# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 1 Introduction  
Structural reliability is a comprehensive evaluation indicator of structural performance, including reliability, stability, and durability. A structure must be able to withstand natural disasters, such as earthquakes and typhoons. As a result of the material aging and environmental corrosion, the structure will inevitably face a variety of structural damage, such as cracking, spalling, and so on. These damages will affect the reliability of the building structure and even cause collapse. Therefore, health monitoring systems are widely used in various structures such as bridges [1; 2], harbors [3], high-rise buildings [4; 5; 6], large space structures [7], and so on. A health monitoring system includes data acquisition system, transmission system, data processing system, and safety assessment system [8]. A data acquisition system contains many sensors such as accelerometers, displacement meters, inclinometers, strain gauges, and stress gauges. These sensors collect huge amounts of data for health monitoring systems. The transmission systems are mainly divided into wired transmission system and wireless transmission system. With the continuous development of transmission technology, it provides convenience for health monitoring systems. In addition, the data processing system is the core of the health monitoring system. It determines the effect of implementation of the health monitoring system and provides the basis for structural safety assessment. The purpose of the data processing system is to identify the degree and location of structural damage. According to the theory of structural dynamics, structural damage can change the structural dynamic characteristics, such as the mode shape, frequency, and stiffness of the structure [9]. The degree and location of the damage can be detectedby the change of these modal parameters. Therefore, some structural damage detection methods [10; 11; 12; 13; 14] based on modal identification have been studied. These methods focus on the changes of the structural modal parameters. They can analyze the signal in both time and frequency domains. However, the frequency domain analysis of these methods mainly relies on the Fourier transform. When the Fourier transform extracts the frequency spectrum from the signal, all the time domain information of the signal is used. It lacks the ability to analyze the local features of the signal [15]. Nevertheless, the wavelet analysis has solved this problem. It uses a variable window to process the signal in both time and frequency domains [16]. The effect of structural damage on the output signal is very weak. The ability of wavelet analysis to extract local features can be used to identify the effect. Therefore, wavelet analysis is also widely used in the field of damage identification, such as damage recognition based on the principle of wavelet singularity detection [17] and damage identification by wavelet packet energy method [18]. Another type of damage identification technology is based on statistical information. At present, the mainstream structure in the building structure is the frame structure. The two most important components in the frame structure are beam and column. Thus, the possible damage locations of structures are limited. Collecting a large amount of information for limited damage is the advantage of statistical learning theory. This type of method collects the output signal of the structure under different conditions. Different damages have different effects on the output signal. Therefore, the damage identification problem can be converted into a classification problem. For example, the support vector machine [19; 20; 21; 22] takes the characteristic parameters of the signal as the input for the network and takes the degree and location of the structural damage as the outputs for the network. Then, the structural damage dataset is established. One part of the dataset is used as a training set and the other is used as a validation set.