Inrush Hazards Research Articles

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

Estimation of rock mass permeability using relaxation time and P‐wave velocity

AbstractDue to the inherent unpredictability of geological conditions, tunnelling operations are often at risk of encountering water inrushes. Such incidents can lead to construction delays, impose financial strains and pose significant safety threats to the workers involved. Water‐bearing geological formations are the main triggers for such incidents, with factors such as the positioning, water quantity and permeability distribution of these formations being key to predicting the occurrence and severity of water inrush disasters. By leveraging the complex interplay among relaxation time, P‐wave velocity and permeability within the rock's physical properties, a series of indoor tests were conducted on 40 artificial reef limestone cores to extract the necessary parameters. Through the analysis of the data, the comprehensive permeability prediction model was established, and the correlation coefficient was 0.9420 between the model's predictions and actual measurements. At the same time, through theoretical and mechanism analysis, the relationship between permeability and relaxation time and the relationship between permeability and P‐wave velocity were analysed. Finally, 10 natural reef limestone samples were used to verify the accuracy of the model. The prediction model enables an accurate evaluation of tunnel permeability, thus providing a scientific basis for the mitigation of tunnel water inrush hazards.

Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining

Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. To obtain a more robust and precise prediction of WCFZ in undersea mining, a WCFZ prediction dataset with 122 cases of fractured zones was constructed. Five machine learning algorithms (linear regression, XGBRegressor, RandomForestRegressor, LineareSVR, and KNeighborsRegressor) were employed to develop five corresponding predictive models, taking multiple factors into account.The optimal parameters for each model are obtained through ten-fold cross-validation (10CV). The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination (R2) and mean squared error (MSE). A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria, showing a high R2 value of up to 0.925 and a low MSE value of 3.61. The proposed model was validated in a recently established mining area on Sanshan Island, China. It shows low absolute and relative errors of 0.71 m and 2.01%, respectively, between the predicted value from the model and observation result from the field, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ.

Hydrogeological analysis and remediation strategies for water inrush hazards in highway karst tunnels

Geological hazards, particularly water inrush, pose frequent challenges in tunnel constructions within karst regions. Drawing from the engineering experiences in addressing multiple large-scale water inrush hazards in the Dejiang tunnel, a combined method of field investigation and theoretical analysis was employed for qualitative and quantitative analyses of water inrush mechanisms. The regional geological investigation highlighted the formation of strong and weak aquifers due to alternating weak limestone and mudstone in the synclinal strata, creating favorable conditions for large-scale karst conduit. Atmospheric precipitation water connects karst cavities through groundwater-eroded karst conduit, establishing a recharge system for water inrush hazards. Three empirical formulas are adopted to investigate the influence of rainfall on the tunnel’s actual water inflow. Importantly, it was found that considering only the influence of rainfall or surface water cannot reasonably predict the inflow in this area. The disaster’s primary causes are attributed to the heavy rainfall, karst conduit, underground river, and weak strata, which have contributed to the persistent nature of water inrush events. The principle of “drainage and blockage” was used to address the issue of water inrush. Proposed remediation strategies during construction, including the innovative membrane bag high-pressure grouting, have proved to be effective in addressing the connected karst conduits. The drainage and blockage effects were achieved by installing drainage pipes during the grouting reinforcement process. Finally, the water pressure-resistant structure was employed to enhance the stiffness of the lining. As validated by monitoring data, the measures serve as crucial methods to guarantee the safety of tunnel structures, effectively prevent water inrush and amplify the ecological benefits of water-rich areas.

