Application of machine learning for the prediction of atmospheric corrosion in the metropolitan area of Mexico City
Abstract
In this work, Machine Learning (ML) is applied to predict atmospheric corrosion in the Metropolitan Zone of Mexico City. For this purpose, mass loss is measured as a dependent variable associated with the independent variables relative humidity, wetting time, temperature and sulfur dioxide deposition time in 12 stations of the study site and with the generated database.
ML models are used with some supervised learning tools, such as: Neural Networks (NN), Regression Trees (RT), Optimized Regression Tree (ORT), Regression Ensemble (RE), Support Vector Machine (SVM) and Linear Regression (LR). For this problem, Neural Networks (NN) have the best results, with a Correlation Coefficient R2 = 0.9814 and a Mean Square Error MSE = 37.9.
The main results allow us to determine that the proposed framework can be extended to predict the behavior of other complex problems.
Downloads
References
L. Coelho, D. Zhang, Y. Van Ingelgem, D. Steckelmarcher, A. Nowé & H. Terrya, “Reviewing machine learning of corrosion prediction in a data-oriented perspective”, Materials Degradation, vol. 6, p. 1, 2022.
G.F. Hays, Now Is the Time, World Corrosion Organization, New York: NP, USA, 2010.
http://red.corrosion.org/Corrosion+Resources/Publications/_/nowisthetime.pdf
M.E. Emetere, S.A. Afolalu, L.M. Amusan & A. Mamudu, “Role of Atmospheric Aerosol Content on Atmospheric Corrosion of Metallic Materials”, Int. J. Corros. 2021.
E. Kusmiereck, E. Chrzescijanaka, “Atmospheric Corrosion of Metal in Industrial City Environment”, Data Brief, vol. 3, pp. 149-154, 2015.
G. Lazarenko, A. Kasprzhitskii & T. Nazdracheva, “Anti-Corrosion Coatings for Protection of Steel Railway Structures Exposed to Atmospheric Environment A Review”, Const. Build. Mater, vol. 288, pp. 115-123, 2021.
H. Jia, G. Qiao, & P. Han, “Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures-A review”, Cement and Concrete Composites, 104725, 2022.
https://www.sciencedirect.com/journal/cement-and-concrete-composites/vol/133/suppl/C
R. Muñoz Ledo & J. Uruchurtu Chavarría, “Characterization of atmospheric aggressiveness on structural metallic materials in the Metropolitan Zone of Mexico City”, Journal of Environmental Contamination, vol. 18, no. 1, pp. 27-32, 2002.
V. Duarte, S. Zuniga-Jara and S. Contreras, "Machine Learning and Marketing: A Systematic Literature Review," in IEEE Access, vol. 10, pp. 93273-93288, 2022, doi: 10.1109/ACCESS.2022.3202896.
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9869838
M. McInnes, C. Dick, C. Thring, D. Irving and D. Hughes, "Flexible, scalable, printed ultrasound sparse array for corrosion detection using machine learning," 2023 IEEE International Ultrasonics Symposium (IUS), Montreal, QC, Canada, 2023, pp. 1-4, doi: 10.1109/IUS51837.2023.10307983.
Y. Diao, L. Yan, & K. Gao, “Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features”, Materials & Design, vol.198, 109326, 2021. https://www.sciencedirect.com/science/article/pii/S0264127520308625
Z. Pei, D. Zhang, Y. Zhi, T. Yang, L. Jin, D. Fu, & X. Li, “Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sREor via machine learning”, Corrosion science, vol. 170, 108697, 2020. https://research.tudelft.nl/en/publications/towards-understanding-and-prediction-of-atmospheric-corrosion-of-
M. Terrados-Cristos, F. Ortega-Fernández, G. Alonso-Iglesias, M. Díaz-Piloneta, & A. Fernández-Iglesias, “Corrosion prediction of weathered galvanised structures using machine learning techniques”, Materials, vol. 14, no. 14, 3906, 2021.
