Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models
Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated w...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4231 |
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| author | Marta Terrados-Cristos Marina Diaz-Piloneta Francisco Ortega-Fernández Gemma Marta Martinez-Huerta José Valeriano Alvarez-Cabal |
| author_facet | Marta Terrados-Cristos Marina Diaz-Piloneta Francisco Ortega-Fernández Gemma Marta Martinez-Huerta José Valeriano Alvarez-Cabal |
| author_sort | Marta Terrados-Cristos |
| collection | DOAJ |
| description | Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure. |
| format | Article |
| id | doaj-art-0fa0305f44664c4292e4a1e7bcda94bc |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0fa0305f44664c4292e4a1e7bcda94bc2025-08-20T03:50:21ZengMDPI AGSensors1424-82202025-07-012513423110.3390/s25134231Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive ModelsMarta Terrados-Cristos0Marina Diaz-Piloneta1Francisco Ortega-Fernández2Gemma Marta Martinez-Huerta3José Valeriano Alvarez-Cabal4Project Engineering Department, University of Oviedo, 33004 Oviedo, SpainProject Engineering Department, University of Oviedo, 33004 Oviedo, SpainProject Engineering Department, University of Oviedo, 33004 Oviedo, SpainProject Engineering Department, University of Oviedo, 33004 Oviedo, SpainProject Engineering Department, University of Oviedo, 33004 Oviedo, SpainAtmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure.https://www.mdpi.com/1424-8220/25/13/4231corrosion monitoringatmospheric corrosionmachine learningchloride depositionlife assessmentpredictive modeling |
| spellingShingle | Marta Terrados-Cristos Marina Diaz-Piloneta Francisco Ortega-Fernández Gemma Marta Martinez-Huerta José Valeriano Alvarez-Cabal Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models Sensors corrosion monitoring atmospheric corrosion machine learning chloride deposition life assessment predictive modeling |
| title | Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models |
| title_full | Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models |
| title_fullStr | Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models |
| title_full_unstemmed | Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models |
| title_short | Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models |
| title_sort | corrosion risk assessment in coastal environments using machine learning based predictive models |
| topic | corrosion monitoring atmospheric corrosion machine learning chloride deposition life assessment predictive modeling |
| url | https://www.mdpi.com/1424-8220/25/13/4231 |
| work_keys_str_mv | AT martaterradoscristos corrosionriskassessmentincoastalenvironmentsusingmachinelearningbasedpredictivemodels AT marinadiazpiloneta corrosionriskassessmentincoastalenvironmentsusingmachinelearningbasedpredictivemodels AT franciscoortegafernandez corrosionriskassessmentincoastalenvironmentsusingmachinelearningbasedpredictivemodels AT gemmamartamartinezhuerta corrosionriskassessmentincoastalenvironmentsusingmachinelearningbasedpredictivemodels AT josevalerianoalvarezcabal corrosionriskassessmentincoastalenvironmentsusingmachinelearningbasedpredictivemodels |