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Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation

Muto, Matthew M. and Beck, James L. (2008) Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation. Journal of Vibration and Control, 14 (1-2). pp. 7-34. ISSN 1741-2986. doi:10.1177/1077546307079400.

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System identification of structures using their measured earthquake response can play a key role in structural health monitoring, structural control and improving performance-based design. Implementation using data from strong seismic shaking is complicated by the nonlinear hysteretic response of structures. Furthermore, this inverse problem is ill-conditioned for example, even if some components in the structure show substantial yielding, others will exhibit nearly elastic response, producing no information about their yielding behavior. Classical least-squares or maximum likelihood estimation will not work with a realistic class of hysteretic models because it will be unidentifiable based on the data. It is shown here that Bayesian updating and model class selection provide a powerful and rigorous approach to tackle this problem when implemented using a recently developed stochastic simulation algorithm called Transitional Markov Chain Monte Carlo. The updating and model class selection is performed on a previously-developed class of Masing hysteretic structural models that are relatively simple yet can give realistic responses to seismic loading. The theory for the Masing hysteretic models, and the theory used to perform the updating and model class selection, are presented and discussed. An illustrative example is given that uses simulated dynamic response data and shows the ability of the algorithm to identify hysteretic systems even when the class of models is unidentifiable based on the data.

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Additional Information:©2008 SAGE Publications Received 21 March 2006 accepted 9 August 2006. The authors gratefully acknowledge the George W. Housner Fellowship and Harold Hellwig Fellowship received by the first author from the California Institute of Technology. The authors also wish to thank J. Ching, National Taiwan University of Science and Technology, for sending them the TMCMC algorithm prior to its publication.
Funding AgencyGrant Number
George W. Housner FellowshipUNSPECIFIED
Harold Hellwig FellowshipUNSPECIFIED
Subject Keywords:Bayesian methods, Masing hysteretic models, system identification, Markov Chain Monte Carlo simulation, model class selection
Issue or Number:1-2
Record Number:CaltechAUTHORS:20120810-120310571
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:33097
Deposited By: Sydney Garstang
Deposited On:10 Aug 2012 22:43
Last Modified:09 Nov 2021 21:32

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