Chengye Yang

Accurate prediction of peak particle velocity (PPV) in open-pit mine blasting is critical for ensuring operational safety and effective vibration control. This study proposes a hybrid modeling approach that integrates the Centered Collision Optimization (CCO) algorithm with Extreme Gradient Boosting (XGBoost), enhanced by SHAP-based sensitivity analysis to improve model transparency and mechanistic interpretability. A comprehensive dataset was constructed based on 193 field-measured blasting records collected from the Panzhihua Iron Mine in China, incorporating nine key input parameters. Model performance was rigorously evaluated using four widely recognized metrics: coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). The results demonstrate that the CCO–XGBoost model achieves superior predictive performance, with R² = 0.967, RMSE = 0.110, MAE = 0.067, and VAF = 96.35%, outperforming conventional approaches. SHAP-based sensitivity analysis reveals that blast-to-monitor distance (R) is the dominant negative predictor of PPV, contributing 43% to the total influence, with its vibration attenuation effect intensifying significantly when R exceeds 54 m. Charge per hole (q) and total charge per delay (Q) are identified as the primary positive influencing factors, accounting for 24% and 20% of the total contribution, respectively: the positive promoting effect of q on PPV strengthens markedly when q exceeds 17 kg, while Q exerts a continuous positive increasing influence on PPV when it exceeds 253 kg. Compared to existing hybrid models, the CCO–XGBoost uniquely avoids local optima and ensures higher global stability. This study fills the gap by providing quantifiable engineering thresholds for practical vibration control, making the model directly applicable to on-site blasting optimization.

Aiming at the problem of surrounding rock instability easily induced by high ground stress in the process of deep-well mining, the optimization of stope structure parameters is studied by combining numerical simulation with theoretical analysis. Firstly, the physical and mechanical properties of rock mass are fully understood using laboratory experiments. Then, six kinds of stope structure parameter schemes are preliminarily designed using the Matthews chart method. According to the geological conditions of the Ruihai Gold Mine, a large three-dimensional numerical model is established. Based on FLAC3D, the follow-filling continuous mining method is used to simulate the six schemes. By analyzing the influence and law of different stope structures on the stress, displacement, and plastic zone evolution of surrounding rock, the most effective mining strategy to balance the safety and economic benefits of the target area is determined. In the area with good rock mass quality, the optimal stope dimensions are 20 m in height, 15 m in width, and 80 m in length. In the rock mass area with fault crossing or relatively developed joint fissures, a reduced configuration of 20 m height, 10 m width, and 70 m length is recommended to enhance stability and stress management. Finally, comparative analysis of mining methods confirms that the follow-filling continuous mining method effectively mitigates ground pressure, offering a theoretical foundation for the safe and efficient extraction of deep mineral resources.

In the mechanized mining of metal mines, identifying and optimizing vulnerabilities within the production system is essential for enhancing operational efficiency and ensuring sustainable development. By leveraging data from 88 stopes at Guangxi Tongkeng Mine over a decade, we constructed a comprehensive dataset encompassing drilling, charging, blasting, ventilation, support, ore drawing, and maintenance. The XGBoost algorithm was employed to model factors influencing stope production capacity (PC), with its parameters optimized using the Marine Predator Algorithm (MPA). The MPA–XGBoost model demonstrates a high predictive accuracy for PC (R² = 0.958, VAF = 95.981%, MAE = 4.844, RMSE = 7.033). A Shapley Additive Explanations (SHAP) analysis reveals that drilling efficiency (DE) contributes most positively (35.6%), while ventilation time (VT) and equipment maintenance time (EMT) negatively impact PC. SHAP dependence plots indicate that increasing DE significantly enhances PC, whereas excessive VT or EMT leads to a substantial decline in PC. These findings offer valuable insights and a robust foundation for optimizing design and improving production management in mechanized mining operations.

