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Earthquakes and Structures
  Volume 25, Number 1, July 2023 , pages 15-26
DOI: https://doi.org/10.12989/eas.2023.25.1.015
 


A hybrid deep learning model for predicting the residual displacement spectra under near-fault ground motions
Mingkang Wei, Chenghao Song and Xiaobin Hu

 
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.
 
Key Words
    deep learning; long short-term memory; near-fault; residual displacement spectrum; structural parameters
 
Address
Mingkang Wei and Chenghao Song: School of Civil Engineering, Wuhan University, No. 8 East Lake South Road, Wuhan 430072, Hubei P.R., China
Xiaobin Hu: 1) School of Civil Engineering, Wuhan University, No. 8 East Lake South Road, Wuhan 430072, Hubei P.R., China, 2) Engineering Research Center of Urban Disasters Prevention and Fire Rescue Technology of Hubei Province, No. 8 East Lake South Road, Wuhan 430072, Hubei P.R., China
 

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