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Article

Artificial Neural Network-Based Modeling of Atmospheric Zinc Corrosion Rates Using Meteorological and Pollutant Data

by
Anoop K. Maurya
1,†,
Saurabh Tiwari
2,†,
Annabathini Geetha Bhavani
3,
Nokeun Park
2,4,* and
Nagireddy Gari Subba Reddy
5,*
1
School of Mechanical Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
2
School of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
3
SRM Institute of Science and Technology, Delhi-NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad 201204, Uttar Pradesh, India
4
Institute of Materials Technology, Yeungnam University, Gyeongsan 38541, Republic of Korea
5
Virtual Materials Laboratory, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Coatings 2025, 15(5), 538; https://doi.org/10.3390/coatings15050538
Submission received: 2 April 2025 / Revised: 24 April 2025 / Accepted: 29 April 2025 / Published: 30 April 2025
(This article belongs to the Special Issue Anti-corrosion Coatings of Metals and Alloys—New Perspectives)

Abstract

Understanding the depth and severity of corrosion is crucial for predicting the long-term durability and economic viability of Zn-based structures. This study investigates the relationship between meteorological and pollution parameters on the corrosion rate of zinc using an artificial neural network (ANN) model trained on global data. The model incorporates temperature, time of wetness (TOW), SO2 concentration, Cl concentration, and exposure time as input variables, with corrosion depth as the output. The ANN model demonstrated high predictive accuracy, achieving correlation coefficients of 0.99 and 0.95 for the training and test datasets, respectively, indicating strong agreement with the experimental data. A graphical user interface was developed to facilitate the practical application of the model. Sensitivity analysis using the index of relative importance (IRI) identified the SO2 concentration and TOW as the most influential factors, emphasizing their critical role in zinc corrosion. These findings enhance our understanding of the Zn corrosion dynamics and provide valuable insights into corrosion prevention strategies. A user-friendly graphical user interface (GUI) was developed using Java, enabling accurate prediction of the corrosion depth in zinc with approximately 95% accuracy without requiring prior knowledge of neural networks or programming.
Keywords: artificial neural network (ANN); index of relative importance (IRI); corrosion of Zn; exposure time; sulfur dioxide and chloride concentration; time of wetness (TOW) artificial neural network (ANN); index of relative importance (IRI); corrosion of Zn; exposure time; sulfur dioxide and chloride concentration; time of wetness (TOW)

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MDPI and ACS Style

Maurya, A.K.; Tiwari, S.; Bhavani, A.G.; Park, N.; Reddy, N.G.S. Artificial Neural Network-Based Modeling of Atmospheric Zinc Corrosion Rates Using Meteorological and Pollutant Data. Coatings 2025, 15, 538. https://doi.org/10.3390/coatings15050538

AMA Style

Maurya AK, Tiwari S, Bhavani AG, Park N, Reddy NGS. Artificial Neural Network-Based Modeling of Atmospheric Zinc Corrosion Rates Using Meteorological and Pollutant Data. Coatings. 2025; 15(5):538. https://doi.org/10.3390/coatings15050538

Chicago/Turabian Style

Maurya, Anoop K., Saurabh Tiwari, Annabathini Geetha Bhavani, Nokeun Park, and Nagireddy Gari Subba Reddy. 2025. "Artificial Neural Network-Based Modeling of Atmospheric Zinc Corrosion Rates Using Meteorological and Pollutant Data" Coatings 15, no. 5: 538. https://doi.org/10.3390/coatings15050538

APA Style

Maurya, A. K., Tiwari, S., Bhavani, A. G., Park, N., & Reddy, N. G. S. (2025). Artificial Neural Network-Based Modeling of Atmospheric Zinc Corrosion Rates Using Meteorological and Pollutant Data. Coatings, 15(5), 538. https://doi.org/10.3390/coatings15050538

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