Abstract
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. This 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. The 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.
Highlights
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
To use the advantages of convolutional neural networks (CNNs) in an unsupervised manner, this paper proposes to use them in a new two-stage generative adversarial networks (GANs)-based technique for structural damage identification
To the best of the authors’ knowledge, the technique presented in this paper is the first application of GANs for unsupervised structural damage identification
Summary
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. E supervised learning algorithms need to be provided with labeled data from damaged states of a structure as training samples, in order to perform structural damage detection. As these data are not usually accessible for large civil infrastructures, these algorithms may be impractical for real-world applications. To solve this issue, unsupervised deep-learning-based techniques have lately been developed for SHM and damage detection purposes. Unlike the other unsupervised SHM techniques presented in the literature, the proposed method aims to detect, quantify, and locate structural damages using the raw vibration response of a monitored structure.
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