Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures
Publication: Journal of Engineering Mechanics
Volume 134, Issue 10
Abstract
This paper presents a Bayesian hypothesis testing-based probabilistic assessment method for nonparametric damage detection of building structures, considering the uncertainties in both experimental results and model prediction. A dynamic fuzzy wavelet neural network method is employed as a nonparametric system identification model to predict the structural responses for damage evaluation. A Bayes factor evaluation metric is derived based on Bayes’ theorem and Gaussian distribution assumption of the difference between the experimental data and model prediction. The metric provides quantitative measure for assessing the accuracy of system identification and the state of global health of structures. The probability density function of the Bayes factor is constructed using the statistics of the difference of response quantities and Monte Carlo simulation technique to address the uncertainties in both experimental data and model prediction. The methodology is investigated with five damage scenarios of a four-story benchmark building. Numerical results demonstrate that the proposed methodology provides an effective approach for quantifying the damage confidence in the structural condition assessment.
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Acknowledgments
This study was partly supported by funds from Sandia National Laboratories, Albuquerque, N.M. (Contract No. BG-7732; Monitors: Dr. Thomas L. Paez and Dr. Martin Pilch), and partly by the U.S. Air Force Research Laboratory at Wright Patterson Air Force Base, Ohio (through subcontract to Anteon Corporation; Monitor: Mark Derriso). The support is gratefully acknowledged. The data used to train and validate the new methodology were provided by the IASC–ASCE task group on structural health monitoring, which is also gratefully acknowledged.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 134 • Issue 10 • October 2008
Pages: 820 - 831
Copyright
© 2008 ASCE.
History
Received: Jun 28, 2006
Accepted: Jan 3, 2008
Published online: Oct 1, 2008
Published in print: Oct 2008
Notes
Note. Associate Editor: Arvid Naess
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