Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as the prediction object and used the unit weight, cohesion, internal friction angle, pore water pressure coefficient, slope angle, and slope height as prediction indices to analyze the slope stability based on the collection of 117 slope data points. The genetic algorithm was used to solve the hyperparameters of machine learning algorithms by simulating the phenomena of reproduction, hybridization, and mutation in the natural selection and natural genetic processes. Five algorithms were used, including the support vector machine, random forest, nearest neighbor, decision tree, and gradient boosting machine models. Finally, all of the obtained stability prediction results were compared. The prediction outcomes were analyzed using the confusion matrix, receiver characteristic operator (ROC), and area under the curve (AUC) value. The AUC values of all machine learning prediction results were between 0.824 and 0.964, showing excellent performance. Considering the AUC value, accuracy, and other factors, the random forest algorithm with KS cutoff was determined to be the optimal model, and the relative importance of the influencing variables was studied. The results show that cohesion was the factor that most affects slope stability, and the influence factor was 0.327. This study proves the effectiveness of the integrated techniques for slope stability prediction, makes essential suggestions for future slope stability analysis, and may be extensively applied in other industrial projects.

Dust is a severe environmental issue in open-pit mines, and accurate estimation of its concentration allows for viable solutions for its control and management. This research proposes a machine learning-based solution for accurately estimating dust concentrations. The proposed approach, tested using real data from the Haerwusu open-pit coal mine in China, is based upon the integrated random forest-Markov chain (RF-MC) model. The random forest method is used for estimation, while the Markov chain is used for estimation correction. The wind speed, temperature, humidity, and atmospheric pressure are used as inputs, while PM2.5, PM10, and TSP are taken as estimated outputs. A detailed procedure for implementing the RF-MC is presented, and the estimated performance is analyzed. The results show that after correction, the root mean squared error significantly decreased from 7.40 to 2.56 μg/m³ for PM2.5, from 15.73 to 5.28 μg/m³ for PM10, and from 18.99 to 6.27 μg/m³ for TSP, and the Pearson correlation coefficient and the mean absolute error also improved considerably. This work provides an improved machine learning approach for dust concentration estimation in open-pit coal mines, with a greater emphasis on simplicity and rapid model updates, which is more applicable to ensure the prudent use of water resources and overall environmental conservation, both of which are advantageous to green mining.

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations.

Based on the split Hopkinson pressure bar (SHPB) test system, dynamic impact tests of coal specimens under different impact pressures were carried out to study the relationship between the impact load and the size of crushed lump coal. Based on the theory of stress wave attenuation, the relationship between the blasting impact load in a single‐hole blasting area of a coal seam and the load applied in an impact failure test of a coal specimen in the laboratory was established. According to the characteristics of the fragmentation distribution of the coal specimens destroyed under a laboratory impact load and the requirement of the minimum cost control of coal blasting in an open‐pit coal mine, the fragmentation size range was divided into three groups: large‐diameter, medium‐diameter, and powder particles. Based on this range, the variation rule of the mass percentage of coal fragments with impact pressure was obtained. Established on the evaluation principle of the blasting effect in an open‐pit coal mine, a good impact fragmentation effect was obtained. The good pressure range is 0.30 MPa ≤ P < 0.90 MPa.

The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development.

Constructing a water‐blocking layer in the dump sites of an open‐pit coal mine is of great significance to improve the water retention capacity of the reclamation area. The permeability law of the water‐blocking layer is costly to be analyzed and researched by means of field tests. In this study, the soil‐rock mixture samples similar to the water‐blocking layer were prepared, and the rock block proportion (RBP) and hydraulic pressure were adopted as test variables to conduct a permeability test, which provided theoretical support for on‐site construction. The results show that when the hydraulic pressure is less than the confining pressure, the permeability increases at a steady rate as the rock block proportion increases. When the hydraulic pressure is close to or equal to the confining pressure, penetrating cracks are easily formed between the soil‐rock interfaces of the sample with a higher rock block proportion, resulting in a rapid increase in permeability. With the increase of hydraulic pressure, the migration of the internal fine particles in the sample with a rock block proportion of 40% or less leads to the partial cracks closure, which gradually reduces the permeability. The internal structure of the sample with a rock block proportion more than 40% experiences a process in which the permeability decreases with the crack closure to a significant increase due to the penetrating crack formation. In summary, the soil‐rock mixture with about 30% rock block proportion is characterized by suitable permeability and stability, which guarantees the construction cost on‐site at the same time. In addition, increasing the rolling times of the truck can increase the compaction of the water‐blocking layer and reduce the permeability. The roughness and gradation of the rock blocks can improve the permeability and stability of the water‐blocking layer.