Automatic identification of structural modal parameters based on density peaks clustering algorithm

Estimating modal parameters requires significant user interaction, especially when parametric system identification methods are used and the physical modes are selected in the stabilization diagram. In this paper, a fast density peaks clustering algorithm combined with the covariance-driven stochastic subspace identification method is used to automatically identify modal parameters. Before the automatic identification process, the spurious modes from the stochastic subspace identification method were eliminated by a two-stage method, including using the soft and hard verification criteria to remove spurious modes in the first stage and the removal of spurious modes based on the stability of physical modes in the second stage; thus, a better stabilization diagram was obtained for the subsequent automatic identification. Furthermore, fast density peaks clustering algorithm was applied to select the appropriate structure modes from the stabilization diagram. In the entire identification process, no user participation was required. The proposed method was demonstrated on a 4-degree of freedom (DOF) numerical model and a benchmark frame structure, and the results indicated that the modal parameters can be identified accurately even with the noise effects using the default user-defined parameters. This method showed higher efficiency and universality than the existing methods. Finally, the applicability and robustness of the proposed method in automated operational mode tracking were verified on a real cable-stayed bridge.