Stochastic subspace identification for output-only modal analysis: Application to super high-rise tower under abnormal loading condition

The objective of this paper is to develop an online system parameter estimation technique from the response measurements through using the recursive covariance-driven stochastic subspace identification (SSI-COV) approach. In developing the recursive SSI-COV, to avoid time-consumption of singular value decomposition in recursive SSI, the extended instrumental variable version of the projection approximation subspace tracking method is used in SSI-COV. Besides, to reduce the effect of noise on the results of identification, the preprocessing of data using recursive singular spectrum analysis technique is also presented to remove the noise contaminant measurements to enhance the stability of data analysis. On the basis of the proposed method, both the ambient vibration and seismic response data of a tower (Canton Tower) are used to observe the time-varying system natural frequencies of a tower from its operating condition. Results from using off-line SSI-COV method under normal operating condition are also presented. Comparison on the identified time-varying dynamic characteristics of the tower under normal operating condition and earthquake response of distanced earthquake event is discussed.