The strength and consistency of cemented paste backfill (CPB) are of key concerns in the stope stability and cost control for underground mines. It is common practice to use additives, such as superplasticizer, to improve the performance of CPB. This study mainly focuses on the effects of superplasticizer on the hydration, consistency, and strength of CPB. In this study, a polynaphtalene sulfonate was used as the superplasticizer. The binder is a mix of 33.3% ordinary Portland cement and 66.7% fly ash. The CPB specimens with a tailings-binder ratio of 3:1 and a solid concentration of 70% were then tested by a low field nuclear magnetic resonance system after different hydration times. Effects of polynaphtalene sulfonate on the hydration, fluidity, and strength were investigated. Results showed that the polynaphtalene sulfonate has a strong influence on short-duration hydration, which may contribute to the strength increase of CPB. It has been demonstrated that the polynaphtalene sulfonate improved the fluidity of the CPB mixture. With the increased dosage of polynaphtalene sulfonate, the slump increased. It was also found that the polynaphtalene sulfonate dosage has a negligible effect on the 1 day (d) strength while it has a strengthening effect on the 7 d, 14 d, and 28 d strength of CPB specimens.

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.

The technique of breaking rocks using carbon dioxide phase transition technology is being widely applied in current research. This article combines theoretical and practical methods to analyze the mechanism by which high-pressure gas breaks rock at different stages. Using the observation that liquid carbon dioxide forms a high-pressure jet from release holes at the moment of release, a formula for calculating the initial pressure on the wall in the direction of release was obtained, and the pattern of initial crack formation on the borehole wall under different initial stress conditions was examined. An experiment using carbon dioxide phase transition technology to fracture rock without an initial stress field was conducted. The mechanism of generation and expansion of subsequent cracks under stress waves and high-pressure gas was analyzed, and the formula for calculating crack propagation radius under stress waves was obtained. The results suggested that under the quasi-static action of high-pressure gas, cracks begin to develop when the stress intensity factor K_I at the crack tip is equal to or greater than the fracture toughness K_IC of the rock.

For San-Xin gold and copper mine, deep blasting large block rate is high resulting in difficulty in transporting the ore out; secondary blasting not only increases blasting costs but is more likely to cause the top and bottom plate of the underground to become loose causing safety hazards. Based on the research background of Sanxin gold and copper mine, deep hole blasting parameters were determined by single-hole, variable-hole pitch, and oblique hole blasting tests, further using the inversion method to determine the optimal deep hole blasting parameters. Meanwhile, the PSO-BP neural network method was used to predict the block rate in deep hole blasting. The results of the study showed that the optimal minimum resistance line was 1.24–1.44 m, which was lower than 1.6–1.8 m in the original blasting design, which was one of the reasons for the higher blasting block rate. In addition, the PSO-BP deep hole blasting fragmentation prediction model predicts the block rate of the optimized blasting parameters and predicted a block rate of 6.83% after the optimization of hole network parameters. Its prediction accuracy is high, and the blasting parameter optimization can effectively reduce the block rate. It can reasonably reduce the rate of large pieces produced by blasting, improve blasting efficiency, and save blasting costs for enterprises. The result has wide applicability and can provide solutions for underground mines that also have problems with blasting large block rate.