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http://dx.doi.org/10.7734/COSEIK.2021.34.1.35

On-line Finite Element Model Updating Using Operational Modal Analysis and Neural Networks  

Park, Wonsuk (Department of Civil Engineering, Mokpo National University)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.34, no.1, 2021 , pp. 35-42 More about this Journal
Abstract
This paper presents an on-line finite element model updating method for in-service structures using measured data. Conventional updating methods, which are based on numerical optimization, are not efficient for on-line updating because they generally require repeated eigenvalue analyses until convergence criteria are met. The proposed method enables fully automated on-line finite element model updating, almost simultaneously with vibration measurement, without any user intervention or off-line procedures. The automated covariance-driven stochastic subspace identification (Cov-SSI) method is utilized to identify modal frequencies and vectors, and the identified modal data is fed to the neural network of the inverse eigenvalue function to produce the updated finite element model parameters. Numerical examples for a wind excited 20-story building structure shows that the proposed method can update the series of finite element model parameters automatically. It is also shown that sudden changes in the structural parameters can be detected and traced successfully.
Keywords
finite element model update; inverse eigenvalue neural network; operational modal analysis; covariance-driven stochastic substructure identification; on-line update;
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