Smart Structures and Systems

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ANN based on forgetting factor for online model updating in substructure pseudo-dynamic hybrid simulation

  • Wang, Yan Hua (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Lv, Jing (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Wu, Jing (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University) ;
  • Wang, Cheng (Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University)
  • Received : 2019.10.11
  • Accepted : 2020.05.04
  • Published : 2020.07.25
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Abstract

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

Keywords

Acknowledgement

The research described in this paper was financially supported by the Natural Science Foundation of China under grants No.51708110.

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