Bayesian Model–Data Fusion for Mechanistic Postearthquake Damage Assessment of Building Structures
Publication: Journal of Engineering Mechanics
Volume 142, Issue 9
Abstract
This paper presents a probabilistic framework for estimating seismic-induced damage in partially instrumented buildings. The proposed framework uses acceleration measurements at a limited number of stories and Bayesian filtering to estimate the response at all stories. The paper compares four Bayesian filters: the extended, unscented, and ensemble Kalman filters, and the particle filter. The estimated response throughout the building serves as input to a damage model that yields an estimate of structural damage and its uncertainty at all stories. The methodology is numerically verified in an elastoplastic 5-story shear building and in a 10-story inelastic moment-resisting frame under various types of model errors and minimal instrumentation. It was found that under ideal and mild modeling error conditions, the proposed methodology provides consistent estimates of damage and its uncertainty.
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Acknowledgments
This work was partially supported by the National Science Foundation grant number EEC-1342190. The support is gratefully acknowledged.
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Published In
Journal of Engineering Mechanics
Volume 142 • Issue 9 • September 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Sep 2, 2014
Accepted: Mar 14, 2016
Published online: May 13, 2016
Published in print: Sep 1, 2016
Discussion open until: Oct 13, 2016
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