Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis
Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8419 |
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| author | Barbara Szymanik Maja Kocoń Sam Ang Keo Franck Brachelet Didier Defer |
| author_facet | Barbara Szymanik Maja Kocoń Sam Ang Keo Franck Brachelet Didier Defer |
| author_sort | Barbara Szymanik |
| collection | DOAJ |
| description | Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. |
| format | Article |
| id | doaj-art-57d2d627499f40b0ae81e812b94f9a75 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-57d2d627499f40b0ae81e812b94f9a752025-08-20T04:00:54ZengMDPI AGApplied Sciences2076-34172025-07-011515841910.3390/app15158419Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based AnalysisBarbara Szymanik0Maja Kocoń1Sam Ang Keo2Franck Brachelet3Didier Defer4Faculty of Electrical Engineering, West Pomeranian University of Technology, 70-310 Szczecin, PolandFaculty of Electrical Engineering, West Pomeranian University of Technology, 70-310 Szczecin, PolandL’Institut de Recherche de la Construction, ESTP, 28 Av. du Président Wilson, 94234 Cachan Cedex, FranceUniversity of Artois, IMT Nord Europe, Junia, University of Lille, ULR 4515, Laboratoire de Génie Civil et géo-Environnement (LGCgE), 62400 Béthune, FranceUniversity of Artois, IMT Nord Europe, Junia, University of Lille, ULR 4515, Laboratoire de Génie Civil et géo-Environnement (LGCgE), 62400 Béthune, FranceNon-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure.https://www.mdpi.com/2076-3417/15/15/8419microwave thermographyreinforced concretenon-destructive testingdeep learningrebar localization |
| spellingShingle | Barbara Szymanik Maja Kocoń Sam Ang Keo Franck Brachelet Didier Defer Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis Applied Sciences microwave thermography reinforced concrete non-destructive testing deep learning rebar localization |
| title | Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis |
| title_full | Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis |
| title_fullStr | Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis |
| title_full_unstemmed | Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis |
| title_short | Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis |
| title_sort | detection of steel reinforcement in concrete using active microwave thermography and neural network based analysis |
| topic | microwave thermography reinforced concrete non-destructive testing deep learning rebar localization |
| url | https://www.mdpi.com/2076-3417/15/15/8419 |
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