Mapping knowledge structure and themes trends of corrosion detection of steel: A bibliometric analysis

• Bibliometric analysis using CiteSpace and VOSviewer reveals trends, research forces, and networks in corrosion detection. • The study shows how steel corrosion detection technologies are evolving toward intelligent and automated solutions. • Emphasizes integration of multiple disciplines driving innovative methods in corrosion detection technologies. • Provides a scientific basis for selecting cost-effective, efficient corrosion detection methods in engineering. Detecting steel corrosion is critical for maintaining the safety and longevity of key infrastructures, such as buildings and bridges. Corrosion weakens steel, reduces the lifespan of structures, and presents potential public safety risks, making its detection an essential area of research. This study employs bibliometric analysis to examine 1,484 papers indexed in the Web of Science from 2001 to 2024, providing insights into trends and emerging topics in steel corrosion detection. Key findings include: (1) A significant rise in publications since 2015, though there is still room for improvement in research quality; (2) Limited collaboration, with research often concentrated within individual organizations or small research groups, rather than across institutions; (3) A focus on corrosion detection methods based on electrochemical techniques, non-destructive testing, and image processing, with increasing emphasis on machine vision and deep learning in recent years. Future research should emphasize greater collaboration across institutions, international borders, and disciplines to foster innovative approaches. Advances in deep learning, IoT, and 5G are anticipated to drive the development of real-time, predictive corrosion monitoring systems, boosting automation and detection accuracy. This study provides a comprehensive review of current research, revealing key issues and trends in corrosion detection. The insights and recommendations aim to support scholars and engineers in advancing technological innovation and practical applications in this critical area.

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Institutions

Publication Details

Journal
Computational Materials Science
Published
2025-04-09
DOI
https://doi.org/10.1016/j.commatsci.2025.113889
Citations
1
Primary Topic
Concrete Corrosion and Durability
Type
article
Field-Weighted Citation Impact
0.95
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article

Mapping knowledge structure and themes trends of corrosion detection of steel: A bibliometric analysis

Jia Hou, Wei Chen, Yating Zhu, Shaojin Hao et al.
1 citations
Computational Materials Science
Concrete Corrosion and Durability
0.95
article

Mapping knowledge structure and themes trends of corrosion detection of steel: A bibliometric analysis

Jia Hou, Wei Chen, Yating Zhu, Shaojin Hao, Mingyu Yu
article en
1 citations

Abstract

• Bibliometric analysis using CiteSpace and VOSviewer reveals trends, research forces, and networks in corrosion detection. • The study shows how steel corrosion detection technologies are evolving toward intelligent and automated solutions. • Emphasizes integration of multiple disciplines driving innovative methods in corrosion detection technologies. • Provides a scientific basis for selecting cost-effective, efficient corrosion detection methods in engineering. Detecting steel corrosion is critical for maintaining the safety and longevity of key infrastructures, such as buildings and bridges. Corrosion weakens steel, reduces the lifespan of structures, and presents potential public safety risks, making its detection an essential area of research. This study employs bibliometric analysis to examine 1,484 papers indexed in the Web of Science from 2001 to 2024, providing insights into trends and emerging topics in steel corrosion detection. Key findings include: (1) A significant rise in publications since 2015, though there is still room for improvement in research quality; (2) Limited collaboration, with research often concentrated within individual organizations or small research groups, rather than across institutions; (3) A focus on corrosion detection methods based on electrochemical techniques, non-destructive testing, and image processing, with increasing emphasis on machine vision and deep learning in recent years. Future research should emphasize greater collaboration across institutions, international borders, and disciplines to foster innovative approaches. Advances in deep learning, IoT, and 5G are anticipated to drive the development of real-time, predictive corrosion monitoring systems, boosting automation and detection accuracy. This study provides a comprehensive review of current research, revealing key issues and trends in corrosion detection. The insights and recommendations aim to support scholars and engineers in advancing technological innovation and practical applications in this critical area.

Computational Materials ScienceVol. 253
Wuhan University of Technology (CN), China National Chemical Engineering (China) (CN), China State Construction Engineering (China) (CN), Sanya University (CN)
Openalex Percentile: Top 27%
Concrete Corrosion and Durability
0.95
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