To investigate the pore structure evolution characteristics and damage mechanism of sandstone subjected to treatment with freeze–thaw cycles, quantitative analyses were conducted on the longitudinal wave velocity (LWV) and T₂ spectrum of sandstone before and after 10, 20, 30, and 40 freeze–thaw cycles, using longitudinal wave velocity testing, low-field nuclear magnetic resonance (NMR) testing, and fractal theory. The results show that, with the increase in the number of freeze–thaw cycles, the LWV of sandstone gradually decreases, the amplitude of the saturated T₂ spectrum gradually increases, the amplitude of the centrifugal T₂ spectrum gradually decreases, the total porosity and effective porosity increase, and the residual porosity decreases. After undergoing freeze–thaw cycles, sandstone exhibits obvious fractal characteristics in both the total porosity NMR fractal dimension and the effective porosity NMR fractal dimension, and the growth rates of both decrease exponentially with the increase in the number of freeze–thaw cycles. The magnitude of the fractal dimensions reflects the complexity of the pore structure, with smaller fractal dimensions indicating better pore connectivity. In summary, the damage evolution mechanism of sandstone under freeze–thaw cycles is characterized by the gradual expansion and interconnection of internal closed micro-pores (cracks), along with increased total porosity and effective porosity, leading to enhanced freeze–thaw damage.

Microwave-assisted mechanical rock breaking represents an innovative technology in the realm of mining excavation. The intricate and variable characteristics of geological formations necessitate a comprehensive understanding of the interplay between microwave-induced rock damage and the subsequent deterioration in rock strength. This study conducted microwave irradiation damage assessments on 78 distinct rock samples, encompassing granite, sandstone, and marble. A total of ten critical parameters were identified: Microwave Irradiation Time (MIT), Microwave Irradiation Power (MIP), Longitudinal Wave Velocity prior to Microwave Treatment (LWVB), Longitudinal Wave Velocity post-Microwave Treatment (LWVA), Percentage Decrease in Longitudinal Wave Velocity (LWVP), Porosity before Microwave Treatment (PB), Porosity after Microwave Treatment (PA), Percentage Increase in Porosity (PP), and Uniaxial Compressive Strength following Microwave Treatment (UCSA). Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradient Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to elucidate the relationship between UCSA and the nine additional parameters. This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. The findings reveal that the PKO-XGBoost model provides superior accuracy in delineating relationships among rock physical properties, microwave irradiation variables, microscopic attributes of rocks, and UCSA. Notably, PA emerged as having the most significant effect on UCSA, indicating that microwave-induced microscopic damage is a primary contributor to reductions in rock strength. Additionally, MR exhibited substantial influence; under identical microwave irradiation conditions, rocks with lower density demonstrated greater susceptibility to strength degradation. Furthermore, during microwave-assisted rock breaking operations, it is imperative to establish optimal MIT and MIP values to effectively diminish UCSA while facilitating mechanical cutting processes. The insights derived from this research offer a more rapid, cost-efficient approach for accurately assessing correlations between microwave irradiation parameters and resultant rock damage—providing essential data support for enhancing mechanical rock-breaking efficiency.

In the drifts of underground metal mines, the extraction of rock mass discontinuity characteristics from point cloud models generated with laser scanning has become the main approach. However, the exposure of discontinuities is restricted in drifts, and the size of discontinuities cannot be measured directly. Therefore, it is necessary to use a reasonable sampling tool to estimate the mean trace length of the discontinuities that are mapped in the point cloud model. In this paper, a method to estimate the mean trace length of discontinuities using a three-dimensional (3D) model of a drift (3DM) is proposed. Through the point cloud data of a drift obtained using 3D laser scanning, the information on discontinuities in the surrounding rock was extracted; then, the mean trace length was estimated using 3DEC to set sampling windows on the roof and sidewall in the 3DM. By analyzing the difference between the circular sampling window and the rectangular sampling window using simulated cases, the estimation results showed that the mean trace length obtained using circular measuring windows in the 3DM was closer to the true trace length. Finally, the method was used in a practical engineering case in Jianshan Iron Mine, Panzhihua, Sichuan, China.