Deep learning techniques for predicting nonlinear multi-component seismic responses of structural buildings

•A new approach to nonlinear seismic response predictions of structures using multiple-component features.•Deep learning time-series predictions through hybrid ConvLSTM models.•Signal processing improved with DWT of acceleration time-history.•Hybrid model can predict nonlinear seismic response of in...

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Veröffentlicht in:	Computers & structures Jg. 252; S. 106570
Hauptverfasser:	Torky, Ahmed A., Ohno, Susumu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.08.2021 Elsevier BV
Schlagworte:	Artificial neural networks Butterworth filters Computer architecture Convolutional long short-term memory neural networks Data processing Deep learning Deep neural networks Discrete Wavelet Transform Discrete wavelet transforms Fast Fourier transformations Fourier transforms Machine learning Multi-component response Neural networks Nonlinear seismic response prediction Recurrent neural networks Seismic response Superstructures Wavelet transforms Deep neural networks Multi-component response Discrete wavelet transforms Convolutional long short-term memory neural networks Nonlinear seismic response prediction
ISSN:	0045-7949, 1879-2243
Online-Zugang:	Volltext
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