A data driven real-time perception method of rock condition in TBM construction

Abstract

In tunnel boring machine (TBM) construction, the presence of collapsible rock mass (CRM) can lead to accidents such as collapse and jamming. This study presents a novel CRM early warning strategy based on real-time TBM rock fragmentation data to improve safety and efficiency in CRM conditions. The strategy includes a qualitative classification model and a quantitative probability model for CRM identification. The results indicate that the distribution dissimilarity index β effectively reflect the significance of variables across CRM and non-CRM datasets. Various parameters, including TPI, FPI, WR, and AF, show discriminatory ability between CRM and non-CRM samples. In particular, the CRM-weighted index, which combines the strengths of the individual indices, achieves a distributional dissimilarity index of 1.05, significantly higher than any of the individual indices. The qualitative classification model proves effective in identifying samples from collapse areas, demonstrating ability to identify samples located in adverse geological condition. The quantitative model shows that the probability of CRM is generally higher in adverse geological area samples, particularly in zones where collapse has occurred, with a CRM probability is approaching 1. The proposed strategy provides accurate early warnings to prevent collapse accidents and represents a practical approach to improving the safety and efficiency.