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Structural health monitoring (SHM) is considered as an incentive multi-disciplinary technology for conditional assessment of infrastructure system. However, most classical output-only system identification methods based on a stationary assumption fail to achieve reliable results under non-stationary scenarios. Numerous techniques from the disciplines of multivariate statistics and pattern recognition in the field of structural damage detection have been developed, such as stochastic subspace identification or null-space and subspace damage identification. In order to obtain the accurate and quick structural damage assessment of a structure, measurement from all sensing nodes in the structure must be used. Therefore, dimension reduction and data visualization techniques for damage assessment need to be investigated. In this study a structural health monitoring method for damage identification and localization, which incorporated with the principal component analysis (PCA) based data compression and pattern recognition is developed. First, the frequency response function (FRF)-based damage assessment will be investigated. Based on the FRF to construct the Sammon map from all the measurements will be investigated. Sammon’s mapping is a nonlinear dimensional reduction algorithm which seeks to preserve distances as far as possible. Similarity among different data structure can be used to detect damage localization. Besides, the correlation of time-frequency analysis of data (Hilbert amplitude spectrum from WPT) among different measurement and the differences on the reconstructed data using multivariate AR-model are also used to detection damage. Verification of the proposed methods by using shaking table tests of two structures is demonstrated: One is focus on the damage of lower vibration modes and the other one is focus on the damage of high frequency modes.
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