# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 4 Experimental Results  
### Neural Network Training  
Taking the four-story steel frame structure of Columbia University as the research object, the experiment research was carried out using the fault diagnosis method proposed in this paper. The experimental data obtained from the open-source data of the four-story steel frame structure fault diagnosis are published by Columbia University. The training of the convolutional neural network proposed in this paper was carried out on a laptop with an Intel Corei7-4710 MQ CPU and NVIDIA GT940M 2G GPU. The structure parameters of the model network used in experiments can be seen in Table 2. In the experiment, the data processing and convolutional neural network operating environments are Tensorflow 2.4.8, Keras 3.4.2, and Python 3.8.0.  
The training curve of the neural network contains information on the convergence rate, whether the network has been fitted, and whether the network is stable after training convergence. Five damage cases provided by Columbia University were used in this paper, and the data of each case contained 24,000 points. In order to get more abundant training samples, the sliding window data enhancement method was adopted in this paper. The window length is 1024, and the sliding step length is 24. Therefore, 957 original data samples of each case were obtained. A total of 4785 training samples were generated under five damage cases, 70% of samples were used for training the neural network and 30% of samples were used for testing the neural network. During training, the learning rate of the Adam algorithm is 0.001, the batch_size is 128, the dropout is 0.6, and the training epoch is 300. The training curve is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer Type** & **Kernel Size** & **Stride** & **Channel Size** & **Output Size** & **Padding** \\ \hline Convolution1 & 64 & 16 & 16 & 256 \(\times\) 16 & yes \\ Pooling1 & 2 & 2 & 16 & 128 \(\times\) 16 & yes \\ Convolution2 & 3 & 1 & 32 & 128 \(\times\) 32 & yes \\ Pooling 2 & 2 & 2 & 32 & 64 \(\times\) 32 & yes \\ Convolution 3 & 3 & 1 & 64 & 64 \(\times\) 64 & yes \\ Pooling 3 & 2 & 2 & 64 & 32 \(\times\) 64 & yes \\ Convolution 4 & 3 & 1 & 64 & 32 \(\times\) 64 & yes \\ Pooling 4 & 2 & 2 & 64 & 16 \(\times\) 64 & yes \\ Convolution 5 & 3 & 1 & 64 & 16 \(\times\) 64 & yes \\ Pooling 5 & 2 & 2 & 64 & 8 \(\times\) 64 & yes \\ LSTM1 & 100 & & 1 & 100 \(\times\) 1 & \\ Dropout & 0.6 & & 1 & 50 \(\times\) 1 & \\ LSTM2 & 50 & & 1 & 10 \(\times\) 1 & \\ Desne & 10 & & 1 & 10 \(\times\) 1 & \\ Softmax & 5 & & 1 & 5 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure parameters of model network.  
Figure 8: Training curve.  
As can be seen from Figure 8, there is an over fitting phenomenon before the training epoch is 100. With the increase of training epoch, the curve gradually converges smoothly; the values of loss function of the training set and test set converge to around 0. At the same time, the accuracy rate of the training set and test set reaches 100%, and there is no local gradient jump phenomenon.