The Brazilian disc test is a popular tensile strength test method for engineering materials. The fracture behavior of specimens in the Brazilian disc test is closely related to the validity of the test results. In this paper, the fracture process of granite discs under different loading configurations is simulated by using a coupled finite–discrete element method. The results show that the maximum tensile stress value is located within 18 mm (0.7 times the disc radius) of the vertical range of the disc center under different loading configurations. In small diameter rods loading, the invalid tensile strength is obtained because the crack initiation and plastic strain is at the end of the disc. The crack initiation points of flat platen loading and curved jaws loading are all within the center of the disc, and the valid tensile strength can be obtained. The tensile strength test results under different loading configurations show that the error of small diameter rods loading is 13%, while the errors of flat platen loading and curved jaws loading are both 1%. The curved jaws loading is the most suitable for measuring the tensile strength of brittle materials such as rock, followed by flat platen loading. The small diameter rods loading is not recommended for the Brazilian test.

The occurrence of rockburst can seriously impact the construction and production of deep underground engineering. To prevent rockburst, machine learning (ML) models have been widely employed to predict rockburst based on some related variables. However, due to the costs and complicated geological conditions, complete datasets to evaluate rockburst cannot always be obtained in rock engineering. To fill this limitation, this study proposed an ensemble tree model suitable for incomplete datasets, i.e., the histogram gradient boosting tree (HGBT), to build intelligent models for rockburst prediction. Three hundred fourteen rockburst cases were employed to develop the HGBT model. The hunger game search (HGS) algorithm was implemented to optimize the HGBT model. The established HGBT model had an excellent testing performance (accuracy of 88.9%). An incomplete database with missing values was applied to compare the performances of HGBT and other ML models (random forest, artificial neural network, and so on). HGBT received an accuracy of 78.8% in the incomplete database, and its capacity was better than that of other ML models. Additionally, the importance of input variables in the HGBT model was analyzed. Finally, the feasibility of the HGBT model was validated by rockburst cases from Sanshandao Gold Mine, China.

Strengthening the base-angle of a roadway can have a beneficial impact on its overall stability, but the specific optimal parameter selection range is still unclear. Fast Lagrangian Analysis of Continua 3D (FLAC3D) software is used to carry out the stability analysis of 5 kinds of roadway models with different section shapes under the conditions of no support and different base-angle bolt support angles, and the simulation verification is carried out under the actual working conditions of the Sanshandao Gold Mine. The conclusion is as follows: without support, the self-stability of a tri-centric arch roadway is the best, and that of a rectangular roadway is the worst. When the base-angle bolt dip angle is between 15° and 45°, a better supporting effect can be obtained under the established 5 kinds of roadway sections. In the straight wall circular arch roadway of the Sanshandao Gold Mine, the roadway stability is the best when the angle of the base-angle bolt is 15°. However, changing the length of the base-angle bolt, even if the length of the base-angle bolt is increased to a certain extent, will decrease the overall supporting effect of the supporting structure. This paper can provide a reference for the selection of tunnel bottom corner bolt support parameters.