• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.147-154
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• 2019

The finite element model updating can be defined as the problem of finding the parameters of the finite element model which gives the closest response to the actual response of the structure by measurement. In the previous researches, optimization based methods have been developed to minimize the error of the response of the actual structure and the analytical model. In this study, we propose an inverse eigenvalue problem that can directly obtain the parameters of the finite element model from the target mode information. Deep Neural Networks are constructed to solve the inverse eigenvalue problem quickly and accurately. As an application example of the developed method, the dynamic finite element model update of a suspension bridge is presented in which the deep neural network simulating the inverse eigenvalue function is utilized. The analysis results show that the proposed method can find the finite element model parameters corresponding to the target modes with very high accuracy.

• Computational Structural Engineering
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• v.30 no.4
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• pp.19-24
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• 2017

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.371-372
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• 2010

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.121-128
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• 2003

In recent years, various artificial neural network algorithms are used in the damage assessment of civil infrastructures. So far, many researchers have used the artificial neural network as a pattern classifier for the structural damage assessment but, in this paper, the neural network is used as a structural reanalysis tool not as a pattern classifier. For the model updating using the optimization algorithm, the summation of the absolute differences in the structural vibration modes between undamaged structures and damaged ones is considered as an objective function. The stiffness of structural components are treated as unknown parameters to be determined. The structural damage detection is achieved using model updating based on the optimization techniques which determine the estimated stiffness of components minimizing the objective function. For the verification of the proposed damage identification algorithm, it is numerically applied to a simply supported bridge model.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.19-28
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• 2006

Most conventional model updating methods must use mathematical objective function with experimental modal matrices and analytical system matrices or must use information about the gradient or higher derivatives of modal properties with respect to each updating parameter. Therefore, most conventional methods are not appropriate for complex structural system such as bridge structures due to stability problem in inverse analysis with ill-conditions. Sometimes, moreover, the updated model may have no physical meaning. In this paper, a new FE model updating method based on a hybrid optimization technique using genetic algorithm (GA) and Holder-Mead simplex method (NMS) is proposed. The performance of hybrid optimization technique on the nonlinear problem is demonstrated by the Goldstein-Price function with three local minima and one global minimum. The influence of the objective function is evaluated by the case study of a simulated 10-dof spring-mass model. Through simulated case studies, finally, the objective function is proposed to update mass as well as stiffness at the same time. And so, the proposed hybrid optimization technique is proved to be an efficient method for FE model updating.