Mine Roof Research Articles

The failure of edge-cracked hard roof in underground mining: An analytical study

Hard roof is the primary concern of strata control in underground mining. Various techniques have been utilized to fracture the hard roof and control the failure of strata. Understanding the impact of cracks on strata behaviour is vital for optimizing strata control strategies. In this study, the hard roof was regarded as a beam structure with different loading, support, and boundary conditions. The equivalent spring model was adopted to represent the edge-cracked section of the hard roof, which allows additional rotation at the crack location. A piecewise-defined function was developed for solving equations of hard roof in the vicinity of the crack section. By combining the hard roof beam model and the equivalent spring model, the impact of a crack on the hard roof can be measured. A case study was carried out to explore the impacts of crack location and crack depth on the mechanical state of the hard roof. Results showcase the failure of the hard roof controlled by the crack depth and greatly influenced by the crack location. From the perspective of coal burst prevention, roof fracturing should be implemented at the high-stress area of strata, whereas it has been challenging in practice to determine such a location precisely. To address this challenge, it was suggested that hard roof fracturing should be carried out before coal seam de-stressing, increasing the likelihood of a crack occurring in a high-stress area. By adopting the proposed method, the mechanical state of the edge-cracked hard roof can be quantified.

Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation

AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as a means of ranking the influence that the input parameters have on the output of the model. Its results indicate that among different parameters, the CMRR parameter with a sensitivity degree of 0.11 has the most impact on the model, even with the smallest change in this parameter, a significant change is made in the model output.

Evaluating the Geo-Environmental Conditions within a Working Face Using a Hybrid Intelligent Optimization Model

Geological anomalies within the working face likely induce geological disasters, such as water, gas, and coal mine roof fall, directly impacting the rational planning and safe mining of underground resources. Constrained by the conditions of underground closed spaces, effective reconstruction under incomplete and highly sparse projection is the central challenge when evaluating geo-environmental conditions. This work proposes a new hybrid intelligent optimization model (MPGA-SIRT) that integrates a multiple-population genetic algorithm (MPGA) with the simultaneous iterative reconstruction technique (SIRT) to finely reconstruct the geo-environmental conditions within a working face based on electromagnetic wave tomography theory. MPGA-SIRT can provide a more precise initial inversion model for the conventional linear reconstruction technique of SIRT, incorporating a local search property by leveraging the robust global search capacity of MPGA. The advantages of MPGA-SIRT have been demonstrated through numerical modeling, theoretical testing, and engineering practices on the 8208 working face in the Datong mining area, Shanxi Province. In comparison to individual SIRT inversion models, MPGA-SIRT reconstruction yields more accurate and stable performance, as demonstrated by the evolution curve of the objective function and the corresponding convergence tomography results. Consequently, the geomagnetic wave absorption coefficient within the area of reconstruction can be precisely ascertained through the use of our proposed technique. This innovation represents a groundbreaking strategy for assessing geological anomaly zones within a working face. The introduced method stands as a valuable theoretical instrument for confronting the complexities associated with geo-environmental reconstruction in underground engineering.

Optimization of a coal mine roof characterization model using machine learning

Predicting areas of increased propensity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing rock mass strength or characterization indices in isolation. Validation of applied ground support systems typically, and in part, comprises a review of observed deformation and analysis of extensometer data from adjacent previously mined areas. This information is seldom routinely assessed relative to the rock mass strength or characterization data. Typically, roof deformation is a response to several combining factors such as mining-induced stress, roof lithology, rock mass strength, and roadway geometry. Therefore, it is optimal to develop an approach to integrate these factors to quantify their relative significance to causing roof deformation, with the goal of establishing a reliable quantitative model for predicting roof deformation.This paper provides a case study in machine learning using Artificial Neural Network (ANN) and geophysical log data information from an underground coal mine located in the Bowen Basin (Australia), whereby a suite of parameters is considered pertinent to excavation stability. This machine learning predictive model analyses key geotechnical parameters including field and mining-induced stress, rock mass strength, clay/shale content, and strata monitoring data to identify strata hazards. Finally, the classification model can indicate the risk of roof displacement and observed roof deformation as communicated through a contoured ‘geotechnical hazard map’. This hazard map can assist site engineers understand roof conditions in underground coal mining and thus put in place operational mitigation processes to reduce geotechnical risk on mining. This could potentially improve the identification of high-risk areas and inform future bolting requirements to maintain a safe working environment and minimise production interruptions.

