Abstract:Using the stochastic state-space model and expectation maximization (EM) algorithm, operational modal analysis for civil structures is performed in this paper. The EM algorithm is a process to obtain the maximum likelihood estimate of the model by updating the model parameters iteratively. The modal parameters could be obtained from the identified parameters of the state-space model. In this study, the square-root version of the Kalman filtering method is applied to improve the computational robustness of the EM algorithm. Considering the correlation between input and measurement noises in the state-space model, an extended parameterization for the state-space model is established. The performances of the stochastic subspace identification (SSI) method, the EM algorithm without considering the noise correlation, and the proposed EM algorithm considering the noise correlation, are comparatively studied, and the results show that the EM algorithm considering the noise correlation is more accurate than that without considering the correlation. In addition, the proposed EM algorithm performs better than the SSI method in the case of short-length data.