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

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.