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.
Hao Sun and Oral Büyüköztürk, "Bayesian model updating using incomplete modal data without mode matching," Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 98050D (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 21, 2016; Published: 1 April 2016); https://doi.org/10.1117/12.2219300.
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