Experimental Study of an Adaptive Extended Kalman Filter for Structural Damage Identification

Abstract

An objective of the structural health monitoring system 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 damage tracking technique, based on the extended Kalman filter approach, has been proposed. 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 using a small-scale three-story building model. Two types of excitations have been used, including the white noise applied to the top floor of the model and the earthquakes applied to the base. To simulate structural damage during the test, an innovative device is proposed in this paper 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 the stiffness are also compared with the stiffness predicted by the finite-element method. Experimental results demonstrate that the AEKF approach is capable of tracking the variation of stiffness parameters leading to the detection of structural damage.