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Batch and Recursive Bayesian Estimation Methods for Nonlinear Structural System Identification

Astroza, Rodrigo and Ebrahimian, Hamed and Conte, Joel P. (2017) Batch and Recursive Bayesian Estimation Methods for Nonlinear Structural System Identification. In: Risk and Reliability Analysis: Theory and Applications. Springer Series in Reliability Engineering. Springer , Cham, Switzerland, pp. 341-364. ISBN 978-3-319-52425-2. http://resolver.caltech.edu/CaltechAUTHORS:20180111-134220191

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Abstract

This chapter presents a framework for the identification of nonlinear finite element (FE) structural models using Bayesian inference methods. Using the input-output dynamic data recorded during an earthquake event, batch and recursive Bayesian estimation methods are employed to update a mechanics-based nonlinear FE model of the structure of interest (building, bridge, dam, etc.). Unknown parameters of the nonlinear FE model characterizing material constitutive models, inertia, geometric, and/or constraint properties of the structure can be estimated using limited response data recorded through accelerometers or heterogeneous sensor arrays. The updated nonlinear FE model can be used to identify the damage in the structure following a damage-inducing event. This framework, therefore, can provide an advanced tool for post-disaster damage identification and structural health monitoring. The batch estimation method is based on a maximum a posteriori estimation (MAP) approach, where the time history of the input and output measurements are used as a single batch of data for estimating the FE model parameters. This method results in a nonlinear optimization problem that can be solved using gradient-based and non-gradient-based optimization algorithms. In contrast, the recursive Bayesian estimation method processes the information from the measured data recursively, and updates the estimation of the FE model parameters progressively over the time history of the event. The recursive Bayesian estimation method results in a nonlinear Kalman filtering approach. The Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) are employed as recursive Bayesian estimation methods herein. For those estimation methods that require the computation of structural FE response sensitivities (total partial derivatives) with respect to the unknown FE model parameters, the direct differentiation method (DDM) is used. Response data numerically simulated from a nonlinear FE model (with unknown material model parameters) of a five-story two-by-one bay reinforced concrete frame building subjected to bi-directional horizontal seismic excitation are used to illustrate the performance of the proposed framework.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-319-52425-2_15DOIArticle
https://link.springer.com/chapter/10.1007/978-3-319-52425-2_15PublisherArticle
ORCID:
AuthorORCID
Astroza, Rodrigo0000-0003-0711-1259
Ebrahimian, Hamed0000-0003-1992-6033
Conte, Joel P.0000-0003-2068-7965
Additional Information:© Springer International Publishing AG 2017. First Online: 25 February 2017. This book was prepared in honor of Professor Armen Der Kiureghian, one of the fathers of modern risk and reliability analysis. Partial support of this research by the UCSD Academic Senate under Research Grant RN091G − CONTE is gratefully acknowledged. The first author acknowledges the support provided by the Fulbright-CONICYT Chile Equal Opportunities Scholarship. Any opinions, findings, and conclusions or recommendations expressed in this study are those of the authors and do not necessarily reflect those of the sponsors.
Funders:
Funding AgencyGrant Number
University of California San DiegoRN091G − CONTE
Fulbright-CONICYT Chile Equal Opportunities ScholarshipUNSPECIFIED
Record Number:CaltechAUTHORS:20180111-134220191
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180111-134220191
Official Citation:Astroza R., Ebrahimian H., Conte J.P. (2017) Batch and Recursive Bayesian Estimation Methods for Nonlinear Structural System Identification. In: Gardoni P. (eds) Risk and Reliability Analysis: Theory and Applications. Springer Series in Reliability Engineering. Springer, Cham
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84268
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:11 Jan 2018 23:31
Last Modified:04 Apr 2019 20:26

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