Novel Unscented Kalman Filter for Health Assessment of Structural Systems with Unknown Input
Publication: Journal of Engineering Mechanics
Volume 141, Issue 7
Abstract
A novel procedure for structural health assessment, denoted as unscented Kalman filter with unknown input (UKF-UI), is proposed using the nonlinear system identification concept. To increase its implementation potential, a substructure concept is introduced, producing a two-stage approach. It integrates the unscented Kalman filter concept and an iterative least-squares technique. The two most important features of the method are that it does not need the information on the time history of the excitation to identify structural systems represented by finite elements, and that it can identify defects in them using only a limited amount of noise-contaminated nonlinear response information. The proposed method is robust enough to detect the locations and severity of defects at different locations in the structure. The defect detection capability increases significantly if the defective member is in the substructure or close to it. The method is conclusively verified with the help of two examples using impulsive and seismic excitations. The superiority of UKF-UI over extended Kalman filter-based procedures is documented. The proposed UKF-UI procedure has high implementation potential and can be used for health assessment of large structural systems.
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Acknowledgments
This study is based on work partly supported by the Iraq’s Ministry of Higher Education and Scientific Research. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the writers and do not necessarily reflect the views of the sponsor.
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Published In
Journal of Engineering Mechanics
Volume 141 • Issue 7 • July 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Nov 11, 2013
Accepted: Jan 5, 2015
Published online: Apr 22, 2015
Published in print: Jul 1, 2015
Discussion open until: Sep 22, 2015
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