New Stochastic Subspace Approach for System Identification and Its Application to Long-Span Bridges

Abstract

This paper investigates the model order determination problem in the identification of dynamic characteristics of long-span bridges subjected to ambient excitation. Based on a stochastic state-space model framework, a new approach for state variable estimation is proposed, which is developed for the purpose of properly determining the order of a mathematical model of the structure under consideration. Comparing the newly developed approach with existing ones, their performances for system identification are evaluated with respect to their ability to highlight structural properties against noise ones in terms of the solution of a singular value problem, from a theoretical point of view and in applications to numerical and field measurements of a suspension bridge. From these applications, it is demonstrated that the newly developed approach is the most effective among the existing ones in discriminating structural modes, including weakly excited and closely spaced modes, from noise ones in terms of singular values, even when dealing with low signal-to-noise ratio signals and nonwhite wind excitation.