An automated operational modal analysis algorithm and its application to concrete dams

Abstract

With the increasing application of long-term dynamic monitoring systems to civil engineering structures, automated operational modal analysis (OMA) has become a fascinating research area for structural health monitoring. In recent years, different automated approaches based on the existing parametric or nonparametric OMA algorithms have been developed. However, they usually perform less well in large complex structures with weak excitation because of inherent limitations of those algorithms. In this work, a three-stage automated OMA algorithm based on a combination of the second-order blind identification (SOBI) and the covariance-driven stochastic subspace identification (SSI-COV) is proposed, which takes full advantage of both parametric and nonparametric algorithms while overcoming the limitations. The automated approach avoids the ambiguous work related to interpreting stabilization diagrams without losing accuracy and thus obtains more reliable modal parameters. The proposed method is fully validated by simulated data from an 8-DOF system and data recorded on a concrete arch-gravity dam in view of single and continuous monitoring. The results show that all physical modes can be well-separated and reliably identified by the proposed method with high accuracy, confirming the excellent performance of the method, especially for continuous monitoring.