Automatic Defect Detection for Concrete Bridge Decks Using Geometric Feature Augmentation and Robust Point Cloud Learning Strategy
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Le résumé fourni par la source
Surface defects such as depressions, heaving, and irregular undulations frequently develop on aging concrete bridge decks under repeated traffic loading and environmental effects. Accurate and objective identification of such defects is essential for structural serviceability and safety, yet manual inspection remains labor-intensive and subjective. This study develops a systematic framework for surface defect identification through geometric feature augmentation with a streamlined point cloud learning strategy. In practical engineering scenarios, point cloud data of concrete bridge decks can be periodically acquired via vehicle-mounted mobile laser scanning (MLS) systems and subsequently streamlined for analysis. The proposed method heightens defect sensitivity by extracting interpretable geometric descriptors, further integrating multi-scale representations to capture surface defects across varying spatial extents. Evaluated on a public point-level annotated benchmark, the proposed method clearly outperforms the same network trained with geometric coordinates only. To improve result reliability, all experiments were repeated four times with different random seeds, and the performance is reported as mean ± standard deviation. Results show that the proposed method achieves a precision of 0.597 ± 0.021 and an accuracy of 0.933 ± 0.009 under the benchmark protocol. Overall, these results demonstrate a reproducible proof of concept under controlled benchmark conditions for bridge deck surface defect segmentation, while broader cross-site and cross-sensor validation will be pursued in future work.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Automatic Defect Detection for Concrete Bridge Decks Using Geometric Feature Augmentation and Robust Point Cloud Learning Strategy
- Date Crossref
- 09/03/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Beijing University of Technology pays non établi dans la noticeUniversité ou école supérieure
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Chongqing University of Technology pays non établi dans la noticeUniversité ou école supérieure
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Beijing University of Civil Engineering and Architecture pays non établi dans la noticeUniversité ou école supérieure
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Beijing Building Construction Research Institute (China) pays non établi dans la noticeEntreprise
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College of Architecture and Civil Engineering pays non établi dans la noticeUniversité ou école supérieure
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College of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Ltd. Beijing Yuedu Construction Engineering Co. pays non établi dans la noticeEntreprise
Beijing University of Technology, Chongqing University of Technology et Beijing University of Civil Engineering and Architecture, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.