During the excavation process in fractured rock masses in underground mines, blasting excavation can cause dangerous block collapses on surrounding rock. The applicable blasting method can effectively reduce the damage of surrounding rock caused by blasting disturbance. In order to investigate the impact of blasting disturbances of dangerous blocks in fractured rock mass drift, this study focuses on the instability characteristics of dangerous blocks during the excavation process in a mine located in Jilin Province. Based on detailed detection of discontinuities characteristics in the study area, the damage effects of shaped charge hydraulic blasting and conventional blasting on surrounding rock structure are analyzed and compared by using equivalent static method. The 3DEC discrete element software was used to analyze the instability characteristics of surrounding rock dangerous blocks under these two different blasting methods. The results indicate that using shaped charge hydraulic blasting can effectively reduce the impact of blasting disturbances on the excavated rock mass and minimize dangerous block collapses during drift excavation.

In plateau and high-altitude areas, freeze-thaw cycles often alter the uniaxial compressive strength (UCS) of rock, thereby impacting the stability of geotechnical engineering. Acquiring rock samples in these areas for UCS testing is often time-consuming and labor-intensive. This study developed a hybrid model based on the XGBoost algorithm to predict the UCS of rock under freeze-thaw conditions. First, a database was created containing longitudinal wave velocity (Vp), rock porosity (P), rock density (D), freezing temperature (T), number of freeze-thaw cycles (FTCs), and UCS. Four swarm intelligence optimization algorithms—artificial bee colony, Newton–Raphson, particle swarm optimization, and dung beetle optimization—were used to optimize the maximum iterations, depth, and learning rate of the XGBoost model, thereby enhancing model accuracy and developing four hybrid models. The four hybrid models were compared to a single XGBoost model and a random forest (RF) model to evaluate overall performance, and the optimal model was selected. The results demonstrate that all hybrid models outperform the single models. The XGBoost model optimized by the sparrow algorithm (R² = 0.94, RMSE = 10.10, MAPE = 0.095, MAE = 7.22) performed best in predicting UCS. SHapley Additive exPlanations (SHAP) were used to assess the marginal contribution of each input variable to the UCS prediction of freeze-thawed rock. This study is expected to provide a reference for predicting the UCS of freeze-thawed rock using machine learning.

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.

Changbai jade is a type of jade ore with good economic value that is found in the tuff of the Upper Triassic Changbai Formation of the Mesozoic period in Jilin Province, China. However, the mineral composition of the kaolinite group in Changbai Jade has not been definitively identified, and there has been a lack of systematic mineralogical and spectral analysis. To analyze the mineralogical and spectroscopic characteristics of Changbai jade, this study utilized several modern testing methods, including X-ray fluorescence spectrometry (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), Fourier transform infrared absorption spectroscopy (FTIR), Raman spectrum test (Raman), and Ultraviolet visible (UV-vis). Mineralogical and spectroscopic analyses were conducted on Changbai jade ore samples CB21 and CB22. The results indicated that the main metamorphic minerals of the two samples are dickite and the color-causing factor of the yellow part is pyrite. This research provides basic theoretical research data for jade processing technology and the geological origin of Changbai jade.

To address insufficient dynamic characterization and unsystematic multi-index integration in tailings dam hazard assessment, a comprehensive measurement method coupling numerical simulation with unascertained measure theory is proposed. A pyrite tailings reservoir was taken as the case study. FLOW-3D was used to perform three-dimensional water–sediment coupled dam-break simulations, from which key dynamic parameters (maximum deposition depth and peak flow velocity) were measured. The 3DMine model was coupled to quantify inundation extent and economic losses. A four-index evaluation system was constructed, and linear unascertained measure functions were applied for risk classification under three dam-height scenarios (60 m, 63.5 m, and 65 m). Simulated results show that, as dam height increases, breach initiation advances from 39 s to 28 s, maximum deposition depth rises from 4.35 m to 5.26 m, and peak velocity increases from 14.88 m/s to 15.22 m/s. The 370 m side drift remains safe, while the 352 m side drift, concentrator plant, and office area become inundated. Risk classification yields Grade III/II for 60 m, Grade IV/II for 63.5 m, and Grade IV for 65 m. The proposed measurement-based methodology enables systematic quantitative evaluation and supports dam-height optimization and downstream disaster mitigation.