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Hence, this work paves the way toward a nonlinear model updating methodology with broad applicability.
A model updating methodology is proposed for calibration of nonlinear finite element (FE) models simulating the behavior of real-world complex civil structures subjected to seismic excitations.
This paper reports the rigorous derivations of the Bayesian probabilistic structural model updating methodology and its application to full scale civil engineering structure.
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This model, in conjunction with experimental measurements of PZT transducers, is used to present an updating methodology to quantitatively detect interfacial debonding of these kinds of structures.
Using the benchmark thermal challenge problem as an example, we study several possible model updating formulations using the proposed methodology.
A Bayesian probabilistic methodology for model updating is first implemented for the purpose of updating the structural model using dynamic data.
The experimental results are given primacy, and then the model must be modified by trial and error methodologies of model updating approach.
A detector is constructed based on the above methodology, and a model updating strategy is also provided.
Day-ahead and real-time weather forecasting, demand response and model updating are also integrated into the proposed methodology using a receding horizon optimization strategy.
In this paper, a fast Bayesian methodology is presented for structural model updating utilizing modal information from multiple setups.
A two-step methodology is proposed for finite element model updating.
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