Integrating Microseismic Monitoring for Predicting Water Inrush Hazards in Coal Mines

The essence of roof water inrush in coal mines fundamentally stems from the development of water-bearing fracture zones, facilitating the intrusion of overlying aquifers and thereby leading to water hazard incidents. Monitoring rock-fracturing conditions through the analysis of microseismic data can, to a certain extent, facilitate the prediction and early warning of water hazards. The water inflow volume stands as the most characteristic type of data in mine water inrush accidents. Hence, we investigated the feasibility of predicting water inrush events through anomalies in microseismic data from the perspective of water inflow volume variations. The data collected from the microseismic monitoring system at the 208 working face were utilized to compute localization information and source parameters. Based on the hydrogeological conditions of the working face, the energy screening range and its calculation grid characteristics were determined, followed by the generation of kernel density cloud maps at different depths. By observing these microseismic kernel density cloud maps, probabilities of roof water-conducting channel formation and potential locations were inferred. Subsequently, based on the positions of these roof water-conducting channels on the planar domain, the extension depth and expansion direction of the water-conducting channels were determined. Utilizing microseismic monitoring data, a quantitative assessment of water inrush risk was conducted, thereby establishing a linkage between microseismic data and water (inrush) data, which are two indirectly related datasets. The height of microseismic events was directly proportional to the trend of water inflow in the working face. In contrast, the occurrence of water inflow events and microseismic events exhibited a specific lag effect, with microseismic events occurring prior to water inrush events. Abnormalities in microseismic monitoring data partially reflect changes in water-conducting channel patterns. When connected with coal seam damage zones, water inrush hazards may occur. Therefore, abnormalities in microseismic monitoring data can be regarded as one of the precursor signals indicating potential floor water inrushes in coal seams.

Statistical analysis of the impact of ore draw strategies on wet inrush hazard susceptibility in a panel cave mine

Inrush hazards involve a sudden inflow of material into an underground mine excavation. In a cave-mining setting, this typically involves high amounts of fine-grained material and water flowing from an extraction-level drawpoint. Operations encountering these hazards have developed tactical and strategic risk management plans to address the risk posed to mine safety and production. These plans have improved over time in meeting safety requirements owing to the utilization of evolving automated systems. However, managing the impact of inrushes on production rates has remained an area of improvement, particularly in mature caves with high quantities of fine material and stored water. In such conditions, understanding the impact of draw strategies on inrush susceptibility is particularly important to guide a risk-informed draw optimization strategy, with the goal of minimizing spill frequency while maintaining target production rates. In this study, the impact of draw strategies on inrush susceptibility was assessed through the statistical analysis of draw and spill history at PT Freeport Indonesia's Deep Ore Zone mine. Two draw-related variables were used for quantification of draw strategy: Draw Rate and Differential Draw Index. Differential Draw Index is a new parameter developed in this study to quantify draw non-uniformity, with the aim of capturing the impact of differential draw rates on inrush susceptibility between neighboring drawpoints. The results from this study show that Draw Rate and Differential Draw Index should be considered concurrently when assessing the impact of draw strategy on inrush hazards. An up to 8-fold increase in spill probability was observed when both Draw Rate and Differential Draw Index were in their upper ranges, compared to when either of these variables was in its lower ranges. The results also showed that if a drawpoint had a long-term history of isolated draw, it was more susceptible to inrush under short-term non-uniform draw.

Gradient optimization method for tunnel resistivity and chargeability joint inversion based on deep learning

The water inrush hazards have become one of the bottleneck problems that constrain tunnel construction. Ahead geological prospecting is the major tool to avoid geo-hazards and ensure safe, economical and efficient tunnelling. This work proposes a novel deep learning-based electrical method, which jointly inverses resistivity and chargeability to estimate water-bearing structures and water volume. Specifically, we design an encoder–decoder network, with one shared encoder to extract features from input data, two encoders to output resistivity and chargeability models, respectively, and an elaborate collinear regularization on the two outputs to reduce solution multiplicity. Moreover, our input is first transformed to the gradient domain to address the spatial incorrespondence issue between observation data and the electrical model. Compared with traditional linear inversion methods, our proposed method demonstrates superiority in locating and delineating anomalous bodies. The ablation study also shows that joint inversion of two parameters could benefit and outperform independent parameter inversion.

