Bayesian model updating using incomplete modal data without mode matching
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This study investigates a new probabilistic strategy for model updating using incomplete modal data. A hierarchical Bayesian inference is employed to model the updating problem. A Markov chain Monte Carlo technique with adaptive random-work steps is used to draw parameter samples for uncertainty quantification. Mode matching between measured and predicted modal quantities is not required through model reduction. We employ an iterated improved reduced system technique for model reduction. The reduced model retains the dynamic features as close as possible to those of the model before reduction. The proposed algorithm is finally validated by an experimental example. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Date issued
2016-03Department
Massachusetts Institute of Technology. Department of Civil and Environmental EngineeringJournal
Proceedings of SPIE--the Society of Photo-Optical Instrumentation Engineers
Publisher
SPIE
Citation
Sun, Hao, and Oral Buyukozturk. “Bayesian Model Updating Using Incomplete Modal Data without Mode Matching.” Proceedings of SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 20 March, 2016, Las Vegas, Nevada, SPIE, 2016. © 2016 SPIE
Version: Final published version
ISSN
0277-786X
1996-756x