| Title |
Finite Element Model Updating of Structures Using Deep Neural Network |
| Authors |
공밍(Gong, Ming) ; 박원석(Park, Wonsuk) |
| DOI |
https://doi.org/10.12652/Ksce.2019.39.1.0147 |
| Keywords |
유한요소모델 업데이팅;깊은 신경망;역 고유치 해석;현수교 Finite element model updating;Deep neural networks;Inverse eigenvalue problem;Suspension bridge |
| Abstract |
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. |