# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 1 Introduction
The training set is used to train the model, and the validation set is used to verify the detection performance of the training model. Then, the model is used to identify the structural damage. Furthermore, support vector machines are friendly to small datasets. However, the dataset needs to be processed by feature engineering [23; 24]. The core of feature engineering is the method of feature extraction. It includes data preprocessing, feature selection, and dimensionality reduction. Different types of damage extract different features. So, feature engineering requires a lot of experimental experience. The feature selection of data directly determines the accuracy of this type of damage identification method. Therefore, a damage identification method without having to rely on feature engineering is required. In recent years, convolutional neural networks have made remarkable achievements in the field of object detection. The convolutional neural network was first proposed in 1985 [25]. It was then applied to the multi-layer neural network, and the first convolutional neural network model was proposed [26]. However, the computational complexity of the model is too large. The development of convolutional neural networks has been stagnant. Until 2006, [PERSON] pointed out that multiple-hidden-layer neural networks have superior feature learning ability [27]. Since then, convolutional neural networks have developed continuously and have achieved amazing results in various fields [28]. The convolutional neural networks extract features by a series of layers such as convolutional layer and pooling layer. It can realize the end-to-end object detection, and the training model can be used to directly classify the input data. The method requires a large amount of data. The existing health monitoring system generates a large amount of monitoring data. How to use them effectively has become an important issue. The big data and convolutional neural networks can improve the accuracy of structural damage detection [29; 30; 31]. In 2017, [PERSON] et al. [32] applied the convolutional neural network to crack detection. They can identify and locate cracks in the image. Then, deep learning is widely used in the field of damage detection [33]. Hurricanes as a natural disaster often cause severe damage to infrastructure [34]. [PERSON] et al. [35] used CNN to identify trees and their types from satellite images. The tree damage assessment module was used to calculate the vulnerability function and thus estimate the likelihood of road closure. [PERSON] et al. [36] proposed an automatic disaster detection system based on CNN and satellite images that can be used to identify landslides and floods. Experimental results showed that the system has an average identification accuracy of between 80% and 90% for disasters. [PERSON] et al. [37] proposed a method that combines Mask R-CNN and building boundary regularization to extract and localize building polygons from satellite images. Experimental results showed that the proposed method produces better regularized polygons. [PERSON] et al. [38] proposed a Faster R-CNN and machine vision-based damage identification method for five different types of surface damage. The results showed that the proposed method has an average precision of 87.8% and the detection speed of 0.03 s per image. However, these methods belong to supervised learning, which requires data with labels. In contrast, unsupervised learning does not require data labeling, which greatly improves the engineering application scenario. In the unsupervised mode, structural damage detection is referred to as unsupervised novelty detection. [PERSON] et al. [39] proposed a fast-clustering algorithm based on density peaks for structural damage detection and localization. A Gaussian kernel function with radius was introduced to calculate the local density of data points, and a new damage-sensitive feature was proposed. [PERSON] et al. [40] proposed an unsupervised learning method based on deep auto-encoder with a one-class support vector machine.