Earthquakes and Structures

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A hybrid deep learning model for predicting the residual displacement spectra under near-fault ground motions

  • Mingkang Wei (School of Civil Engineering, Wuhan University) ;
  • Chenghao Song (Engineering Research Center of Urban Disasters Prevention and Fire Rescue Technology of Hubei Province) ;
  • Xiaobin Hu (School of Civil Engineering, Wuhan University)
  • Received : 2022.10.27
  • Accepted : 2023.06.16
  • Published : 2023.07.25
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Abstract

It is of great importance to assess the residual displacement demand in the performance-based seismic design. In this paper, a hybrid deep learning model for predicting the residual displacement spectra under near-fault (NF) ground motions is proposed by combining the long short-term memory network (LSTM) and back-propagation (BP) network. The model is featured by its capacity of predicting the residual displacement spectrum under a given NF ground motion while considering the effects of structural parameters. To construct this model, 315 natural and artificial NF ground motions were employed to compute the residual displacement spectra through elastoplastic time history analysis considering different structural parameters. Based on the resulted dataset with a total of 9,450 samples, the proposed model was finally trained and tested. The results show that the proposed model has a satisfactory accuracy as well as a high efficiency in predicting residual displacement spectra under given NF ground motions while considering the impacts of structural parameters.

Keywords

Acknowledgement

This work was supported by The National Natural Science Foundation of China under Grant No. 51578429. The financial support is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of the sponsor.

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