# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 6 Conclusions  
The authors proposed two CNN-based models (A and B) to estimate the seismic response spectra of the surface. They found that model B, whose input data are acceleration, velocity, and displacement response spectra at the bedrock and output data are acceleration response spectra on the surface, outperforms model A for a site. In addition, model B does not require tailored earthquake data with the same number of values and duration. Therefore, model B for each site was trained for every selected site as a main model. Moreover, the performance of model B was compared with the result of SHAKE2000.  
Among the 100 datasets in each site (12 sites) which are fabricated for the proposed model, 20 sets were used for the test. It predicts both values of the amplitudes and the natural periods fairly well, where the responses amplify the most, surpassing the performance of the physical model for all the sites with few exceptions. In particular, when the natural periods range from 0.01 to 0.3 s, the proposed model outperforms the conventional software. The average mean squared errors of the proposed model are dozens of times lower than the physical tool for specific sites. With the proposed CNN model, the evaluation of the ground response is available for sites that offer the earthquake measurements data.  
As subsequent research, the authors aim at constituting a model to evaluate the surface ground motion in sites where the soil properties are available, by implementing the soil properties into the dataset.  
Conceptualization, J.A.; methodology, S.H.; formal analysis and software, S.H., J.J., H.-T.N. and J.A.; validation, S.H., J.J. and J.A.; investigation, S.H., H.-T.N., J.J. and J.A.; data curation, S.H., H.-T.N. and J.A.; writing--original draft preparation, S.H., H.-T.N. and J.A.; writing--review and editing, S.H., H.-T.N. and J.A. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (grant 21 CATP-C152100-03).  
Not applicable.  
Data Availability Statement: Data are available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kyoshin.bosai.go.jp](https://www.kyoshin.bosai.go.jp)]([https://www.kyoshin.bosai.go.jp](https://www.kyoshin.bosai.go.jp)), accessed on 28 May 2021.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Site** & **CNN Model** & **SHAKE 2000** \\ \hline FKSH17 & 0.000138 & 0.001734 \\ FKSH18 & 0.011128 & 0.123401 \\ FKSH19 & 0.005695 & 0.014009 \\ IBRH13 & 0.011128 & 0.001150 \\ IWTH02 & 0.006520 & 0.005750 \\ IWTH05 & 0.002500 & 0.078644 \\ IWTH12 & 0.000301 & 0.001670 \\ IWTH14 & 0.001006 & 0.006529 \\ IWTH21 & 0.000243 & 0.009845 \\ IWTH22 & 0.000544 & 0.049496 \\ IWTH27 & 0.001015 & 0.002871 \\ MYGH04 & 0.002990 & 0.066302 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average mean squared error of the proposed model and SHAKE2000.