## 5 Experimental Results and Discussion for Three-Story Building Structure
### Experimental Setup and Data Description  
Because the sliding window length is set to 324, every one-dimensional vibration signal with 324 sampling length is obtained. The vibration signal are transformed into frequency information with 324 \(\times\) 1 via FFT method. Then, frequency information is converted to \(F{r_{M}}=18\times 18\) features matrix via shape dimension transformation, considering that DCNN is adopted at extracting spatial features. Finally, DCNN extracts damaged features from \(F{r_{M}}\) matrix to identity structural damage. Figure 6 shows the process of features transformation of four different damage condition, including that damaged degree is 10%, damaged degree is 20%, damaged degree is 30%, and damaged degree is 40%. The frequency information of different damage conditions is different with increasing damage degree.  
Figure 5: \(K\)-fold crossvalidation method.  
Table 3 shows the configuration of the proposed method. The size of the convolutional kernel is 7 \(\times\) 7 in the first layer, and the size of the convolutional kernel is 5 \(\times\) 5 in the second. The maximum pooling layer is 2 \(\times\) 2 in the third layer, the size of the convolutional kernel in the fourth layer is 3 \(\times\) 3 in the fifth layer, and the size of the convolutional kernel is 3 \(\times\) 3 in the sixth layer, and the maximum pooling layer is 2 \(\times\) 2 in the seventh layer.  
Table 4 shows the four-fold crossvalidation result on three three-story building structures using the proposed method. In the four-fold crossvalidation, the accuracy of training datasets is 93.48% on average, and the accuracy of testing datasets is 93.29% on average. It shows that FFT-DCNN is suitable for identifying structural damage degree effectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Validation (\%)** & **Testing (\%)** \\ \hline Fold-1 & 93.31 & 93.72 \\ Fold-2 & 93.26 & 92.17 \\ Fold-3 & 93.38 & 93.95 \\ Fold-4 & 93.98 & 93.31 \\ Mean & 93.48 & 93.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of four-fold crossvalidation.  
Figure 6: Vibration signal decomposition via FFT method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Layer Name** & **Filter Size** & **Kernel Size** & **Stride** & **Padding** & **Output Size** \\ \hline First layer & Convolution 1 & 8 & \(7\times 7\) & \(1\times 1\) & 1 & \(18\times 18\) \\ Second layer & Convolution 2 & 12 & \(5\times 5\) & \(1\times 1\) & 1 & \(18\times 18\) \\ Third layer & Dropout 1 & – & – & – & – & \(18\times 18\) \\ Fourth layer & Max-pooling 1 & 12 & \(2\times 2\) & \(1\times 1\) & 1 & \(9\times 9\) \\ Fifth layer & Convolution 3 & 16 & \(3\times 3\) & \(1\times 1\) & 1 & \(9\times 9\) \\ Sixth layer & Convolution 4 & 20 & \(3\times 3\) & \(1\times 1\) & 1 & \(9\times 9\) \\ Seventh layer & Max-pooling 2 & 20 & \(3\times 3\) & \(1\times 1\) & 1 & \(3\times 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters configuration of DCNN.  
Figure 7 shows the accuracy curve for the proposed methods on Fold-1 datasets. The accuracy of training, testing, and validation reach 0.9 after achieving 200 epochs. The results show that FFT-DCNN presents an excellent ability of features extraction on the three-story building structure. In Figure 8, the loss curve of the FFT-DCNN model is smooth, which shows an excellent fitting ability and training process.  
Figure 8: Training loss history of the FFT-DCNN algorithm.  
Figure 7: Training accuracy history of the FFT-DCNN algorithm.