A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring

Abstract

Civil structures, infrastructures and lifelines are constantly threatened by natural hazards and climate change. Structural Health Monitoring (SHM) has therefore become an active field of research in view of online structural damage detection and long term maintenance planning. In this work, we propose a new SHM approach leveraging a deep Generative Adversarial Network (GAN), trained on synthetic time histories representing the structural responses of both damaged and undamaged multistory building to earthquake ground motion. In the prediction phase, the GAN generates plausible signals for different damage states, based only on undamaged recorded or simulated structural responses, thus without the need to rely upon real recordings linked to damaged conditions.

A literature review: Generative adversarial networks for civil structural health monitoring

“…To identify and clarify the position of GANs in the civil SHM field, in terms of the type of GAN applications studied by the researchers, an illustrative figure is made (Figure 9) which shows the classification of the applications of GANs in civil SHM and the corresponding studies with the GAN models used in each study. As the main concept of GAN is to learn data domain and data generation, some studies solely studied data generation [ (Kanghyeok and do Hyoung, 2019;Xiong and Chen, 2019;Zhang and Wang, 2019;Tsialiamanis et al, 2020;Xu et al, 2021;Yu et al, 2021;Tsialiamanis et al, 2022a;Heesch et al, 2021;Colombera et al, 2021;Luleci et al, 2022b;Luleci et al, 2023)] (a total of 11 studies) by using original GAN or other GAN variants. Thus, the data generation category is separated from other categories.…”

“…In another study (Colombera et al, 2021) by the authors, a GAN-based autoencoder for SHM is proposed where DCGAN is used to train on damaged and undamaged acceleration data of a multistory building. Then, the authors concluded that the model was able to generate reasonable data for different damaged and undamaged states of the building.…”