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 CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes.
X. Yuan et al., "Faster Post-Earthquake Damage Assessment based on 1D Convolutional Neural Networks," Applied Sciences, vol. 11, no. 21, article no. 9844, MDPI, Oct 2021.
The definitive version is available at https://doi.org/10.3390/app11219844
Civil, Architectural and Environmental Engineering
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Convolutional Neural Networks; Feedforward Neural Networks; Ground Motion Records; Seismic Damage Assessment; Wavelet Transform
International Standard Serial Number (ISSN)
Article - Journal
© 2021 The Authors, All rights reserved.
21 Oct 2021