An incremental data acquisition method for identifying tunnel rock mass classes based on vibration signals of open-type TBM cutterhead and gripper

To address the issues of weak generalization capability and difficult model iteration in rock mass class identification models for tunnel boring machine (TBM) tunneling, this study proposes an incremental data acquisition method for identifying tunnel rock mass classes. Based on the XE-III section of the second phase of the northern Xinjiang water supply project (YEGS project), a vibration monitoring system was deployed on the open-type TBM to collect vibration signals from the cutterhead and gripper, as well as TBM tunneling parameters. A database was constructed by integrating these data with the tunnel rock mass classes. Rock mass class identification models for the tunnel face and the gripper position were established using the XGBoost algorithm. The prediction results of the two models were matched according to the tunneling step, and the consistent results were automatically labeled as the rock mass classes for subsequent chainage, thereby achieving incremental data acquisition and iterative model updating. By introducing a manual review mechanism and relabeling the identification results of minority-class samples, the generalization capability and iterative performance of the model were further improved. This incremental data acquisition method can drive continuous model updating and enhance identification accuracy, thereby providing strong support for TBM intelligent tunneling.