Structural performance assessment with minimum uncertainty-filled information

A novel structural performance/health assessment method, denoted as GILS-EKF-UI, is under development by the research team at the University of Arizona. The procedure is essentially inverse solution of a system identification-based algorithm in the presence of uncertainty. The unique feature of the algorithm is that it can identify members' properties and in the process access the performance/health of a structural system using only noise-contaminated dynamic response information measured at few locations completely ignoring the excitation information. The experimental and analytical verification of the method is emphasized in this paper. To verify the GILS-EKF-UI method using minimum response information, a substructure is considered. A two-dimensional defect-free steel frame is identified using limited analytical (noise-free and noise-contaminated) and experimental responses. Several defects were then introduced in the frame. Two defects in particular; by removing a member and by reducing the cross sectional area of a member over a finite length, are presented in the paper. In all cases, the GILS-EKF-UI method predicted the health of the frame by correctly identifying the defect and its location, conclusively establishing the viability of the novel concept.