Abstract
This work presents an intelligent support system for a novel, non-destructive (NDT), 2D method to identify parameters of reinforced concrete (RC) structures. Using association rule analysis (ARA), it detects relationships between signal changes and structure parameter modifications, identifying signal parameters influenced by a single structural parameter. Multitask learning is used to identify concrete cover thickness, reinforcing bar diameter, and steel class. Features are extracted from the three spatial components of magnetic induction via ACO decomposition, which is suited for creating complex databases. Genetic algorithms improve noise resilience in function approximation. Results are shown as Fuzzy Rough Sets. Three vertically placed sensors, combined with AI, enable precise identification of parameters, with changes in one not affecting others.
Paper Type
Poster
DOI
10.62036/ISD.2025.59
Hybrid AI Framework Based on Fuzzy Rough Sets for Two-Dimensional Magnetic Evaluation of Reinforced Concrete Structures
This work presents an intelligent support system for a novel, non-destructive (NDT), 2D method to identify parameters of reinforced concrete (RC) structures. Using association rule analysis (ARA), it detects relationships between signal changes and structure parameter modifications, identifying signal parameters influenced by a single structural parameter. Multitask learning is used to identify concrete cover thickness, reinforcing bar diameter, and steel class. Features are extracted from the three spatial components of magnetic induction via ACO decomposition, which is suited for creating complex databases. Genetic algorithms improve noise resilience in function approximation. Results are shown as Fuzzy Rough Sets. Three vertically placed sensors, combined with AI, enable precise identification of parameters, with changes in one not affecting others.
Recommended Citation
Frankowski, P.K., Majzner, P., Czapliński, W. & Stawicki, T. (2025). Hybrid AI Framework Based on Fuzzy Rough Sets for Two-Dimensional Magnetic Evaluation of Reinforced Concrete StructuresIn 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.2025.59