Research Article Generative Adversarial Network for Damage Identification in Civil Structures Zahra Rastin , 1 Gholamreza Ghodrati Amiri , 1 and Ehsan Darvishan 2 1 Natural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran 2 Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran Correspondence should be addressed to Gholamreza Ghodrati Amiri; ghodrati@iust.ac.ir Received 19 May 2021; Revised 15 August 2021; Accepted 23 August 2021; Published 6 September 2021 Academic Editor: Carlo Rainieri Copyright © 2021 Zahra Rastin et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. Most of the proposed methods employ supervised algorithms that require data from different damaged states of a structure in order to monitor its health conditions. As such data are not usually available for real civil structures, using supervised algorithms for the health monitoring of these structures might be impracticable. is paper presents a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised SHM and damage identification. In the first stage, a deep convolutional GAN (DCGAN) is used to detect and quantify structural damages; the detected damages are then localized in the second stage using a conditional GAN (CGAN). Raw acceleration signals from a monitored structure are used for this purpose, and the networks are trained by only the intact state data of the structure. e proposed method is validated through applications on the numerical model of a bridge health monitoring (BHM) benchmark structure, an experimental steel structure located at Qatar University, and the full-scale Tianjin Yonghe Bridge. 1. Introduction Civil structures need to have their health state monitored regularly. is is necessary in order to detect damages in the early stage and ensure the safety of these structures by repairing the detected damages on time. SHM provides a practical means of automatic assessment of structural health conditions and thus has attracted so much attention in recent years. e main objectives of the studies conducted in this field are damage detection, localization, and quantifi- cation in civil, aerospace, and mechanical structures. SHM is conducted by processing the data acquired from a network of sensors installed on the structure [1]. With great advances in computational power and sensing tech- nologies, leading to the age of big data, new opportunities have been provided for SHM. Machine learning is a promising technology of artificial intelligence that is so popular for big data processing and has recently emerged in the field of SHM [2, 3]. Machine learning tools such as feedforward artificial neural network (ANN) [4–6], support vector machine (SVM) [7–9], and genetic algorithm (GA) [10–12] have been widely used in SHM and damage de- tection. Among machine learning techniques, deep-learn- ing-based algorithms are gaining more and more popularity due to their ability to extract damage-sensitive features automatically. Vision-based deep learning approaches have been uti- lized in health monitoring of structures for purposes such as crack detection [13–16], fatigue detection [17], concrete spalling detection [18], and corrosion assessment [19, 20]. Although these techniques have been proven to be effective in detecting visible damages, they cannot be used to detect damages hidden in internal parts of structures. erefore, most of the SHM studies are recently focused on vibration- based methods. ese methods use the vibration response of a structure to perform damage detection tasks. e idea behind vibration-based methods is that damage-induced changes in a structure alter its vibration response [21]. Vibration-based SHM and damage detection through deep learning can be performed using supervised or Hindawi Shock and Vibration Volume 2021, Article ID 3987835, 12 pages https://doi.org/10.1155/2021/3987835