Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks

Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks

Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D...

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Detalles Bibliográficos
Autores principales: Xinzhe Yuan, Dustin Tanksley, Liujun Li, Haibin Zhang, Genda Chen, Donald Wunsch
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
seismic damage assessment
convolutional neural networks
feedforward neural networks
ground motion records
wavelet transform
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Acceso en línea:https://doaj.org/article/e2ae3fa7137a4d019489ac7204158710
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Internet

https://doaj.org/article/e2ae3fa7137a4d019489ac7204158710

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