# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 5 Discussion  
The model training, validation, and test results in Section 4.2 demonstrate that the 1D CNN model and the 2D CNN model performed similarly; however, the FNN model performed at a lower level. In this section, the computing time and computing resources consumed by the three models were investigated to evaluate their computing efficiencies. The same model training and test strategies outlined in Section 4.2 were used in the CPU computing configurations. The workstation CPU was an Intel Xeon CPU E5-2670 V3 @ 2.30 GHz with 126 G memory and 48 cores. Table 1 compares the three neural network models under the same computing configurations with a GPU and/or a CPU. Though the 2D CNN model with the WT-encoded GMR images achieved the highest prediction accuracy of 81.6%, the WT encoding of the GMRs, the model training, and the testing of the 2D CNN model consumed significantly more time than the 1D CNN model and the FNN model. However, the 1D CNN model had a prediction accuracy of 81.0%, as compared to the 2D CNN model, and it took less computing time and fewer resources. As compared to the 2D CNN model, the 1D CNN model reduced GMR preprocessing time by 99.9%, training time by 96%, and testing time by 90%. The FNN model took the least time among the three models but was also the least accurate at 70.8%. Therefore, the FNN model is not recommended for a GMR-dependent seismic damage assessment. Hand-crafted IMs were required by the FNN model to achieve high prediction performance, as demonstrated in previous studies [15; 16; 17].  
In terms of the computing resources, GPUs are more expensive with less memory size than CPUs. The 2D CNN model had the most trainable parameters and almost reached the GPU memory limit because the WT-encoded GMR images had large input sizes. A batch size larger than 32 in the 2D CNN training could cause problems due to the lack of GPU memory. Compared to the 2D CNN model, the 1D CNN model reduced the trainable parameters by 86% and the memory usage by 69%. The FNN model had the smallest memory usage due to its shallow architecture with middle-size learnable parameters, but the FNN model is not recommended because it was the least accurate. Compared to the 1D CNN model, the 2D CNN model with WT encoding increases the dimensionality of the input GMRs, further leading to computing inefficiency, especially in CPU computing. As stated in [62], the data volume, dimensionality, acceptable time, and storage complexities are important to a novel efficient, effective algorithm. Therefore, based on the results of prediction performance, computing time, and resource usage, the 1D CNN model is recommended for rapid post-earthquake damage assessment among these three studied neural network models. Note that the scarcity of reliable historical GMRs on a specific site, instead of using GMRs scaled and synthesized from historical GMRs all over the world, is still a challenge to training neural network-based seismic classifiers that require a large training set to achieve desirable prediction performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{\begin{tabular}{c} **Trainable** \\ **Parameters** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Memory** \\ **Usage (MiB)** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **GMR** \\ **Preprocessing** \\ **Time (s)** \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} **Model Training** \\ **Time (s)** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **Model Testing** \\ **Time (s)** \\ \end{tabular} } \\ \cline{3-8}  & & & **GPU** & **CPU** & **GPU** & **CPU** & **GPU** & **CPU** \\ \hline
1 DCNN & 105,283 & 2299 & 0.08 & 0.09 & 18 & 66 & 0.11 & 0.15 \\ \hline
2 DCNN & 787,715 & 7423 & 106 & 104 & 504 & 6926 & 1.13 & 6 \\ \hline FNN & 313,259 & 313 & 0.08 & 0.09 & 12.7 & 14 & 0.07 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computing time and resources of the three neural network models.