Automated Operational Modal Analysis of a Cable-Stayed Bridge

Abstract

Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented.