Identification of structural element mass and stiffness changes using partial acceleration responses of chain-like systems under ambient excitations

It is important to conduct structural damage detection using only output time histories of structures under ambient excitations. Recently, an approach has been proposed for structural damage detection under ambient excitations using the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF). However, structural mass is assumed known in this approach. Although some methodologies have been presented for detecting damage due to relative structural mass and stiffness changes in chain-like systems, measurements of structural responses including acceleration, velocity, and displacement at each degree of freedom are required. This greatly limits the application of these methodologies. In this paper, without structural mass information, an algorithm is proposed to identify structural element mass and stiffness changes using only partial acceleration responses of chain-like systems under ambient excitations. First, based on the equations for cross-correlation functions of structural responses under ambient stationary excitations modeled as independent stationary white noise processes, EKF is utilized for identifying the structural element stiffness-mass coupled coefficients using only partial acceleration responses. Then, these coefficients are decoupled by cluster analysis and the least squares estimation to obtain structural element stiffness and mass changes. Furthermore, the proposed algorithm is extended for identifying chain-like structural element mass and stiffness changes under non-stationary ambient excitations assumed as time modulating white noises. The performances of the proposed algorithm are numerically investigated using the Phase I The American Society of Civil Engineers structural health monitoring benchmark building under stationary or non-stationary ambient excitations, respectively. Moreover, the experimental data with structural element mass and stiffness changes conducted at the Los Alamos National Laboratory are utilized to test the proposed algorithm. (C) 2020 Published by Elsevier Ltd.