Authors
Kibeom Kwon, Young Jin Shin, Jaehoon Jung, Byeonghyun Hwang, Hangseok Choi
Published in
Scientific reports. Jul 12, 2026. Epub Jul 12, 2026.
Abstract
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 (COE). 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 COE 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 COE 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 COE 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 COE identified in this study, the proposed framework provides a systematic and transferable approach for determining site-specific COE values in different tunnelling projects.
PMID:
42437784
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.
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