Study on the early warning of cracking and water inrush risk of coal mine roof and floor

Microseismic monitoring has proven to be an effective approach for detecting and preempting water inrush incidents within mining operations. However, challenges persist, particularly in terms of relying on a singular early warning index and the complexities involved in quantification. In response to these obstacles, a dedicated investigation was undertaken against the backdrop of mining activities at the 11,023 working face of Paner Coal Mine. Primarily, a novel methodology for categorizing the roof and floor into distinct zones was established based on the vertical distribution of microseismic events. Furthermore, this study delves into the dynamic evolution of key source parameters, such as microseismic energy, apparent stress, and apparent volume, amidst mining disturbances, enabling a comprehensive evaluation of the risk associated with roof and floor cracking, as well as potential water inrush incidents. A groundbreaking approach to early warning was proposed, operating on three pivotal dimensions: the depth of fractures, the intensity of fractures, and the likelihood of water inrush. Through rigorous validation during mining operations at the 11,023 working face, the efficacy was substantiated. Ultimately, the achievements offer invaluable insights and practical guidance for the advancement and implementation of water inrush early warning systems in coal mining contexts.

Integrating robotic systems in underground roof support machine

Traditionally, the roof bolting process is performed manually by skilled workers, which is time-consuming and labor-intensive. This often exposes human operators to several risks. Bolt installation in underground coal mining operations involves moving and manipulating heavy tools quickly while ensuring the safety of the area. Hence, humans must relocate from an active mining site to a more secure place where the operator can control and monitor the roof bolting process. This study explores the integration of a six-axis robotic arm and other specialized novel technologies into an existing roof bolter machine to automate the roof bolting module, therefore replacing human duties. Owing to the harsh mining conditions in underground coal mines, the integration of robotic systems requires proper planning to ensure reliable and safe operations. The methodology adopted for integrating the robotic system in this study may be regarded as a conceptual framework for the automation of underground mining roof bolting operations. This study highlights certain specifications that should be considered when automating underground mining machines. The results showed that a robotic arm that performs human tasks during roof bolting operations can be successfully integrated into a roof bolting module by establishing a robust communication network to facilitate seamless interaction between different hydraulic, mechanical, and electronic components.

Study on the destabilization and deformation breakage characteristics of the mine roof under repetitive mining of close coal seam group

Study on the destabilization and deformation breakage characteristics of the mine roof under repetitive mining of close coal seam group

Associations between Surface Deformation and Groundwater Storage in Different Landscape Areas of the Loess Plateau, China

The Loess Plateau is an important grain-producing area and energy base in China and is an area featuring dramatic changes in both surface and underground processes. However, the associations between surface deformation and groundwater storage changes in different landscape types in the region are still unclear. Based on Sentinel-1 and GRACE (Gravity Recovery and Climate Experiment) data, this study monitored and verified the surface deformation and groundwater storage changes in different landscape types, such as those of the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area, in the Loess Plateau of China from 2020 to 2021. Through time series and cumulative analysis using the same spatial and temporal resolution, the associations between these two changes in different regions are discussed. The results show that: (1) the surface deformation rates in different landscape types differ significantly. The minimum surface deformation rate in the Kubuqi Desert is −5~5 mm/yr, while the surface deformation rates in the Hetao Irrigation District, the open-pit mine recovery area in the Jinbei Mining Area, and the Shendong Mining Area are −60~25 mm/yr, −25~25 mm/yr, and −95.33~26 mm/yr, respectively. (2) The regional groundwater reserves all showed a decreasing trend, with the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area declining by 359.42 mm, 103.30 mm, 45.60 mm, and 691.72 mm, respectively. (3) The surface elasticity deformation had the same trend as the temporal fluctuation in groundwater storage, and the diversion activity was the main reason why the temporal surface deformation in the Hetao Irrigation District lagged behind the change in groundwater storage by 1~2 months. The measure of “underground water reservoirs in coal mines” slows down the rate of collapse of coal mine roof formations, resulting in the strongest time-series correlation between mild deformation of the surface of the Shendong mine and changes in the amount of groundwater reserves (R = 0.73). This study analyzes the associations between surface deformation and groundwater storage changes in different landscape areas of the Loess Plateau of China and provides new approaches to analyzing the dynamic associations between the two and the causes of changes in both variables.

