Abstract：Modal parameters are important dynamic indexes to monitor the service performance of bridge structures in practice. The precise identification of modal parameters is of great significance to bridges. On view of this, the current vibration signal de-noising algorithms and modal parameter identification algorithm are commented and improved to some extent. On the one hand, a novel adaptive signal decomposition and reconstruction algorithm, adaptive ensemble empirical mode decomposition (AEEMD), is proposed. Compared with the ensemble average empirical mode decomposition (EEMD), the proposed method can add amplitude standard deviation of white noise and average numbers of integration automatically according to the specific characteristics of signals. Not only can the endpoint effect effectively handled, but also can the modal aliasing phenomenon existed in the intrinsic mode function be avoided. Finally, the automated extraction of the effective IMF components and signal reconstruction can be fulfilled by the improved EEMD. On the other hand, Multidimensional data clustering analysis method is adopted in stochastic subspace identification to distinguish the false mode and the true mode intelligently by establishing a discriminant matrix model with vibration frequency，modal damping ratio and coefficients, thus the automatic modal parameter identification can be realized. Furthermore, the effectiveness of the proposed method is verified by both the simulated signal and testing signal from real bridge structure. The analysis results show that the proposed method can be used in the automatic model parameter identification of actual bridge structures.
