Abstract
This study aims to develop a novel AI-driven approach for detecting and evaluating corrosion in reinforced concrete (RC) structures, answering one of the most significant challenges in the construction industry. The research aims to overcome the difficulties of identifying corrosion with limited data. Gathering representative learning databases is challenging due to problems obtaining adequate samples and the high diversity in rebar, concrete, and structural parameters. The research quantitatively analyzes measurements obtained through Magnetic Force Induced Vibration Evaluation (M5), a nondestructive testing (NDT) method. The process is enhanced by employing the specialized Association Rules Analysis (ARA) with a dedicated feature extraction technique. The findings suggest that utilizing a variety of patterns and features enhances the method's identification effectiveness.
Paper Type
Poster
DOI
10.62036/ISD.2025.46
Enhancing the Identification of Corrosion in Reinforced Concrete Structures Using Association Rules Analysis and the Non-Destructive M5 Method
This study aims to develop a novel AI-driven approach for detecting and evaluating corrosion in reinforced concrete (RC) structures, answering one of the most significant challenges in the construction industry. The research aims to overcome the difficulties of identifying corrosion with limited data. Gathering representative learning databases is challenging due to problems obtaining adequate samples and the high diversity in rebar, concrete, and structural parameters. The research quantitatively analyzes measurements obtained through Magnetic Force Induced Vibration Evaluation (M5), a nondestructive testing (NDT) method. The process is enhanced by employing the specialized Association Rules Analysis (ARA) with a dedicated feature extraction technique. The findings suggest that utilizing a variety of patterns and features enhances the method's identification effectiveness.
Recommended Citation
Frankowski, P.K., Majzner, P., Mąka, M. & Chady, T. (2025). Enhancing the Identification of Corrosion in Reinforced Concrete Structures Using Association Rules Analysis and the Non-Destructive M5 MethodIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.46