## 1 Introduction  
Due to environment and load acting on structures, civil structures can produce different levels of damage, such as degradation, corrosion, and fatigue. It reduces the building service life, leading to threats to public safety [1]. Structural health monitoring (SHM) is a critical technology to assure the safety of civil engineering [2, 3]. SHM is mainly classified into two categories: data-driven methods based on statistical pattern recognition and physics-based methods based on finite element model updating [4]. Compared with physics-based methods building a numerical model, data-driven methods have many advantages over physics-based methods while identifying structural damage under load and environmental influence such as temperature and moisture effect [5]. Thus, many researchers have focused on data-driven methods for structural damage detection to protect the safety of civil structures. The data-driven method is usually decomposed into several steps: data acquisition, feature extract, damage detection method, and decision-making, where damage detection methods are a big challenge for SHM [6, 7, 8]. With the development of computing power, machine learning (ML) algorithms have been widely used in the SHM field. Since vibration signals can reflect structural damage conditions, ML algorithms usually utilize structural vibration data to recognize structural damage, especially in handling vibration data with small datasets [9, 10]. For example, [PERSON] [11] adopted a support vector machine (SVM) and differential evolution algorithm (DEA) to identify the structural damage in moment frame connections. Using semi-rigid beam simulated damage in structural connections, and then the vibration data of structures with damaged connections were generated by the analytical model. Then, these data were fed into the SVM model to update the model's parameters and weight. The result showed that the SVM-DEA had an excellent performance for structural damage detection. [PERSON] [12] proposed a method based on Bayesian theory and immune genetic algorithm (IGA) to recognize structural damage where Bayesian was applied to identify damage sites, and then improved IGA was utilized to identify the damage level. This study provided a two-stage method that can effectively assess the damage locations and extent. [PERSON] [13] utilized the person correlation coefficient to select essential information features, and then the features were fed into Ensemble Generalized Multiclass Support-Vector-Machine (EGMSVM) to recognize structural damage. To verify the effectiveness of the proposed method, different algorithms such as LDA, random forest, SVM, was tested on a simulated hydraulic platform. The result showed that the EGMSVM achieved high accuracy and low variance and deviation. However, these methods, such as support vector machine, ensemble algorithm, Bayesian algorithm, belong to \"shallow\" machine learning. In addition, although \"shallow\" ML algorithms recognize structural damage and location with high accuracy for small datasets, it is poor performance in handling the massive vibration data from SHM systems. With the development of sensors technology and data acquisition, SHM systems can collect an amount of data from various sensors installed on civil structures. Since deep learning methods effectively handle massive data, it has attracted much attention from many researchers in many fields such as image classification [14; 15] and natural language processing [16]. In these methods, vibration-based convolutional neural networks (CNN) algorithms are widely utilized in civil engineering since it is powerful in extracting the feature from raw vibration data to recognize structural damage. For example, [PERSON] [17] adopted CNN to capture abstract features and complex classifier boundaries and then classified the damaged and healthy condition of structures. The experimental result showed that CNN accomplished real-time damage diagnosis and location with high accuracy, robustness, and computational efficiency.