Continuous Modal Parameter Identification of a Cable-Stayed Bridge Based on Robustious Decomposition and Covariance-Driven Stochastic Subspace Identification

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The objective of this paper was to develop an effective and efficient continuous health monitoring system. For this purpose, a novel improved ensemble empirical mode decomposition (EEMD) method has been introduced to decompose the nonlinear and non-stationary dynamic data. The improved EEMD method can commendably remove the noise from the original signals, and also it can alleviate the phenomenon of mode mixing. Then the reconstructed signal obtained after applying the improved EEMD technique was used to identify the continuous modal parameters such as frequency and damping ratio using the covariance-driven stochastic subspace identification method. The above techniques were then employed to the data obtained from a real-life cable-stayed bridge to identify its continuous modal parameters. The results validate that the proposed method is very effective in dealing with dynamic nonlinear and non-stationary data and can be used very effectually in real-life continuous health monitoring of cable-stayed bridges.