Modern Heritage Restoration Using GANs: Insights into Polymer–Composite Aging

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 02 | Page :
    By

    Vijay Kumar,

  • Apurva Jain,

  • Rachna Narula,

  • Megha Sehgal,

  • Meena Siwach,

  • Tulika Bhatia,

  1. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  2. Assistant Professor, Department of Computer Science & Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Guru Gobind Singh Indraprastha University, New Delhi, India
  3. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  4. Assistant Professor, Department of Bachelor of Computer Applications, Bharati Vidyapeeth (Deemed to be university) Institute of management and research, New Delhi, India
  5. Assistant Professor, Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India
  6. B.Tech student, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

Abstract

In the Era of modernization, accomplish a Net Zero goes beyond installing more solar panels and wind turbines but we also need to make sure their lifespan possibility. With the help of Polymer compound for protective coatings to find the applications in solar cells, turbine blades, batteries that degrade over the course of time causes by sun exposure, moisture, and pollution. however, due to these same types of degradation have seen in the protective varnishes and materials used in paintings and sculptures. In the context there is technology plays a main role with the machine learning plays a main role for transitioning the approach in restoration work both for artwork as well as energy infrastructure which has traditionally depended on skilled manual inspection. The modern artwork restoration methods that use the algorithms of machine learning with a specific emphasis on CNNs, GANs, and reinforcement learning techniques. But there is challenges also arise which include the problem in fetching quality training data, computation method for a power needed substantial and we have to be careful about ethical issues especially when it comes to maintaining genuineness. The scientific tools of this study examine how varnishes and coatings on artworks break down over time and how polymer composites in renewable energy devices age and wear out. The true value of this study lies in assessing ML approaches on diverse datasets ranging from centuries old painting to modern solar panels in order for us to construct more robust algorithms to minimize waste and extend equipment life, directly advancing the net-zero clean energy transition.

Keywords: Machine learning, deep learning, artificial intelligence, cultural heritage, restoration, CNN.

How to cite this article:
Vijay Kumar, Apurva Jain, Rachna Narula, Megha Sehgal, Meena Siwach, Tulika Bhatia. Modern Heritage Restoration Using GANs: Insights into Polymer–Composite Aging. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
Vijay Kumar, Apurva Jain, Rachna Narula, Megha Sehgal, Meena Siwach, Tulika Bhatia. Modern Heritage Restoration Using GANs: Insights into Polymer–Composite Aging. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239805