Water–rock two-phase flow model for water inrush and instability of fault rocks during mine tunnelling

Water inrush hazard is one of the major threats in mining tunnel construction. Rock particle migration in the seepage process is the main cause of water inrush pathway and rock instability. In this paper, a radial water–rock mixture flow model is established to study the evolution laws of water inrush and rock instability. The reliability of the proposed model is verified by the experimental data from a previous study. Through the mixture flow model, temporal-spatial evolution laws of different hydraulic and mechanical properties are analysed. And the proposed model’s applicability and limitations are discussed by comparing it with the existing water inrush model. The result shows that this model has high accuracy both in temporal evolution and spatial distribution. The accuracy of the model is related to the fluctuation caused by particle migration and the deviation of the set value. During the seepage, the porosity, permeability, volume discharge rate and volume concentration of the fluidized particle increase rapidly due to the particle migration, and this phenomenon is significant near the fluid outlet. As the seepage progresses, the volume concentration at the outlet decreases rapidly after reaching the peak, which leads to a decrease in the growth rate of permeability and porosity, and finally a stable seepage state can be maintained. In addition, the pore pressure is not fixed during radial particle migration and decreases with particle migration. Under the effect of particle migration, the downward radial displacement and decrease in effective radial stress are observed. In addition, both cohesion and shear stress of the rock material decreased, and the rock instability eventually occurred at the outlet.

The formation mechanism of dynamic water and mud inrush geohazard triggered by deep-buried tunnel crossing active fault: Insights from the geomechanical model test

Water and mud inrush geohazard is one of the most important geohazards for underground engineering. Understanding the formation mechanism of water and mud inrush is critical for directing the prevention and control of this geohazard. As a result, a large number of methods have provided insight into this topic, such as the geomechanical model test. However, this geohazard triggered by deep-buried tunnel crossing active fault has not yet been explored. Thus, this study develops a geomechanical model test system to simulate the dynamic water and mud inrush caused by Kangding No.2 tunnel crossing Yunongxi active fault. The results imply that seismic loading can result in fluctuating changes in seepage pressure and stress in the tunnel rock mass. Increasing seismic intensity broadens the fluctuation range, together with a dramatic rise in peak seepage pressure. The coupled effect of earthquake loading and high seepage pressure makes the water-resisting rock mass extremely vulnerable to fracturing, increasing the probability of water and mud inrush. High seismic intensity leads to rock fracture propagation, ulteriorly giving rise to stress releasing, eventually provoking the fracturing of rock mass. Precursor of water and mud inrush can be observed before the geohazard occurs, reflected by the gradual rise of seepage pressure and intermittent fluctuation of stress. Following the increasing earthquake intensity, peak acceleration occurs in a random position of water-resisting rock mass, but mostly appears at the tunnel contour of surrounding rock. The free face of the tunnel exerts an amplifying effect on seismic acceleration. Seismicity affects the formation of water and mud inrush from active fault, which ulteriorly determines the minimum safety thickness of water-resisting rock mass. Monitoring the seepage pressure and stress in water-resisting rock mass is effective for the early warning of inrush hazard.

Preventing water inrush hazards in coal mines by coal gangue backfilling in gobs: influences of the particle size and stress on seepage characteristics.

The backfilling mining method that fills gobs with coal gangue can prevent water inrush hazards, protect groundwater resources, and protect the ecological environment of the mining area. However, initial conditions including the particle size distribution of gangue and the stress environment may affect the seepage characteristics of gangue backfill and inrush prevention ability. Taking the particle size and stress as main controlling factors, the seepage tests were designed for gangue to evaluate influences of the particle size and stress on the void ratio, permeability, and non-Darcian flow factor of gangue. In the meantime, the four stages in dynamic changes of seepage channels were studied and the impervious envelope lines of gangue backfill materials were provided. The results show that the larger the particle sizes, the stronger the crushing resistance of particles; under high stress (> 6.67MPa), seepage channels in small gangue particles (< 5mm) change in a more complex manner, and the non-Darcian flow phenomena become more significant. The particle size and stress exert significant influences on the seepage characteristics. Therefore, when reducing water inrush hazards by gangue backfilling in gobs, the particle size distribution should be optimized by combining the stress and water pressure conditions. Seepage channels in gangue backfill materials vary with changes in the particle size and stress. Their variation can be divided into four stages: shrinkage of seepage channels, reconstruction of seepage channels, dynamic equilibrium between slight expansion and shrinkage, and persistence of the impervious effect. After the first and second stages have been fully developed, the preliminary impervious conditions are met; after full development of the fourth stage, the gangue backfill materials reach an impervious state.

