Scholarlink

<p>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<sub>OE</sub>). 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<sub>OE</sub> 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<sub>OE</sub> 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<sub>OE</sub> 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<sub>OE</sub> identified in this study, the proposed framework provides a systematic and transferable approach for determining site-specific C<sub>OE</sub> values in different tunnelling projects.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Data-augmented machine learning approach for determination of over-excavation criteria in earth pressure balance shield tunnel boring machine operations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月12日

查看全文
  • Kibeom Kwon,
  • Young Jin Shin,
  • Jaehoon Jung,
  • Byeonghyun Hwang,
  • Hangseok Choi

摘要

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.