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Fractures have significant impact on the stability of underground projects such as coal mining. The identification of geological features based on drilling parameters is a promising option for intelligent detection of rock formations. A series of studies conducted to identify fractures under unstable drilling conditions are presented in this paper. First, the forces acting around the drill bit are analyzed to further improve the force model of the two-wing PDC drill bit. Then, based on the self-developed borehole drilling device, the drilling parameters are collected in real time while drilling in concrete of different strengths and different fracture widths. Finally, the response characteristics of drilling parameters are analyzed, and the multi-parameter voting method is proposed for fracture identification. The experimental results show that when fracture width increases, the crushing area, which forms around the borehole when the drill bit encounters the fracture, tends to increase. The rate of penetration (ROP) increases suddenly, the revolution per minute (RPM) decreases, and the torque increases at fracture encounter. The sudden changes are recovered after passing the fracture. The multi-parameter voting method has a high recognition rate for fractures of width greater than 2 mm, but a relatively low recognition rate for fractures of 1 mm width. In addition to detecting fractures, the method performs well in predicting fracture location and fracture width. Highlights: The response characteristics of multiple drilling parameters at the fracture were analyzed. As the thrust (torque) increases, the amount of change in penetration rate caused by the unit change in thrust (torque) decreases. Results show that the proposed multi-parameter voting method has a high recognition rate for fractures with width greater than 2 mm. [ABSTRACT FROM AUTHOR]

While large-scale rock strata affected by underground coal mining have been widely studied through numerical modeling, there are still some aspects that can be better understood. Specifically, researchers do not fully utilize borehole logs and test results of rock specimens to reveal rock mass property variation along horizontal directions and strata lateral thickness variation at bed level. In this paper, we address this knowledge gap by proposing a data-intensive numerical modeling method (DINMM) that can make full use of these data with consideration of the modeling limitations of BlockRanger, a grid generation tool. Both the proposed method and the conventional numerical modeling method (CNMM) are applied to the Ying-Pan-Hao coal mine via the FLAC3D (Fast Lagrangian Analysis of a Continua in 3 Dimensions) package, and their predictions are then calibrated and compared to discuss the validity. Results show that, compared to the CNMM-based predictions, the root mean square error of 70 monitoring points is decreased at least by 27.4% in the DINMM-based prediction, and the relative error of maximum subsidence is reduced by 5.1% with a reduction rate of 66.5% on average, even though the CNMM-based model was originally better calibrated. We also find that a DINMM-based model is more in line with field observations and theoretical understanding in terms of displacement, stress, and failure propagation. The notion of data-intensive modeling seems to be quite promising and the DINMM should be useful for a better understanding of strata movement and subsidence prediction. Highlights: • We propose a data-intensive modeling method (DINMM) to build numerical models based on multiple borehole logs, rather than on a single or generalized borehole log. • We realized rock mass property variation along horizontal directions and strata lateral thickness variation at bed level when modeling large-scale rock strata. • We find that the root mean square error of 70 monitoring points is decreased at least by 27.4% in the DINMM-based prediction, even though the control model was originally better calibrated. [ABSTRACT FROM AUTHOR]