Risk Assessment and Zonation of Roof Water Inrush Based on the Analytic Hierarchy Process, Principle Component Analysis, and Improved Game Theory (AHP–PCA–IGT) Method

With the large-scale mining of deeply buried coal seams, the risk of roof water inrush increases during mining. In order to ensure safe mining, it is necessary to predict the risk potential of water inrush from the roof aquifer. This study introduces a coupling evaluation method, including the analytic hierarchy process (AHP), principal component analysis (PCA), and improved Game theory (IGT). This paper takes the water inrush from the roof aquifer of the 11-2 coal seam in Kouzidong mine as the research object. An evaluation index system is constructed by selecting six evaluation factors, including the aquitard effective thickness, aquiclude thickness, the ratio of sandstone to mudstone, rock quality designation, fault fractal dimension, and wash water quantity of geological log. The comprehensive weighting method based on IGT is used to optimize the subjective and objective weighting values obtained by AHP and PCA methods in turn, and an AHP–PCA–IGT evaluation model is established to divide and evaluate the water inrush risk zonation of the roof aquifer. The risk degree of the water inrush gradually decreases from the center to the north–south, and the main areas with relatively high risks and higher risks are distributed in a small part of the western and eastern regions. Finally, combining various drilling data examples, drilling pumping tests, and water inrush sites, the accuracy of the predicted results is validated through the vulnerability fitting percentage (VFP). The predictions are basically consistent with the actual results, and this study lays a theoretical foundation for the prevention and control of water inrush hazards.

An improved model to predict the water-inrush risk under an unconsolidated confined aquifer based on analytic hierarchy process and information value method

To prevent water inrush hazards of mining under the Quaternary unconsolidated confined aquifer in North China coalfield, it is necessary to build a water inrush prediction model to guarantee coal safety production. The current method of evaluating mining hazards under unconsolidated confined aquifers is easy to fall into the problems of simple superposition of evaluation factors, large subjectivity of the factor selection, and low accuracy of prediction results. In this paper, an analytic hierarchy process (AHP) is improved according to contribution of information value method (IVM) to analyze the impact of water inrush factors. The factor weight values are considered in calculating the total information value of IVM to build a coupled AHP-IVM model. The information value of each factor is calculated through IVM, and area under curve (AUC) value is used to rank the factors, to exclude the subjective judgment of the importance of each factor. The geological and hydrogeological data of Qinan coal mine in Huaibei coalfield are analyzed and organized. The results suggest that the coupled AHP-IVM model has an AUC value of 0.8309, indicating its great potential and practical significance for future mining operations.

Precise Judgment of Reverse Fault-Induced Water Inrush Hazard under Influence of Roof Goaf Water

Previous research recognized the threat of faults to safe mining and the importance of identifying fault failure patterns, and began to use theoretical research and numerical simulations to study the activation laws of faults during mining. Conventional wisdom may suggest that the height of the fractured water-conducting zone (FWCZ) of the overburden strata over goaf will be increasingly caused by fault activation, thereby causing roof water inrush, in particular, goaf water existing in the roof of working face. Therefore, the FWCZ in the overburden strata make accurate judgments that are regarded as a key foundation to evaluate the safety of coal mining under water bodies. In view of this problem, the 15,103 working face of Wenzhuang Coal Mine in Shanxi Province were taken as the engineering background, the height of the FWCZ of the adjacent 15,100 working face was observed by drilling fluid leakage method and drilling television method, the observed results provided a reference for judgment of the height of the FWCZ of 15,103 working face. Additionally, the drilling method was adopted to conduct exploration on the terminal location of F6 reverse fault in overburden strata of No. 15 coal seam, the result showed that the disturbance range of F6 reverse fault was located in the FWCZ formed after mining the 15,103 working face. Furthermore, the method of numerical simulation analysis was used to study the failure height of overburden strata after mining the 15,103 working face through F6 reverse fault. The height of the FWCZ of F6 reversed fault was basically equal to that of the upper and lower plates, and F6 reverse fault had no influence on the height of the FWCZ after mining the 15,103 working face. There was a sufficient thick overburden strata between the maximal elevation of the fractured zone and the roof goaf water, and mining through F6 reverse fault under old goaf was safe and reliable. The research results can provide reference for the safe mining of passing through reverse faults under the influence of roof goaf water.

