# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 6 Conclusions  
Existing CNN-based approaches encode 1D ground motion records into 2D images for structural damage classification via deep 2D CNN models. This process leads to unnecessarily high computing time and expensive computing resources. Instead, this study directly inputted 1D ground motion records into the 1D CNN model for structural damage classification. The developed 1D CNN model avoided redundant 2D image encoding and expensive computing time. A benchmark r/c frame building was studied using three neural network models: a 1D CNN model, a 2D CNN model, and a shallow FNN model. They were trained, validated, and tested with the same dataset from the NLTHA of the benchmark building using 3201 ground excitations in the same computing environment. The studied results showed that the 2D CNN model yielded the highest accuracy (81.6%) of prediction, the 1D CNN model provided a similar level of prediction accuracy (81.0%), and the FNN model had the lowest accuracy (70.8%) of prediction. In comparison with the 2D CNN, the 1D CNN model reduced trainable parameters by 86%, memory usage by 69%, GMR preprocessing time by 99.9%, training time by 96%, and testing time by 90%. The proposed 1D CNN model is thus recommended for CNN-based post-earthquake damage assessments due to its high prediction accuracy and computational efficiency. Future studies should focus on the influence of GMR duration and time step on the 1D CNN performance. The optimal size of a GMR dataset also needs to be determined to avoid time-consuming NLTHA. Moreover, the 1D model should be further validated with varying structure types, such as bridges, in a future study.  
[PERSON] contributed to the methodology of the study. [PERSON] and [PERSON] contributed to the CNN model training. [PERSON] and [PERSON] performed the numerical simulation of the building. [PERSON] wrote the first draft of the manuscript. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the manuscript revision and review. All authors approved the submitted version. All authors have read and agreed to the published version of the manuscript.  
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 W911 NF-18-2-0260 and W911 NF-14-2-0034. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Leonard Wood Institute, the National Science Foundation, the Army Research Laboratory, or the U.S. Government.  
Not applicable.  
The raw data supporting the conclusions of this article may be made available upon request from the correspondence author.  
The help from [PERSON] and [PERSON] with the numerical simulation of the benchmark building is highly acknowledged.  
The authors declare no conflict of interest.