## 1 Introduction  
In recent years, the establishment of an efficient health monitoring system capable of accurately detecting internal signals from civil structures has emerged as a prominent concern in the field of civil engineering [1; 2; 3; 4]. In the structural health monitoring system (SHMS), classical on-site measurement methods such as ambient vibration testing [5], forced vibration testing [6], and impact vibration testing [7], which are direct methods, often require the installation of numerous sensors directly on the bridge structure [8; 9], and the selection of a vibration parameter that is sensitive to structural damage (e.g., frequency [10], mode shape [11], strain energy [12], etc. ), the parameter is then extracted using sensors and analyzed using a specific method [13; 14]. It is necessary to overcome uncertainties associated with the structural model and the model parameters themselves, including measurement errors, temperature and humidity variations, and other external environmental influences on the structural vibration characteristics. Characteristic signal feedback is utilized to determine the presence of damage in civil structures, enabling the assessment of its location, extent, as well as the current state, functionality, and trend of structural damage [15; 16; 17; 18]. Currently, structural damage identification can be categorized into two main approaches: (a) Local detection of structural damage using techniques such as gamma-ray, ultrasonic, and electromagnetic testing [19]. (b) Global detection of structural damage through frequency response functions (e.g., acceleration response) and modal parameters (e.g., damping, natural frequency, mode shapes), enabling non-destructive detection of existing damage in structures under undamaged or minimally damaged conditions [20; 21]. Existing research has demonstrated that methods based on dynamic response signal data yield significantly improved results in structural damage identification compared to traditional non-destructive techniques [22]. Many cutting-edge techniques in computer vision today rely on deep neural networks [23]. The seamless integration of deep learning technology into the realm of civil engineering has prompted both domestic and international researchers to explore the potential of deep learning models like convolutional neural networks (CNN) [24; 25; 26] and recurrent neural networks (RNN) in addressing challenges within the field of structural damage identification. [PERSON] [27] employed wavelet transformation on one-dimensional data, subsequently fed into a CNN model as images. This validated the reliability of two-dimensional convolutional neural networks for fault diagnosis. [PERSON] et al. [28] utilized acceleration data from a scaled model of a reinforced concrete bridge as experimental data and employed a two-dimensional CNN for damage identification, demonstrating the network's effectiveness in recognizing minor damage. [PERSON] et al. [29] applied CNN to extract acceleration data generated by a simply supported beam finite element model, achieving robust recognition results under various damage conditions and amidst varying noise levels. [PERSON] et al. [30] combined 1D-CNN with the transfer function, utilizing the transfer function of the structural dynamic response of the ASCE benchmark as input data. Comparative analysis with time series and fast Fourier transform (FFT) data revealed that features derived from the transfer function data were more sensitive to damage. [PERSON] et al. [31] introduced a structural damage identification method that integrates CNN and long short-term memory (LSTM). The CNN model was employed to extract topological correlation features from vibration acceleration input data across multiple event windows, while LSTM further captured temporal dimension features. Experimental data from a scale model of a bridge construction were tested and yielding favorable results.