Structural damage prognosis of three-dimensional large structural systems

Abstract

A novel procedure for the health assessment of large three-dimensional (3D) structures with several significant attractive features and improved implementation potential is proposed. Structures are represented by 3D finite elements and a substructure concept is used so that acceleration time histories can be measured only at small part(s) of the structure. Just by measuring relatively few noise-contaminated responses in the substructure, the health of the whole structure can be assessed by the system identification (SI) concept by tracking the stiffness parameter of all the elements using a significantly improved unscented Kalman filter (UKF) algorithm. Since measuring excitation time histories can be very problematic and expensive, the UKF algorithm is integrated with 3D iterative least-squares with unknown input algorithm. UKF fails to identify large structures due to convergence-related issues. The authors used short duration responses and multiple global iterations with weight factor and objective function instead of one long duration response generally used in UKF. For the preselected excitation, short duration eliminates multiple sources of excitation beyond the control of inspector. The weight factor helps accurately locate the defect spot. With informative examples, it is documented that the proposed method is superior to various other forms of Kalman filter-based algorithms.