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

Abstract

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.

Graphical abstract

Keywords

corrosion / generative adversarial network / paint-coated steel / coating defects / predictive maintenance / failure analysis

References

[1] Han X, Yang D Y, Frangopol D M. Optimum maintenance of deteriorated steel bridges using corrosion resistant steel based on system reliability and life-cycle cost. Engineering Structures, 2021, 243: 112633

[2] Harsimran S, Santosh K, Rakesh K. Overview of corrosion and its control: A critical review. Proceedings in Engineering Sciences, 2021, 3(1): 13–24

[3] SmithMWest B N. Building Pathology. London: Routledge, 2021, 89–133

[4] Altunsu E, Güneş O, Öztürk S, Sorosh S, Sarı A, Beeson S T. Investigating the structural damage in Hatay province after Kahramanmaraş-Türkiye earthquake sequences. Engineering Failure Analysis, 2024, 157: 107857

[5] Al Jahdaly B A, Maghraby Y R, Ibrahim A H, Shouier K R, Alturki A M, El-Shabasy R M. Role of green chemistry in sustainable corrosion inhibition: A review on recent developments. Materials Today Sustainability, 2022, 20: 100242

[6] Kuznetsov S N. Scientific herald of VSUACE. Construction and Architecture, 2016, 4(32): 17–24

[7] Lazorenko G, Kasprzhitskii A, Nazdracheva T. Anti-corrosion coatings for protection of steel railway structures exposed to atmospheric environments: A review. Construction and Building Materials, 2021, 288: 123115

[8] Nazari M H, Zhang Y, Mahmoodi A, Xu G, Yu J, Wu J, Shi X. Nanocomposite organic coatings for corrosion protection of metals: A review of recent advances. Progress in Organic Coatings, 2022, 162: 106573

[9] Cui G, Bi Z, Zhang R, Liu J, Yu X, Li Z. A comprehensive review on graphene-based anti-corrosive coatings. Chemical Engineering Journal, 2019, 373: 104–121

[10] RungeJ MRunge J M. A Brief History of Anodizing Aluminum. Cham: Spinger, 2018, 65–148

[11] Edavan R P, Kopinski R. Corrosion resistance of painted zinc alloy coated steels. Corrosion Science, 2009, 51(10): 2429–2442

[12] AssadHLone I ASihmarAKumarAKumarA. An overview of contemporary developments and the application of graphene-based materials in anticorrosive coatings. Environmental Science and Pollution Research International, 2023: 1–20

[13] Faccini M, Bautista L, Soldi L, Escobar A M, Altavilla M, Calvet M, Domínguez E. Environmentally friendly anticorrosive polymeric coatings. Applied Sciences, 2021, 11(8): 3446

[14] Maile F J. Colorants in coatings. Physical Sciences Reviews, 2021, 6(11): 707–789

[15] Ma L, Ren C, Wang J, Liu T, Yang H, Wang Y, Zhang D. Self-reporting coatings for autonomous detection of coating damage and metal corrosion: A review. Chemical Engineering Journal, 2021, 421: 127854

[16] Lee C Y, Lin T J, Sheu H H, Lee H B. A study on corrosion and corrosion-wear behavior of Fe-based amorphous alloy coating prepared by high velocity oxygen fuel method. Journal of Materials Research and Technology, 2021, 15: 4880–4895

[17] Nicacio J A P, Oliveira F C, Dumont M R. Failure analysis and electrochemical testing of ammonium chloride corrosion in a heat exchanger in a diesel hydrotreating unit of a petroleum refinery. Engineering Failure Analysis, 2024, 156: 107758

[18] Croll S G. Surface roughness profile and its effect on coating adhesion and corrosion protection: A review. Progress in Organic Coatings, 2020, 148: 105847

[19] Paik J K, Lee J M, Park Y I, Hwang J S, Kim C W. Time-variant ultimate longitudinal strength of corroded bulk carriers. Marine Structures, 2003, 16(8): 567–600

[20] Lee J, Lee Y J, Shim C S. Probabilistic prediction of mechanical characteristics of corroded strands. Engineering Structures, 2020, 203: 109882

[21] Valor A, Caleyo F, Alfonso L, Rivas D, Hallen J M. Stochastic modeling of pitting corrosion: A new model for initiation and growth of multiple corrosion pits. Corrosion Science, 2007, 49(2): 559–579

[22] Feng J, Mikihito H, Kazuki O, Jia L. An analysis on spatial properties of corroded surface of weathering steel by different environmental conditions. Corrosion Engineering, Science and Technology, 2023, 58(6): 577–587

[23] Bramowicz M, Braic L, Azem F A, Kulesza S, Birlik I, Vladescu A. Mechanical properties and fractal analysis of the surface texture of sputtered hydroxyapatite coatings. Applied Surface Science, 2016, 379: 338–346

[24] Guo X, Kang J, Zhu J. Cellular automata-based numerical simulation of atmospheric corrosion process on weathering steel. Journal of Materials in Civil Engineering, 2018, 30(11): 04018296

[25] Zaki A, Chai H K, Aggelis D G, Alver N. Non-destructive evaluation for corrosion monitoring in concrete: A review and capability of acoustic emission technique. Sensors, 2015, 15(8): 19069–19101

[26] Ameli Z, Nesheli S J, Landis E N. Deep learning-based steel bridge corrosion segmentation and condition rating using mask RCNN and YOLOv8. Infrastructures, 2023, 9(1): 3

[27] Jiang F, Hirohata M. A GAN-augmented corrosion prediction model for uncoated steel plates. Applied Sciences, 2022, 12(9): 4706

[28] Jiang F, Hirohata M, Liu J, Ojima K. Application of accelerated cyclic test with synthetic ocean water salt-deposition process to the evaluation on corrosion characteristics of weathering steel. Corrosion Engineering, Science and Technology, 2022, 57(3): 280–289

[29] RonnebergerOFischerPBroxT. U-net: Convolutional networks for biomedical image segmentation. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention. Munich: Springer International Publishing, 2015, 234–241

[30] DosovitskiyABeyerLKolesnikov AWeissenbornDZhaiXUnterthiner THoulsbyN. An image is worth 16×16 words: Transformers for image recognition at scale. 2020, arXiv: 2010.11929

[31] SandlerMHoward AZhuMZhmoginovAChenL C. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE Computer Society, 2018, 4510–4520

[32] Fujii K, Ohashi K, Kajiyama H, Fujita S. Corrosion aspect of electrical appliances—Development of new accelerated corrosion test simulating appliances environment. Zairyo-to-Kankyo, 2006, 55(8): 349–355

[33] GoodfellowIPouget-Abadie JMirzaMXuBWarde-Farley DOzairSBengioY. Generative adversarial nets. Advances in Neural Information Processing Systems, 2014: 27

[34] MirzaMOsindero S. Conditional generative adversarial nets. 2014, arXiv:1411.1784

[35] ChenXDuan YHouthooftRSchulmanJSutskever IAbbeelP. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Advances in Neural Information Processing Systems, 2016: 29

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The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn