Prediction for rockburst proneness using energy characteristics during rock cutting
Source abstract
Abstract
This study addresses the challenge of predicting rockburst proneness in deep engineering rock mass exploration. It proposes an energy analysis model and a comprehensive criterion system based on digital cutting tests. An energy analysis model for rock cutting has been established through systematically conducting digital drilling tests under different drill bit diameters (8, 10, and 12 mm) and confined pressure conditions (10, 20, 30, and 40 MPa). This model incorporates the specific energy (SE) index, which comprehensively accounts for rock compressive strength, hardness, and fracture development. A rockburst proneness index (BI) for rocks has been constructed based on the principle of energy conservation. The ratio of dissipated energy to break-rock energy characterizes the rockburst proneness. The study reveals that increasing drill bit diameter and confined pressure results in a rise in the SE index, with granite showing the highest SE index and sandstone the lowest. Frictional energy consumption exceeds liquid energy consumption during the cutting process. The harder rocks display higher proportions of frictional energy. Rockburst proneness decreases with increasing drill bit diameter and confined pressure. This effect is most pronounced under small-diameter drill bits (8 mm) and low confining pressure (10 MPa). The proposed BI index demonstrates good linear correlation with existing indices such as BU-I and B3. An accuracy of 95% is achieved in predicting rockburst proneness, with the calculation effectively accounting for both frictional effects and drilling-fluid influences. This can provide reliable theoretical foundations and technical support for optimizing drilling parameters, identifying rock types, and evaluating rockburst proneness.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Drilling and Well Engineering · Rock Mechanics and Modeling · Tunneling and Rock Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CCCC First Highway Consultants Co. Ltd. 1 , Xi’an, Shaanxi,
CCCC Rail Transit Branch 2 , Xi’an, Shaanxi,
Changsha University of Science and Technology 3 , Changsha,
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