SSA-based stochastic subspace identification of structures from output-only vibration measurements

In this study the output-only system identification of civil structures via Stochastic Subspace Identification (SSI) is carried out. A newly developed signal processing technique, called Singular Spectrum Analysis (SSA), is capable to smooth a noisy signals is adopted for preprocessing the data. With the aim of finding accurate and true modal parameters, the SSA-based SSI-COV algorithm is implemented through the stabilization diagram, in which the identified system poles are plotted by increasing the size of data matrix. Comparative study on determining the model order and selecting the true system poles, with and without using SSA to pre-process the data, is examined through numerical simulation. Finally, identification task of the real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures, using SSA-based SSI algorithm is carried out to extract the dynamic characteristics of the tower from output-only measurements.