Study on Construction Response Characteristics of Large Cross-Section Tunnel Crossing Huge Karst Cave Backfill

The existence of giant karst cave can cause collapse and water inrush hazards during the excavation of the karst tunnel, causing serious economic losses. In this paper, based on the FEA software MIDAS, a numerical simulation model for the backfilling of a large karst cave through a large cross-section tunnel was established. The distribution characteristics and change rules of the displacement field, stress field, horizontal support stress, and plastic zone of the tunnel surrounding rock and backfill were explored. The first stage of tunnel excavation leads to a sharp increase in the displacement of the tunnel’s surrounding rock and the deterioration of the plastic zone, which should be strengthened and monitored. The tunnel arch, waist, and wall footing areas were prone to stress concentration. The two layers of horizontal support force show a certain regularity, showing a sharp increase and a gradually smooth growth trend. Meanwhile, the site monitoring results for arch settlement and horizontal convergence of the mega cavern tunnel were analyzed. The results show that the numerical calculation results were in good agreement with the values of site monitoring data. The average errors of final crown settlement and horizontal convergence were 8.6% and 15.9%, respectively, which verified the correctness of the numerical modeling method. This project can provide reliable experience for the construction of similar large cavern tunnels.

Quantifying the Impact of Coal Mining on Underground Water in Arid and Semi-Arid Area: A Case Study of the New Shanghai No. 1 Coal Mine, Ordos Basin, China

The new Shanghai No. 1 Coal Mine is located in arid and semiarid area of northwest China, which is characterized by scarce rainfall, intense evaporation, and limited water resources. High-intensity coal mining has caused severe damage to groundwater resources. The Baotashan sandstone aquifer of the Jurassic system has abundant water resources, and they are stored in the floor strata of mining coal seams. This poses the risk of high-pressure build-up and water inrush hazards during the mining of coal. To avoid these, the Baotashan sandstone aquifer needs to be drained and depressurized, which can result in a huge waste of water resources. Thus, taking the New Shanghai No. 1 Coal Mine as the basis for the case study, the impact of coal mining on the underground water resources was quantified. Large-scale water release tests were performed under the shaft to determine the hydrogeological properties of the Baotashan sandstone aquifer and a three-dimensional numerical model of the groundwater system was established. The dynamic phenomenon of water drainage was simulated and the drained water discharge was predicted under the condition of safe mining.

Evaluation of Water Inrush Hazard in Karst Tunnel Based on Improved Non-Linear Attribute Variable Weight Recognition Model

Water inrush in karst tunnels will cause casualties and economic losses. Thus, it is significant to objectively assess the water inrush risk level and adopt valid preventive measures to reduce losses from this disaster. The relationship between the factors affecting water inrush in the dynamic coupling system is strong nonlinear, so the attribute recognition model, which lessens the mutation points and error and causes the evaluation results to be more reasonable and accurate, is improved nonlinearly in this paper. Firstly, the assessment system was established by selecting seven factors: formation lithology, unfavorable geological conditions, attitude of rock formation, landform and physiognomy, contact zones of dissolvable and insoluble rock, layer and interlayer fissures, and groundwater level. Secondly, the multi-factor interaction matrix, C-OWA operator, and variable weight theory are used to calculate the constant weight and variable weight of each evaluation index. In addition, the linear attribute measurement function of the attribute identification model is optimized by using the non-linear trigonometric function to distinguish the risk level of the water inrush. Finally, the proposed model was successfully used in Qiyueshan Tunnel. The evaluation results of the risk level are more accurate than other methods, and they are in agreement with the excavation results. The proposed model provides a valuable reference for the risk assessment and project management of tunnel construction.

