter identification based on ARMAV and state–space approaches , 2010 .

[10]  C. S. Huang,et al.  Identification of Time‐Variant Modal Parameters Using Time‐Varying Autoregressive with Exogenous Input and Low‐Order Polynomial Function , 2009, Comput. Aided Civ. Infrastructure Eng..

[11]  C. Loh,et al.  IDENTIFICATION OF FEI-TSUI ARCH DAM FROM BOTH AMBIENT AND SEISMIC RESPONSE DATA , 1996 .

[12]  Takatoshi Okabayashi,et al.  System identification of highway bridges from ambient vibration using subspace stochastic realization theories , 2011 .

[13]  S. R. Ibrahim,et al.  The Experimental Determination of Vibration Parameters from Time Responses , 1976 .

[14]  Chi-Hung Huang,et al.  Modal identification of structures from ambient vibration, free vibration, and seismic response data via a subspace approach , 2001 .

[15]  Vasilis Z. Marmarelis,et al.  Advanced Methods of Physiological System Modeling , 1989 .

[16]  C. Loh,et al.  Time Domain Identification of Frames under Earthquake Loadings , 2000 .

[17]  Eric L. Miller,et al.  A sliding window RLS-like adaptive algorithm for filtering alpha-stable noise , 2000, IEEE Signal Processing Letters.

[18]  Chi-Hung Huang,et al.  STRUCTURAL IDENTIFICATION FROM AMBIENT VIBRATION MEASUREMENT USING THE MULTIVARIATE AR MODEL , 2001 .

[19]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise Buildings , 2005 .

[20]  C. S. Huang,et al.  Identification of Instantaneous Modal Parameter of Time‐Varying Systems via a Wavelet‐Based Approach and Its Application , 2014, Comput. Aided Civ. Infrastructure Eng..