To ensure the on-site implementation of regulations and technical measures for rock burst prevention and control, this study takes Yankuang Energy Group Co., Ltd. as an example, establishes an on-site technical management system for preventing and controlling rock burst in coal mines. This on-site technical management system is based on the principles of zero rock burst accident, graded management and control, general manager and chief engineer responsibility, as well as scientific, systematic, streamlined, and efficient management. This system includes a technical management system and an on-site management mode, among which the former includes an organizational system, an institutional system, a technical data management system, and a comprehensive supervision and management system. The on-site management mode includes five aspects and six links. The construction of an on-site technical management system for rock burst prevention and control can ensure the timely detection and rectification of problems, remove management loopholes, and prevent the occurrence of rock burst disasters.
In order to improve the stability of the slope and prevent the occurrence of landslide disaster, this study took the east slope of the first mining area of Zhundong Open-pit Coal Mine as the engineering background, and used a combination of the two-dimensional limit equilibrium method and three-dimensional numerical simulation to optimize the shape of the east slope. By selecting a typical calculation profile based on the Bishop method and the residual thrust method in the two-dimensional rigid body limit equilibrium method, this research carried out the stability analysis of the profile slope, and preliminarily designed the slope shape of the profile position meeting the requirements of the safety reserve coefficient and stripping ratio. Based on the three-dimensional finite element strength reduction method, this paper investigated the reasonably change of the width of the transport plate to solve the problem of the slope shape that does not meet the requirements of safety reserve coefficient and stripping ratio, and established a three-dimensional numerical simulation model of various schemes. It also studied the influence of different tracking distances and slope angles on slope stability, calculated the three-dimensional stability of the slope under different spatial forms, then determined the optimal tracking distance and optimal slope angle, and further optimize the slope stability and stripping ratio. The results show that: when the width of the transport plate of the DBS3 section slope is 8 m, it does not meet the requirement of safety reserve coefficient 1.2; when the width of the transport plate is set to 24 m, it meets the requirement of a safety reserve coefficient of 1.2 and an economic stripping ratio of not more than 8.0 m3/t. The three-dimensional numerical simulation results concluded that the optimal tracking distance on the east side is 50 m, and the optimal slope angle is 35°. After the optimization design of a two-dimensional and three-dimensional slope shape, 2.456 million tons of coal resources were mined, creating a profit of about 21.1268 million yuan. It not only prevents landslide disasters, but also further improve the recovery rate of coal resources.
Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters. This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation. To achieve this, data on fifty geo-blast design parameters were collected and used to train machine learning algorithms. The objective was to develop predictive models for estimating the blast oversize percentage, incorporating seven controlled components and one uncontrollable index. The study employed a combination of hybrid long-short-term memory (LSTM), support vector regression, and random forest algorithms. Among these, the LSTM model enhanced with the tree seed algorithm (LSTM-TSA) demonstrated the highest prediction accuracy when handling large datasets. The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden, spacing, stemming length, drill hole length, charge length, powder factor, and joint set number. The estimated percentage oversize values for these parameters were determined as 0.7 m, 0.9 m, 0.65 m, 1.4 m, 0.7 m, 1.03 kg/m3, 35 %, and 2, respectively. Application of the LSTM-TSA model resulted in a significant 28.1 % increase in the crusher's production rate, showcasing its effectiveness in improving blasting operations.
Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment. Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations. Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks, particularly in environmentally sensitive areas. This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu, India, leveraging the power of Artificial Neural Networks (ANNs) and integrating multi-dimensional geospatial datasets. Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness, reproducibility, and the ability to analyze the complex interplay of factors influencing landslide hazards quantitatively. The methodology involves rigorous pre-processing and integrating spatial data, including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility. These parameters encompass elevation, slope aspect, slope degree, distance to roads, land use patterns, geomorphology, lithology, drainage density, lineament density, and rainfall distribution. Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences. This process identifies the most relevant variables influencing landslide susceptibility, enhancing the model's predictive capabilities. The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors, enabling the development of a robust and accurate landslide susceptibility model. The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics, including accuracy, precision, and the Area under the Receiver Operating Characteristic (ROC) curve. Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods, demonstrating higher accuracy and reliability in predicting landslide-prone areas. The resulting Landslide Susceptibility Map (LSM) categorises the study area into five distinct hazard zones, ranging from very high (664.1 km2), high (598.9 km2), moderate (639.7 km2), low (478.9 km2) and to very low (170.9 km2). Notably, the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences. The study's findings have far-reaching implications for disaster risk reduction efforts, land-use planning, and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.