Coal mine roof accident causation modeling and system reliability research based on directed weighted network

Coal mine roof accidents have frequently caused massive casualties and economic losses. To understand the mechanism of roof accidents, 205 roof accidents in China from 2013 to 2022 were selected, and a total of 44 accident causal factors were identified, including the human-material-environment-supervisory and the final causal factor, and each causation chain was analyzed. Accordingly, a complex network is used to construct a causation network model for roof accidents, and the structural characteristics of the network are analyzed to obtain the key causal factors and important inter-causal relationships. Combined with the robustness analysis, three different intentional attacks are selected, and it is found that the proposed three methods significantly reduce the network efficiency compared with the random attack, and the deliberate attack based on the degree ranking has the best effect, which indicates that the developed method can effectively identify the causes of accidents. This study provides an important basis for the decision-making of coal mine roof accident prevention and reveals the difficulties of accident prevention, and also provides ideas and references for the study of accident mechanism and the construction of accident causation network model using complex networks.

Influence mechanism of mining speed on energy release in a mine roof and elastic coal system

Influence mechanism of mining speed on energy release in a mine roof and elastic coal system

Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection

As one of the most abundant fossil fuels in the world, the safe and efficient mining of coal resources has greatly affects all fields of human production and life. Accurate prediction of the water yield capacity (WYC) of coal seam roofs and advanced detection of water abundance is of great significance to preventing roof water disasters in coal mines. In the process of coal mining, complex roof water inrush mechanism, and changeable factors are faced. Moreover, the uncertainty of the interconnection between various factors increases the difficulty of roof water disaster prevention. To improve the accuracy and reliability of WYC prediction, this study analyzed the main control factors of coal seam roof water inrush and built a WYC quantitative prediction model based on the SAA-CNN algorithm. 47 samples (40 training sets and 7 test sets) were selected to train and learn the model, and the WYC of the 3rd coal mining roof in Jiangzhuang Coal Mine (JZCM) was predicted. To verify the reliability of the model, BP, RF, SVM, CNN, and SAA-CNN models were used for training and error comparison. Finally, the 1001 working face of JZCM was selected to use a variety of geophysical methods and drilling combined for advanced detection and construct the dynamic monitoring and prewarning system of coal seam roof water inrush risk. The results show that the accuracy of the WYC prediction model is SAA-CNN＞RF＞CNN＞SVM＞BP. The average absolute error of the SAA-CNN model is 0.87 m3/h, and the average relative error is 3.56 %. In this study, the quantitative and accurate prediction of WYC of coal seam roofs is realized. Combined with the advanced detection method of “electric-magnetic-drilling”, the prewarning system of water inrush risk of mining is constructed, which provides a new method for the prevention of water disasters in coal seam roofs.

A Stability Analysis of an Abandoned Gypsum Mine Based on Numerical Simulation Using the Itasca Model for Advanced Strain Softening Constitutive Model

An abandoned gypsum mine has been discovered beneath the route of a highway construction in Hunan province, south China. Due to the highway construction and operations safety, there is an urgent need for a comprehensive stability analysis of the abandoned mining area. The 3D laser scanning detection technique has been adopted, and over 400 drillholes were strategically placed near the highway to capture the spatial information of the abandoned gypsum mine. The ore body has an average mining thickness of about 3 m, and the depth of the mining roof ranges from 40 to 60 m, with an average span of 16 m. Based on the research achievements in the engineering geological investigation, rock mass quality assessment, and geometry information, a simplified numerical model has been established for stability analysis. The numerical model employed the IMASS rock mass constitutive model to conduct a stability analysis of the abandoned gypsum mine during the excavation process and in the medium to long term. The IMASS constitutive model can effectively reflect the entire process of rock mass from microscopic damage to macroscopic instability, and the numerical simulation of current and long-term stages provides a much greater understanding of the mining room stability and the effect of various geo-mechanical parameters not considered in traditional empirical methods. The abandoned gypsum mine stability is guaranteed in the mining and current stages. However, the numerical results showed that a 0.4 m spalling thickness of the sidewalls can cause an overall instability and failure of the abandoned mine, and reinforcement measures must be taken for long-term safety. The stability of the abandoned gypsum mine with filling solutions was also evaluated numerically.

