Forecasting infrastructure deterioration with GAN-inversion

Bianchi, Eric; Hebdon, Matthew H.

doi:10.1117/12.2595111

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…To achieve such a concept, domain adaptation techniques incorporating zero-shot, one-shot, or few-shot learning and/or adopting the mechanism of text-toimage synthesis such as using GAN in collaboration with Transformer and/or Diffusion Models could be very advantageous. • Many remarkable studies have been presented in the AI field lately [ (Dhariwal and Nichol, 2021;Ho et al, 2020;Devlin et al, 2018;Saharia et al, 2022;Ho et al, 2022;van den Oord et al, 2016;Rae et al, 2021;Brown et al, 2020;Ramesh et al, 2021;Nichol et al, 2021;Jumper et al, 2021;Cheng et al, 2022;Vahdat and Kreis, 2022)]. As some of them required interdisciplinary collaboration, some of them did not.…”

Section: Discussionmentioning

confidence: 99%

“…Data domain translation [ (Tsialiamanis et al, 2022b;Luleci et al, 2022c;Zhang et al, 2020;Yasuno et al, 2020;Bianchi et al, 2021)] (a total of five studies) is seen less frequently than the other applications in the literature but could be very promising and advantageous to many other problems in civil SHM. The core research problem of domain translation applications is learning the distinct mapping between the data domains.…”

Section: Frontiers In Built Environmentmentioning

confidence: 99%

“…Frontiers in Built Environment frontiersin.org concluded that BSS-GAN is able to achieve better detection performance over conventional models. In this study (Bianchi et al, 2021), the authors studied the image-based application of forecasting infrastructure deterioration with inverse GANs. Basically, they employed the StyleGAN2 model to learn the semantic boundaries in the images e.g., corrosion and no-corrosion in the steel structures.…”

Section: Figure 15mentioning

confidence: 99%

“…Examples of semantic deterioration forecasting where the first image on the left is the real image, and the following images are the gradually translated version of the real image (Bianchi et al, 2021).…”

Section: Figure 17mentioning

confidence: 99%

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

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

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Section: Discussionmentioning

confidence: 99%

Section: Frontiers In Built Environmentmentioning

confidence: 99%

Section: Figure 15mentioning

confidence: 99%

Section: Figure 17mentioning

confidence: 99%

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

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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“…Even though preventive maintenance of infrastructure is not a new topic of investigation, only a few researchers, however, have actually looked into deterioration forecasting based on historical visual inspection data. Recent advances in GANs have opened up new opportunities to fill this critical knowledge gap prevailing in this important area of research (Bianchi and Hebdon, 2021;Sekar and Perumal, 2022) and should be a focus of future studies.…”

Section: Vision-based Forecasting Of Structural Deteriorationmentioning

confidence: 99%

Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions

Mondal

Chen

2022

Front. Built Environ.

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Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research.

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Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AI

Sobien,

Kulkarni,

Batarseh

2025

J. Water Resour. Plann. Manage.

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Forecasting infrastructure deterioration with GAN-inversion

Cited by 5 publications

References 14 publications

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

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

Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions

Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AI

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