In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters, this study proposes an Improved Northern Gok algorithm (INGO) and Random Forest (RF) roadway surrounding rock stability prediction model. This model combines the improved INGO-RF based on the analysis of influencing factors of roadway surrounding rock stability. First, three strategies were employed to enhance the Northern Gob algorithm (NGO): logistic chaotic mapping, refraction reverse learning, and improved sine and cosine. Subsequently, INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF. Secondly, a data set consisting of 34 groups of roadway surrounding rock data is selected. The input indexes of the model include the roof strength, two-wall strength, floor strength, burial depth, roadway pillar width, ratio of direct roof thickness to mining height, and surrounding rock integrity. Meanwhile, surrounding rock stability is considered as the output index. Particle swarm optimization backpropagation neural network (PSO-BPNN), genetic algorithm optimization support vector machine (GA-SVM), Sparrow Search Algorithm optimization RF (SSA-RF) models were introduced to compare the predictive results with the INGO-RF model, and the results showed that: INGO-RF model has the best performance in the comparison of various performance indicators; compared with other models, the accuracy rate (Ac) in the test set has increased by 0.12-0.40, the accuracy rate (Pr) has increased by 0.07-0.65, and the recall rate (Re) has increased by 0.08-0.37; the harmonic mean (F1-Score) of the recall rate increased by 0.08-0.52, the mean absolute error (MAE) decreased by 0.1428-0.4285, the mean absolute percentage error (MAPE) decreased by 7.15%-28.57 %, and the root mean square error (RMSE) decreased by 0.1565-0.3779; and finally, the data on surrounding rock conditions of roadways in multiple mining areas in Shanxi Province were collected to test the INGO-RF model. The results indicate that the predicted outcomes closely align with the actual results, demonstrating a certain level of reliability and stability, which can better meet the practical needs of engineering and avoid the occurrence of mine disasters.
In order to enhance the accuracy and efficiency of coal and gas outburst prediction, a novel approach combining Kernel Principal Component Analysis (KPCA) with an Improved Whale Optimization Algorithm (IWOA) optimized extreme learning machine (ELM) is proposed for precise forecasting of coal and gas outburst disasters in mines. Firstly, based on the influencing factors of coal and gas outburst disasters, nine coupling indexes are selected, including gas pressure, geological structure, initial velocity of gas emission, and coal structure type. The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27, followed by extraction of the principal components of the original data through Kernel Principal Component Analysis (KPCA). The Whale Optimization Algorithm (WOA) was enhanced by incorporating adaptive weight, variable helix position update, and optimal neighborhood disturbance to augment its performance. The improved Whale Optimization Algorithm (IWOA) is subsequently employed to optimize the weight ф of the Extreme Learning Machine (ELM) input layer and the threshold g of the hidden layer, thereby enhancing its predictive accuracy and mitigating the issue of "over-fitting" associated with ELM to some extent. The principal components extracted by KPCA were utilized as input, while the outburst risk grade served as output. Subsequently, a comparative analysis was conducted between these results and those obtained from WOA-SVC, PSO-BPNN, and SSA-RF models. The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters, with results consistent with actual situations. Compared to other models tested, the model's performance showed an increase in Ac by 0.2, 0.3, and 0.2 respectively; P increased by 0.15, 0.2167, and 0.1333 respectively; R increased by 0.25, 0.3, and 0.2333 respectively; F1-Score increased by 0.2031, 0.2607, and 0.1864 respectively; Kappa coefficient k increased by 0.3226, 0.4762 and 0.3175, respectively. The practicality and stability of the IWOA-ELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values. This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.