Abstract:

To explore the approach for dealing with roof fall by the open TBM in coal mine excavation, the mechanism and pattern recognition of roof fall were investigated considering the unfavorable geological conditions such as abundant water, faults, joints and sandstone with fractured structure. Firstly, the roof fall mechanism was analyzed by utilizing the modified excavation compensation theory and the minimum support stress of surrounding rock which fully considered the intermediate principal stress. Based on the successful case of the main inclined shaft of Kekegai mine and TBM site construction data, the characteristics of roof fall were deeply analyzed. Then, in accordance with the collected on-site feedback monitoring information, the variations of excavation parameters before and after the roof fall were systematically examined, and the machine learning models of random forest (RF), back propagation (BP) neural network, and Library for support vector machines (LIBSVM) were constructed to effectively identify the roof fall. The results demonstrate that the internal cause of roof fall is the deterioration of sandstone mechanical properties resulting from water-rock interlace in the cataclastic structure, the external cause is the energy release by mechanic-rock action, and the controlling cause is the excavation stress compensation and the timely application of steel anchor (cable) shotcrete + steel arch (steel plate belt). The sharp increase of penetration, and thrust of the hob, the torque of the cutter head and sharp decline of the cutter head speed are the characteristics of roof fall driving parameters. The RF model has the highest prediction accuracy for the classification of roof fall of surrounding rock, and its accuracy rate of identifying roof fall risk is 1.78% and 11.84% higher than that of BP and LIBSVM, respectively.

Key words: main inclined shaft, tunnel boring machine (TBM), roof falling mechanism, intermediate principal stress, driving parameters, risk of roof fall