Singular Spectrum Analysis (SSA) is a novel technique and has proven to be a powerful tool for time data series analysis. It takes singular value decomposition (SVD) of Hankel matrix embedded by analyzed time data series and decomposes the data into several simple, independent and identifiable components. In this paper, first, the coupling degree of the 1st and 2nd singular values through the composition of the analyzed signal in SSA is used as two important values to detect damage. Besides, based on the extracted sub-space or null-space from SVD of analytic matrix, damage detection algorithm is developed by considering the orthonormality between the sub-space and null-space. The proposed algorithms are verified using non-stationary response data of a model bridge (data from scouring test of a bridge) and field experiment of a bridge during abnormal weather condition. Discussion on the proposed methods with different assessment method to identify the occurrence of damage using SSI-DATA and SSI-COV to identified the system dynamic characteristics are also made.

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