# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning  
[PERSON]  
1 Research Center of Space Structures, Guizhou University, Guiyang 550025, China; [EMAIL_ADDRESS] (T.  
In recent years, numerous structural damage detection methods based on deep learning have been proposed [16; 17; 18] due to the remarkable performance of deep learning in various fields. To this end, researchers focus on designing effective deep learning models to extract features related to structural damage identification in order to accurately locate and classify structural damage. These methods can be mainly categorized into those employing Convolutional Neural Networks (CNNs) [19; 20; 21; 22; 23], those utilizing a Long Short-Term Memory (LSTM) [24; 25; 26], and those combining both CNNs and LSTM (CNN-LSTM) [27; 28; 29]. For a more detailed description of structural damage detection methods, please refer to the comprehensive reviews in [12; 30]. CNNs [19; 20; 21; 22; 23] can learn local features from the input signals, so the straightforward idea is to use a 1D CNN model to extract local features from the 1D signals. CNNs are a type of multilayer feedforward neural network that is extensively employed in various supervised classification tasks [31]. [PERSON] et al. [22] created an acceleration response database related to bridge structures for training a 1D CNN model and demonstrated that CNN was highly sensitive to small structural changes. However, it is difficult for 1D CNNs to process input signals from multiple sensors. To address this difficult, [PERSON] et al. [19] proposed concatenating 1D acceleration response signals from multiple sensors into a 2D matrix as inputs and applying a 2D CNN for structural damage detection. They collected acceleration response data from multiple sensors and constructed a 2D signal matrix, which was fed into a 10-layer 2D CNN for training. Although CNNs are highly effective in extracting local features, they struggle to capture global features present within acceleration response data. LSTM [24; 25; 26], as a representative model for capturing long-distance feature dependencies of signals, uses the output of the previous moment as the input of the current moment to form a recursive connection structure. [PERSON] et al. [24] used LSTM to identify structural damages. They input raw acceleration response data into LSTM to learn the long-term time-series features in the data. As a result, they obtained more accurate damage detection results compared to traditional machine learning algorithms. [PERSON] et al. [26] proposed a multi-category structural damage detection approach based on windowed LSTM. They used windowed sample sequences from signals as inputs for LSTM, enabling the detection of multiple categories and degrees of structural damage. Nevertheless, LSTM ignores the local information from the damage signal, and its performance will degrade when dealing with long-distance dependencies within response data. In recent years, some researchers have combined the local feature learning capability of CNNs with the global feature learning capability of LSTM to propose innovative structural damage detection methods based on CNN-LSTM frameworks [27; 28; 29]. These frameworks can not only automatically extract local features from sensor signals but also capture global feature dependencies within data, which is beneficial for improving the accuracy and robustness of models. However, these hybrid models still have difficulty robustly handling long-range dependencies in the response data. In addition to the model architecture, the noise present in the acceleration response signals obtained from sensors also affect the performance of the model in identifying structural damage. A number of studies [32; 33; 34] have shown that utilizing denoised signals as the input to train deep learning models can effectively enhance the classification accuracy of the models. [PERSON] et al.