Structural model and capacity determination of underground reservoir in goaf: a case study of Shendong mining area in China

The large-scale extraction of coal resources in the western mining areas of China has resulted in a significant loss of water resources, which is a challenge for coordinating resource extraction with ecological preservation in the mining areas. Although underground reservoir technology can effectively solve this problem, measuring the storage capacity of underground reservoirs through engineering experiments is costly and time-consuming. Currently, there is a lack of accurate, reliable, and low-cost theoretical calculation solutions, which greatly restricts the promotion and application of underground reservoir technology. The theoretical calculation methods for underground reservoir capacity were studied based on parameters from the Shendong mining area in China. A water storage structure model for coal mine underground water reservoirs was established, taking into account the settlement boundaries of the bedrock and loose layers in shallow coal seams, based on the key layer theory and the spatial structure model of the mining roof. The mathematical expression for the load on the coal-rock mass in the goaf was derived considering the rock breaking characteristics of the mining roof. The model determined the range of each water storage area, including the zone of loose body, zone of gradual load, and the compacted zone, based on the strength of the water storage capacity. The key parameters for calculating the water storage capacity were determined using a modified model for shallow thick loose layers and thin bedrock movement. Finally, a calculation method for the storage capacity was obtained. Based on the real data from the 22,615 working face of a mine in the Shendong mining area, the water storage capacity of the underground reservoir in the goaf was jointly calculated using FLAC3D, Surfer 12.0 and the proposed calculation method. The calculated water storage capacity was approximately 1.0191 million m3. Although this result was 2.20% smaller than the on-site water pumping experiment data, it still verifies the feasibility of the above calculation method for determining the water storage capacity of underground water reservoirs.

Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse

Understanding the characteristics and evolution of crack propagation in rock masses is crucial for evaluating their stability. By applying clustering theory to analyze recorded microseismic events, we differentiate the development positions of individual cracks amidst multiple crack formations. Three distinct crack cluster distribution patterns are identified, allowing for the evaluation of regional stability through microseismic event density and ellipsoidal model parameters. The process of crack propagation involves independent development at nucleation positions, mutual influence between adjacent locations, and subsequent crack growth and propagation. Additionally, we examine crack evolution prior to roof collapse and establish a connectivity model between surface and goaf roof cracks. When microseismic events are identified as developing along a plane, it indicates a higher risk of damage in that area. Through the analysis of crack propagation location and angle, our study provides a theoretical foundation for predicting crack direction. Notably, our model’s findings align with onsite observations, demonstrating its practical effectiveness. The results of this research offer valuable insights for collapse prediction and early warning systems for mine roofs, contributing to advancements in mining safety and operations.

Water-Richness Zoning Technology of Karst Aquifers at in the Roofs of Deep Phosphate Mines Based on Random Forest Model

The development of fractures and conduits in karst aquifers and the strength of their water richness are key factors in determining whether a water intrusion will occur in a mine. In the phosphorus mining process, if the mining of water-rich areas is carried out, sudden water disasters can easily occur. Therefore, water-richness zoning of the karst aquifer on the roof of the phosphate mine is very important to protect against the incidence of water disasters in the mine. This paper proposes a random-forest-based partitioning model of the water richness of phosphate mine roofs in karst areas based on the random forest intelligence algorithm in machine learning. Taking a productive phosphate mine in southern China as a typical case, seven main assessment indicators affecting the water richness of the phosphate mine roof aquifer were determined. The proposed random forest model was utilized to determine the weight of each evaluation index, and the water richness of the karst aquifer on the roof of this phosphate mine was studied by zoning. The whole structure of the mine is highly water-rich, with strongly water-rich areas mainly concentrated in the central and northeastern part of the mine. The water-richness fitting rates (WFP) introduced for validation were all in agreement with the evaluation results, and the constructed model met the accuracy requirements. The study’s findings can serve as a guide for mine design and water-disaster warnings in karst regions.

Water Abundance Evaluation of Aquifer Using GA-SVR-BP: A Case Study in the Hongliulin Coal Mine, China

At present, coal accounts for more than 56% of China’s primary energy consumption and will continue to dominate for a long time in the future. With the continuous expansion of the mining intensity and scale of Jurassic coal resources in Northwestern China, the problem of mine roof water disasters is becoming increasingly serious. The degree of harm is related to the hydrogeological structure of the overlying strata of the coal seam. Reasonable and effective prediction and evaluation of the water abundance of the coal seam roof aquifer is conducive to making scientific decisions on the prevention and control of roof water disasters, so as to achieve safe mining. In order to solve the problem of water abundance evaluation in mining areas lacking hydrological holes, taking the Hongliulin coal mine in Shennan mining area as an example, four main control factors for water abundance were selected: sandstone thickness, core recovery ratio, brittle rock thickness ratio, and flushing fluid consumption. Combined with unit water inflow and multiple factor comprehensive analysis, a back propagation (BP) artificial neural network and support vector machine regression (SVR) were introduced into water abundance evaluation. The reciprocal variance method was used to predict the measured unit water inflow. Finally, according to the “Detailed Rules for Coal Mine Water Prevention and Control”, the water abundance of aquifers was classified to verify the accuracy of the model and partition the water abundance of the study area. The results indicate that, based on the predicted results of unit water inflow, out of 37 borehole data, 22 weak water abundance holes and 15 medium water abundance holes were evaluated correctly, verifying their applicability. The study area was generally weak in water abundance, with two grades of medium and weak. The medium water abundance area was mainly located in the north and south of the study area, and the weak water abundance area was mainly located in the east and west. It can be seen that this evaluation model has certain applicability for evaluating the water abundance of coal seam roofs. It is of great significance, especially for the evaluation of water abundance in mining areas where hydrological holes are lacking.

