Abstract
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.
Recommended Citation
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
Department(s)
Civil, Architectural and Environmental Engineering
Second Department
Electrical and Computer Engineering
Research Center/Lab(s)
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)
2076-3417
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2021 The Authors, All rights reserved.
Publication Date
21 Oct 2021
Comments
Financial support to complete this study was provided in part by the U.S. Department of Transportation, Office of Assistant Secretary for Research and Technology under the auspices of Mid-America Transportation Center at the University of Nebraska, Lincoln (grant no. 00072738). Partial support for this research was received from the Missouri University of Science and Technology Intelligent Systems Center, the Mary K. Finley Missouri Endowment, the National Science Foundation, the Lifelong Learning Machines program from DARPA/Microsystems Technology Office, the Army Research Laboratory (ARL), and the Leonard Wood Institute; and it was accomplished under Cooperative Agreement Numbers W911NF-18-2-0260 and W911NF-14-2-0034.