###### Abstract
However, traditional machine learning algorithms, such as ANN and naive Bayes, are shallow learning. The trained model has insufficient ability to recognize complicated nonlinear damage, and the accuracy largely depends on the sensitivity of the selected DSFs. Meanwhile, it is easy to fall into the local optimal solution problem during the solution process of evolutionary algorithms. Hence, it is essential to explore more effective ways to solve the aforementioned issues. In 2006, [PERSON] et al. [14] proposed deep belief networks and introduced a layer-by-layer pre-training method, which laid the foundation for the development of deep learning. Shallow learning mostly relies on manual experience or feature conversion methods to obtain features. In contrast, deep learning processes the original data through multilayer nonlinear transformation, and then, it outputs higher-dimensional and more abstract features, thereby avoiding complicated and repetitive feature engineering. Convolutional neural networks (CNNs) and long short-term memory networks (LSTM), which are of great application value, have gradually captured widespread attention from scholars in the engineering field. Various research studies have been conducted, which can be summarized into three aspects. (i) Structural defects detecting. [PERSON] et al. [15] designed a concrete crack detection method combining computer vision (CV) and CNN, which can detect concrete cracks, steel corrosion, bolt corrosion, and steel delamination. [PERSON] et al. [16] introduced VGGNet for structural damage detection through transfer learning technology, which shows that transfer learning technology has broad application prospects in image-based structural defects recognition. [PERSON] et al. [17] formulated a framework for crack recognition and extraction of bridge steel box girders based on a restricted Boltzmann machine. (ii) Structural damage identification. [PERSON] et al. [18] devised a CNN-based damage recognition method to accurately identify the damage location and degree of the simply supported beam finite element model and automatically learn the low order of the mode shape. [PERSON] et al. [19] used the vibration response signal as the model input for the CNN and separately trained the model for each node of the planar steel frame structure to determine the bolt connection state. [PERSON] et al. [20] proposed a novel method based on deep CNNs for identifying and locating damage to building structures equipped with intelligent control equipment. (iii) Data processing and state prediction. [PERSON] et al. [21] work out an anomaly detection method of structural health monitoring data based on CV and CNN. [PERSON] et al. [22] applied the advantages of LSTM to model time series changes and used it to predict the depth of groundwater levels in agricultural areas. [PERSON] et al. [23] utilized LSTM for sewer overflow monitoring and achieved better results than multilayer perception and wavelet neural networks. The above-mentioned research demonstrates that CNN does have a superior performance in feature extraction, and LSTM also has unique advantages in describing time series changes. Recently, the hybrid architecture of CNN and LSTM has been widely adopted due to its outstanding performance. For instance, [PERSON] et al. [24] introduced CNN-LSTM architecture to non-contact computer vision-based vibration measurement and used the structural vibration video to identify its basic modal frequency. [PERSON] et al. [25] employed the hybrid architecture to establish the mapping relationship between the external excitation and the nonlinear response, thereby detecting the early damage of the structure. [PERSON] et al. [26] proposed to use the CNN-LSTM model to help clinicians detect common atrial fibrillation in real time on routine screening electrocardiograms. [PERSON] et al. [27] utilized the CNN-LSTM model for handwritten font recognition, which significantly reduced the word error rate and character error rate. [PERSON] et al.