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30 March 2009 Comparison of various structural damage tracking techniques with unknown excitations based on experimental data
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Abstract
An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the LSE with unknown inputs (LSE-UI) and the EKF with unknown inputs (EKF-UI). Also, new analysis methods, referred to as the sequential non-linear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO) and the quadratic sum-square error with unknown inputs (QSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of experimental tests using a small-scale 3-story building model with white noise excitation. The capability of the LSE-UI, EKF-UI, SNLSE-UI-UO and QSSE-UI approaches in tracking the structural damages will be demonstrated.
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Hongwei Huang, Jann N. Yang, and Li Zhou "Comparison of various structural damage tracking techniques with unknown excitations based on experimental data", Proc. SPIE 7292, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009, 729230 (30 March 2009); https://doi.org/10.1117/12.815333
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