# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
## 3 Damage Diagnosis Model and Process
### Damage Diagnosis Process  
The schematic diagram of the damage diagnosis process of the frame structure is shown in Figure 6. It can be seen from Figure 6 that the whole damage diagnosis process includes data processing, model training and damage diagnosis results. 1. Data processing The data processing process was as follows. Firstly, the vibration signal was collected by the sensor, then the vibration signal was filtered, and finally the vibration signal after filtering was processed by data slicing and enhancement and data normalization. 1. Data acquisition and filtering The data of each damage case is represented by \(S_{i}\), where \(i\) represents the case. Generally, the noise in the construction environment is Gaussian noise [44]. Therefore, a Gaussian low-pass filter was used to process the data collected by sensors, making it smoother, since it can reduce the influence of high-frequency noise on the results. The formula is shown in Equation (1). The filtered data is represented by \(SF_{i}\): \[G(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{x^{2}}{2\sigma^{2}}}\] (1) where \(\sigma\) is the standard deviation of input signal \(x\). 2. Data slicing and enhancement When the number of samples in the training dataset is small, the trained model cannot complete sufficient training, which will lead to over-fitting and thus cannot meet the ideal requirements. When the number of samples in the training dataset is larger, the model can learn more features and gain stronger generalization abilities. Therefore, in order to increase the number of samples in the training dataset and improve the training results, it was necessary to adopt the sliding window method for data enhancement. Different numbers of data samples can be obtained by adjusting the size of the sliding window. The sliding window method can obtain enough data samples. The filtered data \(SF_{i}\) after data enhancement using the sliding window method is expressed as \(SF_{i}\). For example, for a signal with \(K\) vibration points, the size of sliding window is set to \(W\), and the step of sliding window is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer Type** & **Kernel/Step Size** & **Channels** &
\begin{tabular}{c} **Output Size** \\ **(Width \(\times\) Depth)** \\ \end{tabular} & **Zero Padding** \\ \hline Conv\_1 & \(64\times 1/8\times 1\) & 16 & \(128\times 16\) & Yes \\ Max-pooling & \(2\times 1/2\times 1\) & 16 & \(64\times 16\) & No \\ Conv\_2 & \(32\times 1/4\times 1\) & 32 & \(64\times 32\) & Yes \\ Max-pooling & \(2\times 1/2\times 1\) & 32 & \(32\times 32\) & No \\ Conv\_3 & \(3\times 1/1\times 1\) & 64 & \(32\times 64\) & Yes \\ Max-pooling & \(2\times 1/2\times 1\) & 64 & \(16\times 64\) & No \\ Conv\_4 & \(3\times 1/1\times 1\) & 64 & \(16\times 64\) & Yes \\ Max-pooling & \(2\times 1/2\times 1\) & 64 & \(8\times 64\) & No \\ Conv\_5 & \(3\times 1/1\times 1\) & 64 & \(8\times 64\) & Yes \\ Max-pooling & \(2\times 1/2\times 1\) & 64 & \(4\times 64\) & No \\ SE-Res2 Net\_1 & & 128 & \(4\times 128\) & \\ SE-Res2 Net\_2 & & 128 & \(4\times 128\) & \\ SE-Res2 Net\_3 & & 128 & \(4\times 128\) & \\ SE-Res2 Net\_4 & & 128 & \(4\times 128\) & \\ SE-Res2 Net\_5 & & 128 & \(4\times 128\) & \\ Max-pooling & \(2\times 1/2\times 1\) & 128 & \(2\times 128\) & No \\ Fully\_1 & 100 & \(1\) & \(100\times 1\) & \\ Fully\_2 & 50 & \(1\) & \(50\times 1\) & \\ Softmax & 9 & \(1\) & \(9\times 1\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure and parameters of the new model. set to \(S\), then samples with a number of \(N\) can be obtained. The formula can be written as: \[N=\frac{K-W}{S}+1\] (2) 3. Data normalization Data normalization can process data according to the statistical distribution of samples. Normalized data tend to be smoother in training and can accelerate the convergence rate of the model, making the training process more stable.