Substructural first- and second-order model identification for structural damage assessment

This paper presents two methods to perform system identification at the substructural level, taking advantage of reduction in the number of unknowns and degrees of freedom (DOFs) involved, for damage assessment of fairly large structures. The first method is based on first-order state space formulation of the substructure where the eigensystem realization algorithm (ERA) and the observer/Kalman filter identification (OKID) are used. Identification at the global level is then performed to obtain the second-order model parameters. In the second method, identification is performed at the substructural level in both the first- and second-order model identification. Both methods are illustrated using numerical simulation studies where results indicate their significantly better performance than identification using the global structure, in terms of efficiency and accuracy. A 12-DOF system and a fairly large structural system with 50 DOFs are used where the effects of noisy data are considered. In addition to numerical simulation studies, laboratory experiments involving an eight-storey frame model are carried out to illustrate the performance of the proposed method. The identification results presented in terms of the stiffness integrity index show that the proposed methodology is able to locate and quantify damage fairly accurately.