# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 4 Experimental Results
### TSNE Cluster  
TSNE clustering can reduce the output dimension of the specified convolution layer, pooling layer, activation layer, and full connection layer, so as to convert it into a one-dimensional scatterplot, intuitively showing the changes of datasets in the network [40]. Figure 10 is the TSNE clustering results of the damage location diagnosis of the building frame structure by using the method proposed in this paper under the condition of no noise.  
It can be seen from Figure 10 that the vibration data after the first convolution layer are relatively concentrated and mixed, resulting in the characteristic information of case 1, case 2, and case 4 being completely covered by the characteristic information of case 3 and case 5; after the second convolution layer, the characteristic information of each damage case is partially separated; after all convolution layers of WDCNN, the characteristic information of five damage cases is clearly separated, and after two layers of LSTM, the characteristic  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Noise Situation** & **No Noise** & \(-4\) **dB** & \(-2\) **dB** & \(0\) **dB** & \(2\) **dB** & \(4\) **dB** \\ \hline WDCNN & 0.2456 & 3.1714 & 2.5321 & 0.75787 & 0.6256 & 0.6213 \\ WDCNN + LSTM & 0.0785 & 0.1265 & 0.1013 & 0.0965 & 0.0798 & 0.0776 \\ CEEMDAN + WDCNN & 0.0001 & 0.0034 & 0.0023 & 0.0003 & 0.0003 & 0.0001 \\
1-level Wavelet decomposition + the proposed method & \(1.6\times 10^{-6}\) & \(3.5\times 10^{-4}\) & \(8.1\times 10^{-5}\) & \(6.0\times 10^{-5}\) & \(1.1\times 10^{-5}\) & \(9.7\times 10^{-6}\) \\
2-level Wavelet decomposition + the proposed method & 0.0071 & 0.0170 & 0.0146 & 0.0076 & 0.0074 & 0.0071 \\
3-level Wavelet decomposition + the proposed method & 0.0164 & 0.0187 & 0.0167 & 0.0156 & 0.0111 & 0.0166 \\
4-level Wavelet decomposition + the proposed method & 0.0211 & 0.0391 & 0.0360 & 0.0261 & 0.0386 & 0.0226 \\
5-level Wavelet decomposition + the proposed method & 0.0436 & 0.0645 & 0.0537 & 0.0508 & 0.0501 & 0.0512 \\ \hline \end{tabular}
\end{table}
Table 4: Loss.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **No Noise** & \(-4\) **dB** & \(-2\) **dB** & \(0\) **dB** & \(2\) **dB** & \(4\) **dB** \\ \hline WDCNN & 0.7799 & 0.4364 & 0.5321 & 0.65787 & 0.6523 & 0.7012 \\ WDCNN + LSTM & 0.9864 & 0.8801 & 0.8836 & 0.8932 & 0.9802 & 0.9863 \\ CEEMDAN + WDCNN & 0.9998 & 0.9854 & 0.9899 & 0.9603 & 0.9987 & 0.9991 \\
1-level Wavelet decomposition + the proposed method & 1.0000 & 0.9997 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
2-level Wavelet decomposition + the proposed method & 0.9995 & 0.9937 & 0.9951 & 0.9986 & 0.9991 & 0.9997 \\
3-level Wavelet decomposition + the proposed method & 0.9991 & 0.9921 & 0.9945 & 0.9983 & 0.9986 & 0.9993 \\
4-level Wavelet decomposition + the proposed method & 0.9987 & 0.9917 & 0.9937 & 0.9979 & 0.9981 & 0.9989 \\
5-level Wavelet decomposition + the proposed method & 0.9940 & 0.9916 & 0.9923 & 0.9968 & 0.9981 & 0.9987 \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy.  
information of five damage cases is completely and clearly separated, which indicates that LSTM captures the correlation characteristic information between reconstructed signals.