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Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques

Galvanised steel atmospheric corrosion is a complex multifactorial phenomenon that globally affects many structures, equipment, and sectors. Moreover, the International Organization of Standardization (ISO) standards require specific pollutant depositions values for any atmosphere classification or...

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Autores principales: Terrados-Cristos, Marta, Ortega-Fernández, Francisco, Alonso-Iglesias, Guillermo, Díaz-Piloneta, Marina, Fernández-Iglesias, Ana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308046/
https://www.ncbi.nlm.nih.gov/pubmed/34300831
http://dx.doi.org/10.3390/ma14143906
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author Terrados-Cristos, Marta
Ortega-Fernández, Francisco
Alonso-Iglesias, Guillermo
Díaz-Piloneta, Marina
Fernández-Iglesias, Ana
author_facet Terrados-Cristos, Marta
Ortega-Fernández, Francisco
Alonso-Iglesias, Guillermo
Díaz-Piloneta, Marina
Fernández-Iglesias, Ana
author_sort Terrados-Cristos, Marta
collection PubMed
description Galvanised steel atmospheric corrosion is a complex multifactorial phenomenon that globally affects many structures, equipment, and sectors. Moreover, the International Organization of Standardization (ISO) standards require specific pollutant depositions values for any atmosphere classification or corrosion loss prediction result. The aim of this research is to develop predictive models to estimate corrosion loss based on easily worldwide available parameters. Experimental data from internationally validated studies were used for the data mining process, basing their characterisation on seven globally accessible qualitative and quantitative variables. Self-Organising Maps including both supervised and unsupervised layers were used to predict first-year corrosion loss, its corrosivity categories, and an uncertainty range. Additionally, a formula optimised with Newton’s method has been proposed for extrapolating these results to long-term results. The predictions obtained were compared with real values using Euclidean distances to know its similarity degree, offering high prediction performance. Specifically, evaluation results showed an average saving of up to 16% in coatings using these predictions. Therefore, using the proposed models reduces the uncertainty of the final structures state by predicting their material loss, avoiding initial over-dimensioning of structures, and meeting the principles of efficiency and sustainability, thus reducing costs.
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spelling pubmed-83080462021-07-25 Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques Terrados-Cristos, Marta Ortega-Fernández, Francisco Alonso-Iglesias, Guillermo Díaz-Piloneta, Marina Fernández-Iglesias, Ana Materials (Basel) Article Galvanised steel atmospheric corrosion is a complex multifactorial phenomenon that globally affects many structures, equipment, and sectors. Moreover, the International Organization of Standardization (ISO) standards require specific pollutant depositions values for any atmosphere classification or corrosion loss prediction result. The aim of this research is to develop predictive models to estimate corrosion loss based on easily worldwide available parameters. Experimental data from internationally validated studies were used for the data mining process, basing their characterisation on seven globally accessible qualitative and quantitative variables. Self-Organising Maps including both supervised and unsupervised layers were used to predict first-year corrosion loss, its corrosivity categories, and an uncertainty range. Additionally, a formula optimised with Newton’s method has been proposed for extrapolating these results to long-term results. The predictions obtained were compared with real values using Euclidean distances to know its similarity degree, offering high prediction performance. Specifically, evaluation results showed an average saving of up to 16% in coatings using these predictions. Therefore, using the proposed models reduces the uncertainty of the final structures state by predicting their material loss, avoiding initial over-dimensioning of structures, and meeting the principles of efficiency and sustainability, thus reducing costs. MDPI 2021-07-13 /pmc/articles/PMC8308046/ /pubmed/34300831 http://dx.doi.org/10.3390/ma14143906 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Terrados-Cristos, Marta
Ortega-Fernández, Francisco
Alonso-Iglesias, Guillermo
Díaz-Piloneta, Marina
Fernández-Iglesias, Ana
Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques
title Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques
title_full Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques
title_fullStr Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques
title_full_unstemmed Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques
title_short Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques
title_sort corrosion prediction of weathered galvanised structures using machine learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308046/
https://www.ncbi.nlm.nih.gov/pubmed/34300831
http://dx.doi.org/10.3390/ma14143906
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