# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 1 Introduction  
Structural damage detection (SDD) is an important measure to avoid accidents of bridges in service. Structural damages, such as surface cracks, surface fall-off, and aging, generally exist and thus change the mass and stiffness of the structure [1]. The traditional SDD method, visual inspection by experienced engineers, is subjective, time-consuming, and laborious. To offset the disadvantages of the visual inspection method, vibration-based SDD methods have been developed [2, 3]. As structural damages will certainly cause changes in the structural stiffness and mass and then affect the natural frequencies and mode shapes of the structure [4, 5], some early research suggested natural frequencies as a damage index [6, 7], however, natural frequencies have been proved to be insensitive to the damage locations [8]. Compared with natural frequencies, mode shapes are more capable of detecting the local locations [9]. The derivatives of the mode shapes, e.g., the mode curvature, modal strain energy, and strain mode, were also proposed as damage indexes and some encouraging results have been achieved [10, 11]. However, these mode-based methods are vulnerable to the impact of the measurement environment. As the vibration responses of the structure reflect the structural damage information, thus the structural damage may be detected by vibration signals directly [12, 13]. Relevant researchshows that the vibration signal-based damage detection methods contain more damage information than modal parameters [14] and overcome the dependence of detection accuracy on specific modes. However, signal-based methods involve a lot of signal processing work, which requires a powerful data processing tool, such as machine learning algorithms. Classical machine learning algorithms include support vector machines (SVMs), a decision tree (DT), artificial neural networks (ANNs), and so on [15; 16]. While the research results show that SVMs and DTs are difficult to implement for large-scale training samples in complex situations, on the other hand, ANNs can easily overcome the above problems [17]. As a powerful data processing tool, ANNs can automatically extract the information of structural damages in signals and map this information to the structural damage states. Therefore, introducing an ANN into the field of damage detection has great application prospects. As a powerful data processor, ANNs have been widely used in pattern recognition, prediction and estimation, automatic control as well as the field of SDD [18]. An ANN is composed of many artificial neurons, which are essentially nonlinear functions. The inputs and outputs of the ANN are mapped by the nonlinear functions [19; 20]. However, the traditional ANN is time-consuming and over-fitting, which limits its application in practical engineering [21; 22]. In recent years, the convolutional neural network (CNN) has developed rapidly in various fields (e.g., image classification, object recognition, and so on.) [23; 24], and it has been successfully applied to crack detection [25] in civil engineering. However, the image-based detection method cannot evaluate the inner structural damages or damages in inaccessible locations. The combination of a CNN and vibration-based SDD indexes can overcome the limitations of the above image-based methods. [PERSON] et al. [26] used a one-dimensional CNN to detect damages in the numerical model of a steel beam. Their results show that a CNN based on acceleration signals can detect damages with an accuracy of 94.1%. [PERSON] et al. [27] carried out SDD experiments using loosened bolts to simulate the damage of a steel frame and accurately detected the location of the loosened bolts. [PERSON] et al. [28] detected structural stiffness and mass changes in T-shaped steel beams and real-world steel bridges. [PERSON] et al. [29] used a CNN to detect structural damages by combining structural dynamic responses with modal parameters and proved that introducing structural dynamic responses can effectively improve the damage detection effect.