Failure Characteristics of the Water-Resisting Coal Pillar under Stress-Seepage Coupling and Determination of Reasonable Coal Pillar Width

Groundwater inrush hazard has always been a great threat to the construction of vertical shafts in coal mines. Generally, the failure of the water-resisting coal pillar under coupled stress-seepage conditions around the vertical shaft is the main reason for the generation of the water inrush channel. In order to understand the mechanical behaviors of the water-resisting coal pillar, the strength of typical coal affected by the size and water content was investigated, and the stress sensitivity of permeability was investigated by a stress-seepage coupling test. The stress field and deformation of the water-resisting coal pillar were investigated by numerical simulation, the stability of the water-resisting coal pillars with different widths was evaluated, and the reasonable width of the coal pillars under coupled stress-seepage condition was determined. Results show that the water content and coal pillar width have a great influence on the mechanical characteristics of coal samples. Under the conditions of lower water content and larger coal sample width, the coal sample presents higher strength, smaller axial deformation, smaller permeability and porosity, and weak sensitivity to stress. The simulation results show that the boundary of the main roadway at the end of the coal pillar is dominated by tensile stress, and fractures can significantly contribute to the destruction of coal pillars. With the increase in the width of the water-resisting coal pillar, the internal damage variable, maximum tensile stress, porosity, and average water flow velocity of the coal pillar decrease, which reduces the risk of water inrush and improves the safety of the water-resisting coal pillar. An evaluation model of the reasonable width of the water-resisting coal pillar under the stress-seepage coupling was proposed, and the model was verified by the shear slip law and experimental results. This study provides theoretical and experimental guidance for the risk management of groundwater inrush disaster during the construction of vertical shafts in coal mines.

A new attempt for the detection of potential water-bearing structures in tunnels via hydraulic tomography: The first numerical investigation

Water-bearing geological structures in front of the tunnel and underground engineering are prone to water inrush hazards, which seriously threaten construction safety. This study investigated the potential of hydraulic tomography (HT) in detecting water-bearing structures to prevent groundwater hazards in tunnels. We established two synthetic heterogeneous aquifers with two different water-bearing structures, fractures and cavern zones. A comprehensive HT inversion analysis was performed using hydraulic head records generated by forward hydraulic tests. The results show that 1) sufficient pumping and observation wells can assist in capturing water-bearing structures and hydraulic properties; 2) transient hydraulic tomography (THT) inversion has a better effect than steady-state hydraulic tomography (SSHT) inversion since the temporal variation of head records carries non-redundant characteristics and distribution information of water-bearing structures; 3) inverted hydraulic conductivity and specific storage fields are positively correlated with observed heads at the end and early stages of the pumping test, respectively; 4) taking geological information as a prior can greatly assist in mapping potential water-bearing structures. This study can provide valuable guidance for detecting water-bearing structures in tunnel and underground engineering and formulating effective control schemes for water inrush hazards.

Understanding water inrush hazard of weak geological structure in deep mine engineering: A seepage-induced erosion model considering tortuosity

Understanding water inrush hazard of weak geological structure in deep mine engineering: A seepage-induced erosion model considering tortuosity

Characterisation of Fine Particles Generated at Draw points During Sub-Level Cave Mining for Assessment of Mud Inrush Hazard

The inundation of mud, known in the industry as ‘mud inrush’ is a hazard associated with caving mining methods. Ineffective inrush management strategies can result in sections of the mine lost, or in some cases, fatalities. Previous studies have focused on water and various percentages of fines (particles <2mm - <5cm) in draw points as risk indicators for the hazard. Standardised material tests were conducted on samples collected from a sub-level caving operation in Australia. Concrete slump tests were carried out on two in-situ mud samples based on their observed mobility. The immobile sample had 6% more material below 300μm and required a 2% higher moisture content to begin slumping and turn from plastic to soft flow. Shear strength tests reflected the higher plasticity of the immobile sample with a cohesive strength of 23.08 kPa compared to the second mobile sample of 9.16 kPa. The elemental assay revealed the mobile sample had lower iron oxide, and higher silicon oxide content than the immobile sample. The work presented in this paper indicates that fine fractions <0.6mm noticeably affect laboratory test results that are hypothesised to relate mud flowability and potential hazard.