Smart Structures and Systems

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Nonlinear structural model updating based on the Deep Belief Network

  • Mo, Ye (Department of Civil Engineering, Hefei University of Technology) ;
  • Wang, Zuo-Cai (Department of Civil Engineering, Hefei University of Technology) ;
  • Chen, Genda (Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology) ;
  • Ding, Ya-Jie (Department of Civil Engineering, Hefei University of Technology) ;
  • Ge, Bi (Department of Civil Engineering, Hefei University of Technology)
  • Received : 2020.08.21
  • Accepted : 2022.03.15
  • Published : 2022.05.25
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Abstract

In this paper, a nonlinear structural model updating methodology based on the Deep Belief Network (DBN) is proposed. Firstly, the instantaneous parameters of the vibration responses are obtained by the discrete analytical mode decomposition (DAMD) method and the Hilbert transform (HT). The instantaneous parameters are regarded as the independent variables, and the nonlinear model parameters are considered as the dependent variables. Then the DBN is utilized for approximating the nonlinear mapping relationship between them. At last, the instantaneous parameters of the measured vibration responses are fed into the well-trained DBN. Owing to the strong learning and generalization abilities of the DBN, the updated nonlinear model parameters can be directly estimated. Two nonlinear shear-type structure models under two types of excitation and various noise levels are adopted as numerical simulations to validate the effectiveness of the proposed approach. The nonlinear properties of the structure model are simulated via the hysteretic parameters of a Bouc-Wen model and a Giuffré-Menegotto-Pinto model, respectively. Besides, the proposed approach is verified by a three-story shear-type frame with a piezoelectric friction damper (PFD). Simulated and experimental results suggest that the nonlinear model updating approach has high computational efficiency and precision.

Keywords

Acknowledgement

This study was partly supported by the National Natural Science Foundation of China under grand No. 51922036, by the key research and development project of Anhui province under grand No. 1804a0802204, by The Fundamental Research Funds for the Central Universities under grand No. JZ2020HGPB0117, and by the Natural Science Funds for Distinguished Young Scholar of Anhui province under grand No.1708085J06. The results and opinions presented in this paper are those of the authors only and they do not necessarily represent those of the sponsors.

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