# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 1 Introduction  
Structural damage detection, as an important part of structural health monitoring (SHM), aims to timely and accurately find out structural problems so that corresponding maintenances can be taken to ensure the safety and reliability of a structure during its service period. At present, the commonly used damage detection methods include appearance inspection, sample surveys, ultrasonic wave and X-ray, which are used to detect local internal and external defects of structures [1]; there are also other methods like static test methods, which are used to obtain static parameters of structures, such as structural stiffness, deflection, strain and moment of inertia. These detecting methods not only need a large amount of manpower and material resources, but also require a high professional skills and vast engineering experience of detection, in addition to the long detecting cycle and subjective assessment [2]. Dynamic detection methods based on structural modal parameters become a research hotspot in recentdecades and are gradually applied to engineering practice owing to its nondestructive, cost-effective, and efficient characteristics [3]. Meanwhile, the rapid development of digital measurement technology provides solid technical supports for the accurate measurement of structural dynamic responses. The applications of Digital Image Correlation (DIC) technology [4] and Unmanned Aerial Vehicles (UAVs) in damage detection [5; 6] also make the structural dynamic measurement more convenient and simpler. As the modal parameters (modal frequencies and mode shapes) of a structure represent its state and its local damage will cause their changes, which can be used to identify the change of the structure state [7]. However, these modal parameters are not always sensitive to a small damage, which makes it difficult and inefficient to determine the damage location through these modal parameters. Therefore, more effective indexes are generated, such as mode shape curvature and modal strain energy, etc. [8; 9; 10; 11; 12], which are more sensitive to a small damage of a structure. However, the traditional damage detection methods often misjudge damages or miss a damage for large and complex structures. As a damage of a structure often causes the changes of multiple damage indexes, while the traditional structural damage detection methods are all based on a single damage index, which cannot completely describe all the damage features of a complex structure. Therefore, it is vital to find a powerful tool to extract the damage features of a complex structure. With their powerful data processing ability and good robustness, neural networks can extract the features of measured data, thus they can be used to explore the structural performances under different damage scenarios. Over the last few decades, the back propagation (BP) neural network performed better in damage detection [13; 14] among many artificial neural networks applied in damage detection. However, it is slow converging, time-consuming, and prone to over fitting [15]. Now, the convolutional neural network (CNN), which includes convolutional layers into the neural network, has better feature extraction ability and overcomes some drawbacks of the BP neural networks. In recent years, deep learning algorithms based on CNNs have shown their superior performance in the field of SHM, several papers have studied the SHM data anomaly detection [16], crack recognition [17], mass change [18], damage detection of a three-dimensional (3D) steel frame [19], and the first-order sensitivity-based method was used for matching the FE model and the real model [20]. As easily obtained modal parameters, the first-order mode shape can be used as the CNN inputs. Though, low-order mode shapes are usually less sensitive to structural damages, the CNN can extract damage features from low-order mode shapes, which are not distinguishable for naked eyes; its excellent ability of feature extraction could make up the disadvantages of low-order mode shapes.