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

Abstract 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.