Structural health assessment at a local level using minimum information

A novel structural health assessment (SHA) technique is presented in this paper. It is a finite element-based time-domain nonlinear system identification technique. It can assess structural health at the element level using only a limited number of noise-contaminated responses and without using information on input excitation. It assesses the location and severity of defect(s) by tracking the changes in the stiffness properties of individual elements from their expected values. The procedure integrates an iterative least squares technique and the unscented Kalman filter (UKF) concept. The integrated procedure significantly improves the basic UKF concept. To demonstrate the effectiveness of the procedure, the health of a relatively large structural system under single and multiple excitations is assessed. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. The optimum number and location of measured responses are investigated. It is demonstrated that the method is capable of identifying defect-free and defective states of the structures using minimum information. Furthermore, it can locate defect spot within a defective element. It is also demonstrated that the proposed method, denoted as UKF-UI-WGI, is superior to the extended Kalman filter-based procedures for SHA developed by the team earlier.