Energy-absorbing rockbolt is now recognized as an important support element in deep mines around the world. A novel kind of energy-absorbing bolt called J-bolt was developed, which consists of anchoring (damping) module, deformation module, blending module and thread segment. The static pulling loading model and dynamic impact constitutive model of J-bolt were established. The experiment results show: (1) the static pulling load is greater than 190 kN; (2) under multiple dynamic impact loading condition, the peak load of J-bolt exceeds 250 kN each time. Under the given initial dynamic impact loading condition, with the increasing of damping ratio, the oscillation amplitude of J-bolt attenuates quickly along the bolt axial impact direction. The greater the damping effect of J-bolt, the more quickly releasing of the dynamic impact energy. The absorbed energy of J-bolt exceeds 19.3 kJ under each dynamic impact, the accumulated energy absorbing capacity reaches 46.5 kJ. Due to the objective existence of joints in rock mass, the bolted rock mass can generate dilatation and tensile failure in the joints surface of rock mass under dynamic impact condition, but the energy-absorbing support system will keep surfaces of tunnels (stopes) stable, which can effectively absorb the kinetic energy induced by dynamic hazards.

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r² of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

This study presents stability analyses and a cable bolt support design for a typical deep large-span stope (1-1# stope) at the Hongtoushan mine in China, using an integrated empirical and numerical method. Detailed field work including quantification of joint distribution and surface quality, along with laboratory test on intact rock samples, were performed to obtain the geotechnical properties of rock masses. The rock mass of the 1-1# stope was characterized by rock mass rating (RMR), rock mass quality (Q), and geological strength index (GSI), and then the modulus, peak strength, cohesive strength, and internal friction angle of the rock mass were estimated. The stability of the stope was then evaluated by empirical (RMR, stability graph) and numerical approaches (limit equilibrium analyses with UNWEDGE and stress-strain analyses with FLAC3D), considering of the effects of rock mass quality, induced stress, and large-span. A cable bolt support system obtained from the empirical method, was then further analyzed using the FLAC3D and UNWEDGE codes. The results show that the maximum plastic zone thickness and vertical displacement at the stope roof decrease significantly and the safety factor of the unstable wedge block increases significantly after installing the cable bolt support systems recommended by the empirical method. Therefore, it is suggested that an integrated empirical and numerical method is used to obtain quantitative stability assessment and optimum cable bolt support design for deep large-span stope roofs.

Deep mining has become an inevitable trend of mining development. Previously conducted studies have established that reasonable stope structure parameters are the premise to ensure the safe and efficient production of deep mines. In order to ensure the safety of deep mining, in this paper, we systematically review the existing stope structure parameter design methods, and then put forward a deep stope structure design method based on the stability of mining rock mass. Based on rock mass quality classification, this method uses a critical span graph and an improved stability graph, and fully considers the influence of joint occurrence and mining stress on the stability of surrounding rock, to design the stope structural parameters. Taking into consideration the deterioration of the quality of deep rock mass, we collect mining data at home and abroad, improve the stability graph, and make it suitable for the design of stope structural parameters with different mining methods. The design process of stope structural parameters is expounded through field engineering cases, and it has specific guiding significance for the design of stope structural parameters in deep metal mines.