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Abstract Automatic roadway formation by roof cutting is a sustainable nonpillar mining method that has the potential to increase coal recovery, reduce roadway excavation and improve mining safety. In this method, roof cutting is the key process for stress relief, which significantly affects the stability of the formed roadway. This paper presents a directionally single cracking (DSC) technique for roof cutting with considerations of rock properties. The mechanism of the DSC technique was investigated by explicit finite element analyses. The DSC technique and roof cutting parameters were evaluated by discrete element simulation and field experiment. On this basis, the optimized DSC technique was tested in the field. The results indicate that the DSC technique could effectively control the blast-induced stress distribution and crack propagation in the roof rock, thus, achieve directionally single cracking on the roadway roof. The DSC technique for roof cutting with optimized parameters could effectively reduce the deformation and improve the stability of the formed roadway. Field engineering application verified the feasibility and effectiveness of the evaluated DSC technique for roof cutting.

Abstract Gob‐side entry formation by roof cutting is a new technology for no pillar coal mining, which can maximize coal resources and reduce roadway drivage ratio. However, the mechanical behavior of the formed entry is complex while it is crucial to ensure the stability of the entry for mining safety. This paper proposed a machine learning‐based method for predicting the stability of the formed entry, which combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm or genetic optimization (GO) algorithm. The data set from 75 coal mining faces from 2009 to 2022 was employed to train and test the models. A descriptive variable of dynamic unstable distance was introduced to evaluate the stability state of the formed entry and six other parameters were chosen as influence parameters. The two intelligent models were compared with each other to have a comprehensive assessment. Model assessment indices such as R2, mean absolute error, mean absolute percentage error, and root mean square error were used to evaluate the accuracy of the models. The results of both developed models are promising, and the predictive accuracy of the PSO‐ANN model is higher than that of the GO‐ANN model. Through sensitivity analyses, it has been found that the coal seam thickness and roof rock hardness are the most important parameters for influencing entry stability. The developed method provides a practical tool for the prediction of entry stability and the optimization of entry design.

Abstract Automatic roadway formation by roof cutting (ARFRC) is a novel nonpillar mining method that has the potential to dramatically increase coal recovery while reducing the roadway excavation ratio. When this method is used below a fault influenced longwall goaf, large deformation and support failure occur in the roadway using conventional roadway formation techniques. In the study, the ARFRC method was tested in the Liliu mining area of China, which is characterized by goafs and faults. Field experiments and numerical modelling were used to evaluate the stability of the roadway by analysing the behaviour of overlying strata under the special geological condition. The results show that the surroundings of the formed roadway were greatly affected by the fault and the overlying coal pillar in the goaf. In the fault- and coal pillar-affected areas, the loads on the roadway roof increased by approximately 35% and 15%, respectively. According to the strata behaviour of the formed roadway surroundings, targeted support techniques for ARFRC were proposed, and the reliability of the support techniques were demonstrated by field practice.