## 2 Methodology
### Convolutional Self-Attention Neural Network (CSANN) Model Construction  
The CNN model used for target-specific damage identification requires the collection of structural vibration data from the target in order to construct a comprehensive deep learning dataset. In practical engineering applications, a combination of finite element analysis and field testing allows us to acquire the original acceleration responses from each measurement point under different damage states, forming the initial dataset. Furthermore, there exists a correlation between data points at key nodes within the structure, occurring simultaneously. Therefore, segments of acceleration signals within the same time interval are continuously concatenated to enhance the model's ability to distinguish signals under diverse operating conditions. During the acquisition process, each signal sample in the dataset is labeled and differentiated using serial number labels based on the state of the structure. Once the dataset is appropriately labeled, it serves as the training set to be fed into the CNN network. The core structure of the CNN model comprises an input layer, a hidden layer, and an output layer. Typically, the hidden layer is composed of several convolution layers, pooling layers, and fully connected layers, as illustrated in Figure 1.  
In this paper, we employ a one-dimensional convolutional neural network to construct our model. When compared to two-dimensional convolutional neural networks, the one-dimensional variant offers the advantages of being lighter in weight and easier to train due to having fewer parameters. Moreover, it demonstrates superior performance when processing time series data. Importantly, 1D-CNN typically does not require altering the dimension of the input signal, allowing it to preserve the signal characteristics to the fullest extent. Drawing inspiration from the earlier work of scholar [PERSON] [43], we adopt the rectified linear unit (ReLU) as the activation function for the convolutional layer. This choice effectively enhances the performance of the deep convolutional neural network when compared to alternative functions like sigmoid and tanh. In our paper, ReLU is specifically chosen as the activation function for constructing the convolutional neural network model for function \(K\), expressed as follows:  
\[K=F_{ReLU}(x)=max(0,x) \tag{8}\]  
In Equation (8), \(F_{ReLU}(x)\) represents the activation function and \(x\) is the element value of the input function. The convolutional layer calculation is shown in Figure 2.  
Building upon the foundation of the CNN model, we incorporate a self-attention layer. The attention mechanism can be elucidated through the mapping relationships between the query vector, a series of key-value vector pairs, and the resulting output vectors. The core formula of the self-attention mechanism is presented in Equation (9). In this context, the output vector is derived from a weighted summation of the value vectors. Each value vector's weighting coefficient is determined by assessing the compatibility between the query vector and its corresponding key vector. The proportion between the query vector and the main vector is then calculated using these weighting coefficients. Figure 1: Core structure of convolutional neural network. When the query vector, key vector, and value vector pertain to the same sequence, this is referred to as self-attention. In the process of computing a series of query vectors, it is customary to combine the query vector, key vector, and value vector. This is achieved by utilizing query matrices, key matrices, and value matrices to enhance computational efficiency. The underlying principle of the self-attention mechanism is depicted in Figure 3. The dot product method involves multiplying two vectors with different matrices, denoted as \(w\), to obtain \(q\) and \(k\). The dot product of \(q\) and \(k\) yields \(\alpha\).