Rockburst is a common and huge hazard in underground engineering, and the scientific prediction of rockburst disasters can reduce the risks caused by rockburst. At present, developing an accurate and reliable rockburst risk prediction model remains a great challenge due to the difficulty of integrating fusion algorithms to complement each other’s strengths. In order to effectively predict rockburst risk, firstly, 410 sets of valid rockburst data were collected as the original data set in this paper, which was used to process these rockburst cases by the SMOTE oversampling method. Then, four integrated algorithms and eight basic algorithms were selected, which were optimized by hyperparameters and five-fold cross-validation and combined with the random search grid method, thus improving the classification performance of these algorithms. Third, the stacking integration algorithm, which was combined with the principles of various machine learning algorithms and the characteristics of the rockburst cases, integrated the optimization of rockburst algorithms with reference to four combinatorial strategies. Further, we adopted the voting integration algorithm, chose multiple combination schemes, and referred to the weighted fusion of accuracy, F1 score, recall, precision, and cv-mean as the weight values, and the optimal model for rockburst risk prediction was obtained. Finally, using the 35 generated stacking integration algorithms and 18 voting integration algorithms, the optimal model in the fusion strategy was selected and the traditional integration algorithm model was analyzed on the basis of different sample combinations of the models. The results showed that the prediction performance of stacking and voting integration algorithms was mostly better than the ordinary machine-learning performance, and the selection of appropriate fusion strategies could effectively improve the performance of rockburst prediction for ensemble learning algorithms.

To study the occurrence characteristics of hazardous chemical accidents in China, mathematical analysis methods were used to study hazardous chemical fatalities (HCFs) in recent years. This research focused on selecting seven accident characteristics including time characteristics, geographical characteristics, accident type characteristics, hazardous chemical types, hazardous chemical production links, accident cause characteristics, and accident classification. The research results show that: (1) the occurrence of HCFs has obvious time-domain and regional characteristics, the number of casualties on Wednesday working days is the largest, 9:00–11:00 (inclusive) and 15:00–16:00 (inclusive) in a day are the two time periods with the largest number of incidents, the number of hazardous chemical accidents and deaths in economically developed coastal provinces is relatively high; (2) Analyze according to the type of accident statistics, the number of accidents and deaths caused by explosions, poisoning, asphyxiation, and fire are the largest; (3) Analyze according to the type of hazardous chemicals, drugs, compressed gas and liquefied gas, flammable solids, and spontaneous combustion materials, as well as flammable materials when wet are the types of hazardous chemicals that cause the most casualties; (4) Analyze according to the type of hazardous chemical accidents, the number of accidents and deaths caused in the production process is the largest; (5) Analyze according to the type of unsafe behavior by personnel, operator errors, the ignorance of safety, and the ignorance of warnings are the main causes of injuries and deaths caused by hazardous chemicals.; (6) Through single-factor feature analysis and multi-feature comprehensive cross-discussion, countermeasures, and suggestions for preventing and controlling accidents in hazardous chemical enterprises are put forward according to the characteristics of accidents caused by different accident characteristics.

Safety ergonomics is an important branch of safety science and environmental engineering. As humans enter the era of big data, the development of information technology has brought new opportunities and challenges to the innovation, transformation, and upgrading of safety ergonomics, as the traditional safety ergonomics theory has gradually failed to adapt to the need for safe and clean production. Intelligent safety ergonomics (ISE) is regarded as a new direction for the development of safety ergonomics in the era of big data. Unfortunately, since ISE is an emerging concept, there is no research to clarify its basic problems, which leads to a lack of theoretical guidance for the research and practice of ISE. In order to solve the shortcomings of traditional safety ergonomics theories and methods, first of all, this paper answers the basic questions of ISE, including the basic concepts, characteristics, attributes, contents, and research objects. Then, practical application functions of ISE are systematically clarified. Finally, following the life cycle of the design, implementation, operation, and maintenance of the system, it ends with a discussion of the challenges and application prospects of ISE. The conclusion shows that ISE is a cleaner research direction for ergonomics in the era of big data, that it can deepen the understanding of humans, machines, and environment systems, and it can provide a new method for further research on safety and cleaner production. Overall, this paper not only helps safety researchers and practitioners to correctly understand the concept of intelligent safety ergonomics, but it will certainly inject energy and vitality into the development of safety ergonomics and cleaner production.

In large-diameter deep-hole blasting operations for mining stopes, optimizing blasting techniques can enhance ore recovery rates while minimizing damage to the surrounding rock mass. To improve blasting efficiency in the large-diameter deep-hole stopes of the Dongguashan Copper Mine, this study employed numerical simulations to evaluate the impact of various bore-hole parameters and charge structures on blasting outcomes. First, by comparing stress wave distributions and peak pressures for boreholes of different diameters, and analyzing crater formation patterns for different row and borehole spacing configurations, the optimal borehole net-work parameters for 165 mm boreholes with non-coupled charge structures were determined. Subsequently, edge-hole blasting models were developed using radial non-coupled and axial spaced charge methods under different blasting schemes. Finally, the damage characteristics of ore pillar rock masses were examined through damage contour maps and stress curves for different edge-hole charge structures. Key findings indicate that increasing the radial non-coupling coefficient reduces peak stress and stabilizes equivalent stress at monitoring points, minimizing damage to ore pillars. Additionally, axial charge structures with optimized charge density and segment length significantly improve boundary control by reducing the damage radius. These results establish optimized borehole and charge configurations, achieving a balance between efficient fragmentation and minimal ore loss.The findings have practical implications for improving blasting designs in underground mines, particularly under high-stress conditions, and provide a basis for future research on advanced blasting techniques with enhanced energy efficiency and minimal environmental impact.

As mineral resources become increasingly scarce and environmental awareness grows, mining companies urgently need cost-effective and environmentally friendly filling methods for mines. The use of ultrafine tailings combined with various binders at different ratios plays a key role in determining the rheological properties and mechanical strength of cemented paste backfill. This paper provides parameter basis for the design of a mine filling system, firstly, different filling materials and proportions were selected, and basic physicochemical property tests, along with cementitious material analysis, were conducted to determine the key physical and chemical properties and mineral composition of the materials. Next, the rheological properties, transport performance, and bleeding characteristics of the filling slurry were tested to assess the feasibility and process requirements for self-flow transport through pipelines. The results indicated that the transportation performance of the slurry was better when gelling powder was used as the binder. Finally, the compressive, shear, and tensile strengths of the filling blocks were tested under varying slurry mass concentrations and cement-tailings ratios at different curing times. This helped identify the optimal filling concentration and proportions needed to meet the strength requirements of the filling structure. The final study found that the sample with a cement-tailings ratio of 1:6, a mass concentration of 68%, and a gelling powder as the binder reached the strength requirements for mine filling after 28 days. This research offers valuable insights for other mining companies seeking to implement similar environmentally sustainable and cost-effective filling technologies, promoting wider adoption and advancements in the field.