Modelling of Fragile Coal Mine Roof and Estimation of Rock Loads—Some Empirical and Numerical Methods

Modelling of Fragile Coal Mine Roof and Estimation of Rock Loads—Some Empirical and Numerical Methods

A multi-level analysis method for evaluating the risk of roof caving and its application

Roof collapse is the most frequent production accident in the mine production process, which seriously threatens the efficient and safe production of the mine. Therefore, it is urgent to carry out practical research on the roof collapse tendency of the roadway. After searching and analyzing the relevant documents, the primary influencing factors of roof collapse risk based on AHP are determined, namely engineering geology, rock mass support, construction management and natural environment. After refining the main influencing factors, the evaluation factor set is obtained, the fuzzy comprehensive evaluation relationship matrix is established, and the fuzzy comprehensive evaluation model of roof collapse risk is obtained. Finally, the quantitative evaluation of no collapse risk, weak collapse risk, medium collapse risk and high collapse risk is carried out. Taking a metal mine as an example, the risk of roof collapse of its C11 haulage roadway is selected for fuzzy evaluation. The evaluation result is high collapse risk, which is consistent with the evaluation result of the current specification, indicating that the model can be used for mine roof collapse risk evaluation. This method of estimating roof collapse has been applied on-site, which is consistent with the actual situation and has achieved good results. It has guiding significance for predicting the stability of tunnels and supporting operations.

A new fusion method to predict coal mine roof accidents

AbstractIn the context that coal remains the main energy source of China now, the safety of coal mine production is still worth our attention. For the factors influencing the roof accident of coal mine roadway have great complexity and uncertainty, a new risk prediction method of coal mine roof accidents integrating T‐S fuzzy fault tree and Bayesian network (BN) is proposed. The constructed method determines the BN and the conditional probability tables according to the established T‐S fuzzy fault tree. The fault state and fuzzy fault rate of root nodes are described according to the fuzzy number. And the forward inference of Bayesian is used to calculate the probability of the accidents on the roadway roof based on the fault probability of the root nodes and actual fault probability during construction respectively, to realize the risk prediction of roof accidents. Finally, practical data are used to verify the new fusion risk evaluation method by taking two mines as cases. The results show that the influencing factors calculated by the new fusion risk prediction method are completely consistent with the actual situation, confirming the effectiveness and reliability of this method, which providing new ideas for the research about the risk prediction of coal mine roof accidents.

Dynamics of tidal parameters depending on the landslide hazard condition of the salt mine roof

The dynamics of tidal parameters were studied based on the results of tilt observations in 4 points of the landslide hazard zone, which was formed over the exhausted field of a salt mine in the city of Soledar, Donetsk region. The reduction in the thickness of the supporting walls of the salt mine led to the loss of their supporting stability, which led to the lowering of the ground, the destruction of the foundations and walls of residential and administrative buildings in a plot 1.3 km long and 0.5 km wide. To eliminate the risk of collapse, the used underground galleries were filled with salt in order to strengthen their retaining walls.Thus, the landslide hazard zone has become a unique training ground for studying the change in tidal parameters γ and Δϕ depending on the state of the mine roof. In total, 256 continuous hourly monthly series were processed using modern methods of excluding their linear drift and anomalous meteorological disturbances from the initial data at short observation intervals, which made it possible to significantly increase the accuracy of determining tidal parameters based on the results of harmonic analysis. For the first time in the global earth tide practice, the normalization of the tilt parameters during and after strengthening the collapsing roof of the mine was revealed. If before the strengthening of the supporting walls of the underground galleries, the tidal parameters assumed abnormal values (the amplitude factor γ by 30 %, and the phase factor Δϕ by 10—15 degrees differed from the regional analogues), then in the process of backfilling they began to approach their regional values, which they reached a year after the backfilling. The real possibility of using the earth tide method for monitoring and forecasting the landslide hazard and related phenomena of the ground has been proven.