Data-augmented machine learning approach for determination of over-excavation criteria in earth pressure balance shield tunnel boring machine operations
Rattachement africain : kr, jp. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Regulating the discharged muck volume is essential for preventing over-excavation in projects constructed by tunnel boring machines (TBMs). Over-excavation is typically identified when the over-excavation ratio (OER) exceeds a predefined criterion for over-excavation (C OE ). However, this criterion has traditionally been determined subjectively, and the site and operational conditions associated with anomalous over-excavation have not been systematically characterized. This study proposes a data-driven approach to objectively determine the optimal C OE and to identify underlying anomalous conditions. Machine learning models, enhanced through data augmentation techniques, were developed to classify normal and over-excavation cases. An optimal C OE of 1.15 was identified through an analysis of predictive performance and data patterns. The optimal model successfully identified 86.4% of over-excavation cases. The validity of the proposed C OE was further confirmed by examining OER values under normal and abnormal over-excavation, including actual collapse events. Model interpretation revealed that elevated torque, particularly in deep, weathered ground with high water pressure, contributed to over-excavation. Beyond the specific C OE identified in this study, the proposed framework provides a systematic and transferable approach for determining site-specific C OE values in different tunnelling projects.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Data-augmented machine learning approach for determination of over-excavation criteria in earth pressure balance shield tunnel boring machine operations
- Date Crossref
- 12/07/2026
- Éditeur
- Springer Science and Business Media LLC
- 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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Advanced Institute of Convergence Technology pays non établi dans la noticeStructure de recherche
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University of Seoul pays non établi dans la noticeUniversité ou école supérieure
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Korea University Department of Civil pays non établi dans la noticeUniversité ou école supérieure
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Research Center for Disaster & Safety pays non établi dans la noticeStructure de recherche
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HMG Construction R&D Division pays non établi dans la noticeInstitution
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School of Civil pays non établi dans la noticeUniversité ou école supérieure
Advanced Institute of Convergence Technology, University of Seoul et Department of Civil — Korea University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.