The structural damage identification based on monitoring data plays a fundamental role in the bridge health monitoring system to exert the benefit of perception and early warning.To further improve the accuracy of structural damage identification, a new method was proposed which integrates the Kalman filtering and generalized autoregressive conditional heteroskedasticity (GARCH) model.First, the Kalman filter was used to de-noise the raw acceleration data, and a linear recursive autoregressive model (AR) was established to identify the structural damage.Then, the nonlinear recursive GARCH model was introduced to further improve the identification accuracy.Finally, the time-history data obtained in the tests of corroded RC beams was used to verify the effectiveness of the proposal algorithm.The results show that the residual variance ratio of the time series model can be effectively used to identify the structural damage.Compared to Kalman-AR model, the Kalman-GARCH model can explain the nonlinear characteristics and make up the recognition error caused by neglecting the heteroskedasticity of data.The accuracy can be improved by 14.2%.The results provide a new way for bridge structure state perception based on massive data.
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