# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 1 Introduction  
Structural damage detection is an important research field of structural health monitoring (SHM) [1, 2, 3]. During the usage of a bridge structure, the internal damage to the structure will be caused by material corrosion and structural performance degradation. In order to prevent casualties and property losses caused by the sudden collapse of the structure, it is urgent to carry out the identification of structural damage. Traditionally, whether the structure is damaged is judged by changes in the time domain, frequency domain, and mode. It is usually necessary to fix the sensor to obtain the vibration signal of the structure. The obtained signal is transformed into the corresponding identification index through algorithm processing, so as to qualitatively judge the damage location of the structure and quantitatively judge the damage degree of the structure. Among them, the damage identification method of global dynamic characteristics based on natural frequency [4, 5], modal shape [6], and modal strain energy [7] (MSE) is not easy to observe obvious damage through experiments in the process of determining the damage location and damage degree, and the natural frequency is a global variable. It is difficult to identify the local damage of the structure, especially to judge the damage of the symmetrical position under the load. Although the MSE can identify the damage location of the unit, the complete modal shape is needed in the process of solving, so there are also some problems in the extension of the vibration mode, and there will be deviations in the actual measurement process, which is difficult to apply to the practical project. In order to overcome the above limits, a signal processing technology, such as wavelet analysis, is proposed, which is widely used in the field of structural damage detection [8; 9; 10]. Then, wavelet packet analysis, which can decompose low-frequency and high-frequency signals simultaneously, is introduced. It has higher decomposition precision and a better signal processing ability [11; 12; 13]. The extracted damage characteristics include the wavelet coefficient [14], wavelet or wavelet packet band energy [15; 16], wavelet energy ratio [17], etc. Since the energy before and after structural damage will be partially suppressed, so the constructed energy ratio variation (ERV) can correspond to the damage of the structure, which can better reflect the damage information of the structure. In the process of extracting features, it will be disturbed by some noise, so it is necessary to further process the features. For the traditional method of the artificial neural network (ANN) [18; 19], the most representative are the BP neural network [20] and radial basis function (RBF) [21; 22] neural network, which can not only extract the corresponding eigenvalues but also reduce the noise interference. Support vector machine (SVM) and ANN, as two highlights of damage identification methods, are used by many scholars to deal with the problems of structural damage and classification. However, due to their inherent defects, such as only dealing with small sample problems, the calculation process is time-consuming, which easily leads to overfitting of data and has a great impact on early damage detection [23]. Therefore, in order to overcome the shortcomings of traditional neural network algorithms and machine learning algorithms, this paper introduces a convolutional neural network (CNN) that can process a large amount of data and does not require manual extraction of damage features. CNN replaces the multiplication of general neural networks with convolution operations and uses multiple convolution kernels to perform convolution processing separately to extract different types of features. The CNN structure contains multiple convolutional layers and pooling layers to extract more complete feature information. The applicability of the network architecture is verified in the processing of time series and energy features in this paper.