Parametric analysis of SSI algorithm in modal identification of high arch dams

The covariance-driven stochastic subspace identification (SSI-COV) is widely used in the operational modal analysis of structures. However, the appropriate selection of user-defined parameters in the SSI-COV algorithm remains a challenging issue, especially for the modal tracking. This study aims to analyze the effect of the four user-defined parameters in SSI-COV for the modal identification of high arch dams. Two finite element (FE) models of the Dagangshan dam are investigated by the SSI-COV to identify the modal parameters. The FE model with the massless foundation is analyzed to investigate the effect of four user-defined parameters on the identification of dynamic properties, and the selection suggestions are proposed for each parameter. The FE model recognizing the semi-unbounded size of foundation rock is further analyzed to investigate the radiation damping effect based on the proposed suggestions of user-defined parameters. The results show that the radiation damping effect of the semi-unbounded foundation rock is approximately 0.6%-2.0% for the first four modes. Moreover, the modal parameters of the Xiluodu dam (285 m) are identified using ambient vibration test, which illustrates that the proposed suggestions for selecting user-defined parameters are effective and reasonable. This study is very beneficial for the modal tracking and structural health monitoring of arch dams in the future.