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Advanced Sensor Technologies for Corrosion Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 640

Special Issue Editors

College of New Energy, China University of Petroleum (East China), Qingdao 266580, China
Interests: light alloy corrosion and surface treatment; electrochemical evaluation of anti-corrosion coating effects; surface interface physics and chemistry; surface-enhanced Raman spectroscopy analysis; Raman–electrochemical in situ testing
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Guest Editor
School of Electrical Engineering, Shandong University, Jinan 250061, China
Interests: equipment sensing and monitoring technology; electrochemical energy storage materials; membrane science; the diagnosis of high voltage equipment insulation

Special Issue Information

Dear Colleagues,

Corrosion is the main reason for the performance degradation of metal components in natural environments; this can affect the durability of component structures and shorten their service life. Severe cases can lead to component failure and cause safety accidents. Early detection and diagnosis alongside effective prevention and control measures are important components of corrosion protection for metal components. Corrosion monitoring technology can provide a basis from which to evaluate the rationality of material selection, the effectiveness of protective measures, and environmental conditions.

Corrosion monitoring technology can provide corrosion information at any time without manual testing, ensuring the consistency and reliability of test results. Installing sensors and monitoring instruments on key structures to monitor the entire process of corrosion in key parts/structures of equipment is beneficial for the real-time prevention and control of corrosion, and can also provide more data support for equipment life extension.

Dr. Jiantao Qi
Prof. Dr. Xiaolong Wang
Guest Editors

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Keywords

  • corrosion
  • monitor
  • life assessment
  • sensors

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Published Papers (1 paper)

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Research

18 pages, 1123 KiB  
Article
Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models
by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta and José Valeriano Alvarez-Cabal
Sensors 2025, 25(13), 4231; https://doi.org/10.3390/s25134231 - 7 Jul 2025
Viewed by 346
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Corrosion Monitoring)
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