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The roadway instability in deep underground conditions has attracted constant concerns in recent years, as it seriously affects the efficiency of coal mining and the safety of personnel. The large rheological deformations normally occur in deep roadway with soft coal mass. However, the failure mechanism of such roadways is still not clear. In this study, based on a typical soft coal roadway in the field, the in-situ measurements and rock mass properties were obtained. The rheological deformation of that roadway was revealed. Then a time-dependent 3D numerical model was established and verified. Based on the verified model, the deformation properties and evolutionary failure mechanism of deep coal roadway were investigated in detail. The results showed that the deformation of the soft coal roadway demonstrated rheological behavior and the applied support structures failed completely. After roadway excavation, the maximum and minimum stresses around the roadway deteriorated gradually with the increase of time. The failure zones in soft coal mass expanded increasingly over time, which had a negative effect on roadway stability in the long-term. According to the findings, helpful suggestions were further presented to control the rheological deformation in the roadway. This research systematically reveals the instability mechanism of the deep coal roadway and provides some strategies for maintaining roadway stability, which can significantly promote the sustainability of mining in deep underground coal mines.

Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining.

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