Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete
Concrete structures play a vital role in civil engineering. However, their deterioration poses a significant safety risk, making regular inspection essential. The impact method detects defects by analyzing characteristic reaction forces generated when striking areas with internal cavities, offering...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10857282/ |
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| author | Koki Shoda Jun Younes Louhi Kasahara Qi An Atsushi Yamashita |
| author_facet | Koki Shoda Jun Younes Louhi Kasahara Qi An Atsushi Yamashita |
| author_sort | Koki Shoda |
| collection | DOAJ |
| description | Concrete structures play a vital role in civil engineering. However, their deterioration poses a significant safety risk, making regular inspection essential. The impact method detects defects by analyzing characteristic reaction forces generated when striking areas with internal cavities, offering the advantage of being unaffected by acoustic noise and not requiring direct sensor contact. Traditional approaches, relying solely on reaction force signals, have struggled with low defect detection accuracy due to the similarity of signals between healthy and defective concrete and limited data quantity. To address this challenge, we propose a novel method that enhances defect discrimination accuracy by integrating statistical processing and physical property analysis within a machine learning framework designed for force signal characteristics. Our method employs wavelet transformation to convert short-duration force signals into high-resolution time-frequency features, capturing their non-stationary behavior in detail. For dimensionality reduction, we use Uniform Manifold Approximation and Projection to accurately embed data clusters near decision boundaries in a low-dimensional space. The embedded data is then clustered using Fuzzy c-means, and defect cluster identification is automated based on the apparent stiffness of the concrete. Experimental validation through field and laboratory tests confirmed the effectiveness of our method, demonstrating a significant improvement in defect detection accuracy. By advancing the precision and automation of the impact method, this study contributes a valuable tool for enhancing the safety and maintenance of concrete structures. |
| format | Article |
| id | doaj-art-85a33cabd405410cb21614b7d3183aca |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-85a33cabd405410cb21614b7d3183aca2025-02-12T00:02:05ZengIEEEIEEE Access2169-35362025-01-0113253252533810.1109/ACCESS.2025.353592710857282Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of ConcreteKoki Shoda0https://orcid.org/0009-0001-7668-8548Jun Younes Louhi Kasahara1https://orcid.org/0000-0002-5924-8858Qi An2https://orcid.org/0000-0001-7641-2632Atsushi Yamashita3https://orcid.org/0000-0003-1280-069XDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa Campus, Chiba, JapanDepartment of Precision Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, JapanDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa Campus, Chiba, JapanDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa Campus, Chiba, JapanConcrete structures play a vital role in civil engineering. However, their deterioration poses a significant safety risk, making regular inspection essential. The impact method detects defects by analyzing characteristic reaction forces generated when striking areas with internal cavities, offering the advantage of being unaffected by acoustic noise and not requiring direct sensor contact. Traditional approaches, relying solely on reaction force signals, have struggled with low defect detection accuracy due to the similarity of signals between healthy and defective concrete and limited data quantity. To address this challenge, we propose a novel method that enhances defect discrimination accuracy by integrating statistical processing and physical property analysis within a machine learning framework designed for force signal characteristics. Our method employs wavelet transformation to convert short-duration force signals into high-resolution time-frequency features, capturing their non-stationary behavior in detail. For dimensionality reduction, we use Uniform Manifold Approximation and Projection to accurately embed data clusters near decision boundaries in a low-dimensional space. The embedded data is then clustered using Fuzzy c-means, and defect cluster identification is automated based on the apparent stiffness of the concrete. Experimental validation through field and laboratory tests confirmed the effectiveness of our method, demonstrating a significant improvement in defect detection accuracy. By advancing the precision and automation of the impact method, this study contributes a valuable tool for enhancing the safety and maintenance of concrete structures.https://ieeexplore.ieee.org/document/10857282/Non-destructive testinghammering teststructural health monitoringconcrete defect detectionmachine learningrobustness against acoustic noise |
| spellingShingle | Koki Shoda Jun Younes Louhi Kasahara Qi An Atsushi Yamashita Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete IEEE Access Non-destructive testing hammering test structural health monitoring concrete defect detection machine learning robustness against acoustic noise |
| title | Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete |
| title_full | Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete |
| title_fullStr | Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete |
| title_full_unstemmed | Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete |
| title_short | Defect Detection in Concrete Structures Based on Characteristics of Hammer Reaction Force and Apparent Stiffness of Concrete |
| title_sort | defect detection in concrete structures based on characteristics of hammer reaction force and apparent stiffness of concrete |
| topic | Non-destructive testing hammering test structural health monitoring concrete defect detection machine learning robustness against acoustic noise |
| url | https://ieeexplore.ieee.org/document/10857282/ |
| work_keys_str_mv | AT kokishoda defectdetectioninconcretestructuresbasedoncharacteristicsofhammerreactionforceandapparentstiffnessofconcrete AT junyouneslouhikasahara defectdetectioninconcretestructuresbasedoncharacteristicsofhammerreactionforceandapparentstiffnessofconcrete AT qian defectdetectioninconcretestructuresbasedoncharacteristicsofhammerreactionforceandapparentstiffnessofconcrete AT atsushiyamashita defectdetectioninconcretestructuresbasedoncharacteristicsofhammerreactionforceandapparentstiffnessofconcrete |