# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 6 Conclusions  
In this study, a machine learning-based destructive power assessment method of earthquake to structures is proposed. The method is proposed based on the backpropagation neural network (BPNN)  
Figure 16: Features extracted from different convolutional layers.  
and convolutional neural network (CNN). The results of 50,570 numerical simulations are used as samples to train the machine learning models, and some conclusions can be drawn as follow:  
1. For the feature extraction of ground motion time-history data, STFT can well reflect the frequency-/time-domain features of ground motions. The prediction using STFT as feature extractor yield higher accuracy (R\({}^{2}\) = 0.6784) than that using FFT (R\({}^{2}\) = 0.4277).
2. The selection of the number of layers and nodes of the BPNN is related to the features. In this study, in the case of 40 features as input, the optimal model is the BPNN with two hidden layers and three nodes in each layer. For the case of 10 features as input, the optimal model is the BPNN with two hidden layers and four nodes in each layer.
3. The model based on the CNN exhibits better prediction accuracy (R\({}^{2}\) = 0.8737) than the BPNN model (R\({}^{2}\) = 0.6784). This is because the convolution layer of the CNN can identify the frequency-/time-domain information of a signal, and avoid the bias caused by artificial feature extraction in the meantime. The advantage of the CNN is magnified for its deep network architecture and multiple filters in each layer.
4. Feature visualization of convolutional layers reveals that the shallow convolutional layers of the CNN model mainly extract the high-frequency features of a signal, while the deep convolutional layers primarily extract the low-frequency features of a signal. In addition, the features extracted by the CNN model can reflect the time-domain features of a signal.
5. The CNN model exhibits remarkable computational efficiency compared with THA-based method. For example, using a laptop platform (i5-4210H, 8G of RAM, GTX 960M), the prediction of 1000 structures using the CNN model takes 0.762 s, which is over 650 times faster than that using the THA-based method (507.81 s). The high computational efficiency of the CNN-based seismic response prediction makes it promising for use in the timely assessment of regional structures.  
The research proves that the CNN model can make a significantly faster prediction of engineering demand parameters compared with THA-based method. The predicted engineering demand parameters can be used to assess the damage, economic loss, and downtime of regional structures based on damage assessment methods [9; 56], to assist in emergency response and rapid disaster relief.  
Conceptualization, C.X.; Data curation, R.Z.; Funding acquisition, C.X.; Investigation, R.Z., X.D. and Q.L.; Methodology, R.Z. and C.X.; Project administration, C.X.; Resources, C.X. and Y.L.; Software, R.Z. and C.X.; Supervision, C.X. and Y.L.; Writing-original draft, R.Z. and C.X.; Writing-review & editing, R.Z., C.X., X.D. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program (Grant No. 2019 YFC1511003), Intellectual Innovation Program of Shenzhen Science and Technology Innovation Committee (Grant No. JCYJ20180305123919731) and National Natural Science Foundation of China (Grant No. 51708361).  
The authors declare no conflict of interest.