The identification of structural damage is an important objective of health monitoring for civil infrastructures. System identification and damage detection based on measured vibration data have received intensive studies recently. Frequently, damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we propose an adaptive tracking technique, based on the extended Kalman filter approach, to identify the structural parameters and their changes when vibration data involve damage events. The proposed technique is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected on‐line. Our adaptive filtering technique is based on the current measured data to determine the parametric variation so that the residual error of the estimated parameters is contributed only by noise. This technique is applicable to linear and nonlinear structures. Simulation results for tracking the parametric changes of nonlinear elastic, hysteretic and linear benchmark structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting structural damage, using measured vibration data. Copyright © 2005 John Wiley & Sons, Ltd.

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An important objective of structural health monitoring systems is to identify the state of the structure and to detect the damage when it occurs. Analysis techniques for damage identification of structures, based on vibration data measured from sensors, have received considerable attention. Recently, a new adaptive tracking technique, based on the extended Kalman filter approach, has been proposed for the damage identification of structures. Simulation studies demonstrated that the adaptive extended Kalman filter (AEKF) approach is capable of tracking the variations of structural parameters, such as the degradation of stiffness, due to damages. In this paper, we present experimental studies to verify the capability of the AEKF approach in identifying the structural damage by conducting a series of experimental tests. A small-scale 3-story building model is used and the white noise excitations are applied to the top floor of the model. To simulate structural damages during the test, an innovative device is proposed to reduce the stiffness of some stories. Different damage scenarios have been simulated and tested. Measured response data and the AEKF approach are used to track the variation of stiffness during the test. The tracking results for stiffness are then compared with the stiffness predicted by the finite-element method. Experimental results demonstrate that the AEKF approach is capable of tracking the variation of structural parameters leading to the detection of structural damages.