Objective determination of over-excavation criterion in earth pressure balance shield tunnel boring machine operations using data-augmented machine learning

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.