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Proceedings Paper

Bayesian model updating using incomplete modal data without mode matching
Author(s): Hao Sun; Oral Büyüköztürk
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Paper Abstract

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.

Paper Details

Date Published: 1 April 2016
PDF: 9 pages
Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 98050D (1 April 2016); doi: 10.1117/12.2219300
Show Author Affiliations
Hao Sun, MIT (United States)
Oral Büyüköztürk, MIT (United States)

Published in SPIE Proceedings Vol. 9805:
Health Monitoring of Structural and Biological Systems 2016
Tribikram Kundu, Editor(s)

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