MODAL IDENTIFICATION OF A PEDESTRIAN BRIDGE UPON COVARIANCE REGRESSION

Modal parameters are important information of a structure as well as significant basis for structural design and health monitoring. Thus, a modal parameter identification method based on covariance regression is proposed for engineering structures subjected to environmental excitations. Based on the stationary assumption of the ambient excitations and the measurement noise, it is found that the covariances at different time lags should be linearly dependent through a set of scalar coefficients that are directly related to modal parameters of the system. Consequently, the scalar coefficients associated with covariances under different time lags can be obtained firstly through linear covariance regression operation, and then the modal parameters are extracted directly from the coefficients. This method avoids the decomposition of a large Hankel matrix as done by some traditional covariance-driven algorithms and requires a smaller model dimension, which results in higher efficiency than some traditional covariance-driven algorithms. A numerical example of a shear model and a field case of a pedestrian bridge were conducted to verify the effectiveness and efficiency of the proposed covariance regression method. The results demonstrate that the modal parameters identified by the proposed method correspond with those identified by the covariance-driven stochastic subspace identification (SSI-Cov) but in a more efficient manner.