References

  1. Pasupuleti, J. V., Mullu, S., & Anuradha, G. (2022). A Comprehensive Study on Monument Recognition Techniques and its Integration with Explainable AI. Conference Paper, December 2022, DOI: 10.1109/ICECA55336.2022.10009482.
  2. Hegadi, R. S. (2010). Image Processing: Research Opportunities and Challenges. National Seminar on Research in Computers, Bharathiar University, Coimbatore, India, 13 December 2010.
  3. Fareed, M. W.; Nassif, A. B.; Nofal, E. (2024). Exploring the Potentials of Artificial Intelligence Image Generators for Educating the History of Architecture. Heritage, 7(3), 1727-1753, DOI: 10.3390/heritage7030081.
  4. Jindam, S., Mannem, J. K., Nenavath, M., & Munigala, V. (2023). Heritage Identification of Monuments using Deep Learning Techniques. Indian Journal of Image Processing and Recognition, 3(4), June 2023, DOI: 10.54105/ijipr.D1022.063423.
  5. Gaber, J. A.; Youssef, S. M.; Fathalla, K. M. (2023). The Role of Artificial Intelligence and Machine Learning in Preserving Cultural Heritage and Artworks via Virtual Restoration. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences.
  6. Wang, C., Pan, J., Wang, W., Dong, J., Wang, M., Ju, Y., & Chen, J. (2023). PromptRestorer: A Prompting Image Restoration Method with Degradation Perception. NeurIPS 2023.
  7. Li, G.; Li, L.; Pu, Y.; Wang, N.; Zhang, X. (2022). Semantic Image Inpainting with Multi-Stage Feature Reasoning GAN. Sensors, 22(8), 2854.
  8. Gupta, V.; Sambyal, N.; Sharma, A.; Kumar, P. (2021). Restoration of Artwork Using Deep Neural Networks. Evolving Systems, 12:439–446.
  9. Prisilla, A. A., Pusparani, Y., Chao, W.-H., Lung, C.-W., & Rahimah Rum, M. (2023). Using Machine Learning to Classify Art Style in Naturalism and Realism. Kyoto Conference on Arts, Media & Culture 2023.
  10. Ukwuoma, C. C., et al. (2021). Image Inpainting and Classification Agent Training Based on Reinforcement Learning and Generative Models. DOI: 10.1109/ICM52667.2021.9664950.
  11. Basu, A., et al. (2023). Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey. IEEE Access, 11, 53939-53952.
  12. Goussous, J. S. (2020). AI-based Restoration: The Case of Petra. Civil Engineering and Architecture, 8(6), 1350-1358.
  13. Das, B. R., Maringanti, H. B., & Dash, N. S. (2021). Role of AI in Preservation of Culture and Heritage. Intl. Conf. on Digital Restoration of Cultural Heritage.
  14. Zhou, S., & Xie, Y. (2022). Intelligent Restoration Technology of Mural Digital Image. Wireless Communications and Mobile Computing.
  15. Ghaith, K. (2024). AI Integration in Cultural Heritage Conservation. International Journal of Emerging & Disruptive Innovation in Education.
  16. Kumar, S. Patel, & R. Sharma (2024). AI-Driven Approaches for Art Heritage Preservation: Review. IEEE Access, 12, 12678-12692.
  17. Li, J. Zhang, & T. Wang (2024). Digital Twin Technology in Cultural Heritage Management. IEEE Trans. Industrial Informatics, 20(4), 200-215.
  18. Brown, M., & Lee, A. (2024). AI-Powered Solutions in Heritage Conservation. International Journal of AI in Art and Culture, 15(2), 98-115.
  19. Garcia & R. Kumar (2024). CNNs for Cultural Heritage Site Preservation. Cultural Heritage ML Journal, 10(1), 47-62.
  20. Nguyen & C. Torres (2024). Digital Reconstruction of Archaeological Artifacts Using AI. Journal of Digital Archaeology, 12(2), 72-89.
  21. Vashishtha, S., Gaur, H., Das, U., Sourav, S., Bhattacharjee, E., & Kumar, T. (2024). Optifake: optical flow extraction for deepfake detection using ensemble learning technique. Multimedia Tools and Applications, 1-19. https://doi.org/10.1007/s11042-024-18641-x
  22. Vashishtha, S., Gupta, V., & Mittal, M. (2023). Sentiment analysis using fuzzy logic: A comprehensive literature review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(5), e1509. DOI: https://doi.org/10.1002/widm.1509
  23. Syal, Jessica Singh, Chinmay Khanna, Payal Malik, and Srishti Vashishtha. “Leveraging ML Power for Crowdfunding Success Evaluation and Security Enforcement.” In 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), pp. 1-6. IEEE, 2024. DOI: 10.1109/ISCS61804.2024.10581049 or https://ieeexplore.ieee.org/document/10581049
  24. Goyal, Gaurav, Rajat Gupta, Srishti Vashishtha, Chetan Kumar Singh, Hammad Aqdas, and Harsh Garg. “Cognitive Video Analysis for Anomaly Detection Using Deep Learning.” In 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), pp. 1-6. IEEE, 2024. https://doi.org/10.1109/ISCS61804.2024.10581360
  25. Vashishtha, Srishti, Sarthak Jain, Sunny Singh, Vasu Gupta, and Shaveta Arora. “Digitalization of Objects using Augmented Reality and Machine learning-A Secure and Seamless Approach”. In 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), pp. 1-5. IEEE, 2024. DOI: 10.1109/ISCS61804.2024.10581069 or https://ieeexplore.ieee.org/document/10581069
  26. Vashishtha, S., Kumar, S., Bothra, V., Singhal, V., & Sharma, A. (2022). Vehicle detection system using YOLOv4. 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST), 1–5. https://doi.org/10.1109/AIST55798.2022.10064928
  27. Dua, A., Bhatia, A., Kalra, B., & Vashishtha, S. (2021, September). A Novel Recurrent and Convolutional Neural Network Technique for Generating Handwriting from Voice. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1439-1444). IEEE.
  28. Celina M. Review of polymer oxidation and its relationship with materials performance and lifetime prediction. Ann N Y Acad Sci. 2023 DOI: 10.1111/nyas.15368
  29. Zhang X, Wang Y, Li H. Thermal and environmental aging behavior of polymer composites. Mater Res Express. 2023. DOI: 10.1088/2053-1591/acefb0
  30. Li Y, Chen J, Zhang L. Moisture-induced degradation of polymer–lignocellulosic composites. BioResources. 2024. DOI: 10.15376/biores.19.2.2609-2625
  31. Kumar R, Singh A, Verma P. UV-induced aging and physicochemical changes in polymer composites. BioResources. 2024.. DOI: 10.15376/biores.19.2.2353-2370
  32. Smith T, Brown D, Lee K. Durability and fatigue behavior of fiber-reinforced composites under environmental exposure. J Reinf Plast Compos. 2024. DOI: 10.1177/07316844241238507

 

 

 

 

 

 

 

  1. Zhao L, Liu M, Chen X. Chemical and structural evolution in polymer composites under environmental aging. Mater Chem Phys. 2025.

DOI: 10.1016/j.matchemphys.2025.130439

Ahead of Print Subscription Original Research
Volume 14
02
Received 09/02/2026
Accepted 05/03/2026
Published 07/04/2026
Publication Time 57 Days
34

Login


My IP

PlumX Metrics