# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 1 Introduction
The CNN training time with WT encoding reached more than 20 h on a general-purpose computer without graphics processing unit (GPU) farms, according to [PERSON] et al. [23]. The expensive computational overhead for a CNN-based seismic damage assessment approach with GMR image encoding compromised its advantages of avoiding the computation and the selection of hand-crafted IMs. Therefore, a direct end-to-end approach that can use 1D GMRs as inputs was more desirable, as compared to the contemporary CNN approaches that use GMR images as inputs for seismic damage assessment. The 1D CNNs were widely used in real-time time series classification tasks [35; 36; 37]. As stated by [PERSON] et al. [38], for the task of 1D time series classification, 1D CNNs were superior due to their cost-effectiveness, computational efficiency, and practical deployment, and thus they were more suitable, as compared to deep 2D CNNs. Moreover, 1D CNNs need relatively shallow architecture to manage the challenging 1D signal tasks, but 2D CNNs required an extra image-encoding step and deeper architectures than the 1D CNNs to handle the same tasks. Additionally, 1D CNNs are quickly trained on a general-purpose computer with a central processing unit (CPU) configuration, and deep 2D CNNs usually require special hardware setups like GPU farms or cloud computing to reduce training time. Therefore, 1D CNNs were preferable in real-time 1D single classification tasks. The post-earthquake damage assessment is inherently a classification task of 1D GMRs according to their resultant structural damage states. Hence, this study proposed the use of 1D CNNs, instead of 2D CNNs, for post-earthquake damage assessment. The 1D CNN seismic classifier does not use extra preprocessing such as the wavelet transform encoding of GMRs into 2D GMR images. Instead, 1D CNNs used the 1D GMRs (accelerograms) to predict resultant seismic damage states of structures. To demonstrate the advantages of 1D CNN seismic classifiers, a case study of a benchmark r/c building was conducted. Three neural network models--namely, a 1D CNN model, a 2D CNN model with WT encoding, and an FNN model--were trained under the same computing configuration with the same training and validation datasets. Their prediction performances were compared from the same test dataset. The performance of these three models was evaluated in terms of their computational efficiencies and prediction accuracies. The rest of the paper is organized as follows. First, the methodology of the 1D CNN post-earthquake damage assessment is presented. Second, the detailed architectures of the three neural network models are introduced. Thereafter, the case study conducted on these three models, trained with the same training, validation, and test datasets obtained from the nonlinear time history analyses (NLTHA) results of a benchmark r/c frame building, is presented. Third, the prediction performances and the computational efficiencies of these three neural network models are evaluated based on the case-study results. Finally, conclusions from our research and future research recommendations are presented.