journal article

Journal Article

Text

Frontiers of Structural and Civil Engineering. 2025

Predicting deterioration in paint-coated steel due to defects using a generative adversarial network approach

Springer Science and Business Media LLC

English

Text

2025-05-26

https://doi.org/10.1007/s11709-025-1180-9

The version of record of this article, first published in Frontiers of Structural and Civil Engineering, is available online at Publisher’s website: https://doi.org/10.1007/s11709-025-1180-9.

Corrosion significantly impacts the integrity of steel structures, making them more prone to damage and failure. Coating the steel surface with anti-corrosion paint is a prevalent method. Nevertheless, these coatings are susceptible to damage, and corrosion tends to initiate at and spread from the damaged points, potentially leading to severe localized deterioration. Accurately predicting the progression of corrosion and coating deterioration at these critical points is essential for effective maintenance of steel structures. This study focused on two different paint-coatings applied to SM400 steel, onto which defects of varied sizes and shapes were artificially induced to mimic real-world paint-coating damage. These specimens underwent the accelerated corrosion test (ISO 16539 Method B) to generate data on corrosion depth at various time intervals. Subsequently, a modified generative adversarial network (GAN) model was employed to develop a highly accurate prediction model for the deterioration of steel surfaces, where the inputs to the model are four sequential corrosion depth measurements, and the output is the predicted future corrosion depth distribution. The performance of the proposed model was quantitatively evaluated using the root mean square error (RMSE). The model demonstrated outstanding predictive accuracy across all defect scenarios presented in this study. Compared with both traditional GAN variants (Conditional GAN and Information Maximizing GAN), the proposed model demonstrated a lower RMSE in predictive accuracy. This finding underscores its capability for precise corrosion prediction in steel structures, even with a relatively small data set. This predictive capability holds significant potential for predictive maintenance and failure analysis in steel infrastructure. This study not only validates the use of GAN in predictive maintenance but also provides a novel approach for the early detection and management of corrosion, crucial for extending the lifespan of critical infrastructure.

This article is licensed under a Creative Commons Attribution 4.0 International License.

101419

Predicting deterioration in paint-coated steel due to defects using a generative adversarial network approach

Springer Science and Business Media LLC

English

2025-05-26

Frontiers of Structural and Civil Engineering

https://doi.org/10.1007/s11709-025-1180-9

https://hdl.handle.net/11094/101419