E. Bolaños Rodríguez, G. Y. Vega Cano, E. Lezama León, A.E. Solis Galindo, I. Acuña Galván, A. Zárate Rosas & I.G. Pérez Torres. Application of Artificial Neural Networks for Modeling Steel Mass Loss due to Atmospheric Corrosion in the Metropolitan Area of Mexico City. Iberoamerican Conference on Systems, Cybernetics and Informatics, International Institute of Informatics and Systemics, Orlando, Florida, USA, 2022.
R. Garcés Rodríguez. (2002). Evaluation of Atmospheric Corrosion of Steel Exposed to Various Atmospheres. Thesis to Obtain the Degree of Master of Science in Mechanical Engineering with Specialty in Materials, UANL, School of Mechanical and Electrical Engineering. Graduate Studies Division. http://eprints.uanl.mx/5170/
J. E. Rodríguez Yáñez, L. Garrido Arce & E. Sabrío Leiva, “Atmospheric Corrosion Maps of Low Alloy Steel in Costa Rica”, Research Notebooks UNED, vol. 7, no. 2, pp. 181-191, 2015.
ISO 9223 (1987). Classification of Corrosivity Categories of Atmosphores. ISO, Ginebra.
H.L. Muñoz-Fernández, L.A. González-Mendoza & D. Y. Peña-Ballesteros, “Evaluation of the Corrosion Rate of API 5L Grade 65 Steel in a Brine-CO2-SiO3-Mineral Oil System, by Electrochemical Techniques”, Ingeniería y Universidad, vol. 13, no. 1, pp. 27-44, enero-junio, 2009.
Abhishek, Dhankar and N. Gupta, "A Systematic Review of Techniques, Tools and Applications of Machine Learning", 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021, pp. 764-768, doi: 10.1109/ICICV50876.2021.9388637. https://www.researchgate.net/publication/350535931_A_Systematic_Review_of_Techniques_Tools_and_Applications_of_Machine_Learning
Gupta, R. (2020, February). A survey on machine learning approaches and its techniques. In 2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1-6, IEEE. https://www.researchgate.net/publication/341239256_A_Survey_on_Machine_Learning_Approaches_and_Its_Techniques
J.C. González-Islas, O.A. Domínguez-Ramírez, O. López-Ortega, R.D. Paredes-Bautista, & D. Diazgiron-Aguilar, (2021). Machine learning framework for antalgic gait recognition based on human activity. In Advances in Soft Computing: 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Mexico City, Mexico, October 25–30, 2021, Proceedings, Part II 20 (pp. 228-239). Springer International Publishing. https://www.semanticscholar.org/paper/Machine-Learning-Framework-for-Antalgic-Gait-Based-Gonz%C3%A1lez-Islas-Dominguez-Ramirez/9cb1b6f6559d7a71adacfed901de504b2d4204f0
E. Bolaños Rodríguez; E. Lezama León & G.Y. Vega Cano, Characterization of Atmospheric Aggressiveness on Steel in the Mexico City Metropolitan Area, Technical Report, Universidad Autónoma del Estado de Hidalgo (UAEH), 2023.
S. Kokkotis, Moustakidis, Papageorgiou, G. Giakas, & D. E. Tsaopoulos, “Machine learning in knee osteoarthritis: A review”, Osteoarthritis and Cartilage Open, vol. 2, no. 3, 100069, 2020.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718265/
R.E. Nogales, & M.E. Benalcázar, “Analysis and Evaluation of Feature Selection and Feature Extraction Methods”, International Journal of Computational Intelligence Systems, vol. 16, no.1, p. 153, 2023. https://link.springer.com/article/10.1007/s44196-023-00319-1
X. Glorot, & B. Yoshua, “Understanding the difficulty of training deep feedforward neural networks”, In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256, 2010. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
L. Breiman, "Random Forests", Machine Learning, vol. 45, pp. 5–32, 2001. https://link.springer.com/article/10.1023/A:1010933404324
Copyright (c) 2024 ITEGAM-JETIA
This work is licensed under a Creative Commons Attribution 4.0 International License.
