Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. Among these techniques tracking modal parameters and estimating the structural current state from its seismic response measurement can provide useful information for structural safety assessment, therefore, on-line or recursive identification technique needs to be developed for structural seismic response monitoring. In this paper, the recursive subspace identification algorithms based on matrix inversion lemma algorithm (RSI-Inversion) with oblique projection technique was developed. Forgetting factor with enlarge window is introduced in the RSI-Inversion to emphasize the latest state of the time-varying system in this method. In addition to identifying the instantaneous dynamic characteristics of the structural system using RSI, a two-stage damage detection algorithm incorporated with the identified results from RSI will also be applied to localize and quantify the structural damage. Seismic responses of a base-isolated bridge are used to verify the proposed identification and the damage assessment algorithms, i.e. specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction.
Modal parameters identification research based on using vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. Since time–frequency analysis for non-linear and non-stationary signals is extraordinarily challenging for structural response under extreme loading. To capture features in these signals, it is necessary for the analysis methods to be local, adaptive and stable. Classical approach to compute its instantaneous frequency is to consider the amplitude–frequency modulated formulation of its complex (or analytic) signal extension, via the Hilbert transform. The aim of this paper is therefore to present and discuss some non-parametric methods of time frequency analysis improved with respect to the classical implementation, and compare their performance under different conditions with respect to the signals to be examined. In particular, the following methods will be presented:
Modified Complex Morlet Wavelet with Variable Central Frequency (MCMV+VCF)
Enhanced Time-Frequency Analysis Through SVD-based WPT
Synchro-Squeeze wavelet transform method (SSWT)
Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD)
To demonstrate the applicability of the proposed method responses from synthetic signals and instrumented responses are used to demonstrate the capability of feature extraction of the methods, analysis on the synthetic data as well as the response data.
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