Rock Burst Research Articles

Experimental and numerical study of drop hammer test on honeycomb sandwich panel resistant to rock burst in coal mine roadway

This study addresses the ubiquitous issue of effective support resistant to the rock bursts within coal mine roadways and introduces an innovative solution in the form of a steel honeycomb sandwich panel (HSP). It is designed for installation on traditional metal supports in coal mine roadways, serves as an integral element of an energy-absorption support system tailored to withstand rock burst impacts. The investigation incorporates drop hammer impact tests conducted on eight HSP specimens, varying in cell wall thickness and core height. A comprehensive three-dimensional finite element model, inclusive of mesh considerations, was constructed and subsequently validated. Combining experimental and numerical approaches, the drop hammer impact response and energy absorption characteristics of HSP structures were analyzed. The findings reveal good energy absorption capabilities, with HSPs dissipating over 90 % of impact energy, over 85 % attributed to the core layer. Thinner core cell walls reduce peak impact loads, mitigating structural impact effects. In the experimental parameters delineated in the present study, it is observed that core layers with thicknesses of 1.0 mm and 1.5 mm demonstrate enhanced energy absorption capabilities. Variations in core height minimally affect peak impact forces, with only a 7.41 % distinction between 60 mm and 140 mm core layers. However, it is worth noting that higher core layers can achieve an enhanced energy absorption capacity. Thinner facesheets enhance energy absorption, and alterations in impactor mass have minimal effects under constant impact energy conditions. Additionally, a predictive equation for estimating indentation depth was proposed, closely aligned with finite element results. This equation holds potential for informing future HSP designs, offering valuable insights for diverse engineering applications.

Machine Learning-Based Classification of Rock Bursts in an Active Coal Mine Dominated by Non-Destructive Tremors

Rock bursts are dynamic phenomena in underground openings, causing damage to support and infrastructure, and are one of the main natural hazards in underground coal mines. The prediction of rock bursts is important for improving safety in mine openings. The hazard of rock bursts is correlated with seismic activity, but rock bursts are rare compared to non-destructive tremors. The five machine learning classifiers (multilayer perceptron, adaptive boosting, gradient boosting, K-nearest neighbors, and Gaussian naïve Bayes), along with an ensemble hard-voting classifier composed of these classifiers, were used to recognize rock bursts among the dominant non-destructive tremors. Machine learning models were trained and tested on ten sets of randomly selected data obtained from one of the active hard coal mines in the Upper Silesian Coal Basin, Poland. For each of the 627 cases in the database, 15 features representing geological, geomechanical, mining, and technical conditions in the opening as well as tremor energy and correlated peak particle velocity were determined. Geological and geomechanical parameters of the coal seams and surrounding rocks were aggregated into a single GEO index. The share of rock bursts in the database was only about 8.5%; therefore, the ADASYN balancing method, which addresses imbalanced datasets, was used. The ensemble hard-voting classifier most effectively classified rock bursts, with an average recall of 0.74.

A Feasibility Study on Microseismic Monitoring of Rock Burst in Traffic Tunnels

A Feasibility Study on Microseismic Monitoring of Rock Burst in Traffic Tunnels

A size‐dependent energy‐based strain burst criterion

AbstractThis paper presents a size‐dependent energy‐based strain burst criterion linking strength, elasticity, fracture energies and specimen size effect with stress state due to changes in boundary conditions. It proposes the concept of a ‘Burst Envelope’, a surface in three‐dimensional principal stress space derived based on energy storing and dissipation characteristics of a rock sample, taking into account the size of the specimen and potential localised failure pattern. A scalar burst index is also proposed to quantify the bursting scale. To illustrate and verify its functioning, a numerical modelling framework based on the distinct element method and employing a new cohesive‐frictional contact model is used to perform virtual strain burst experiments under different polyaxial loading‐unloading scenarios, mimicking various underground excavation scenarios. The obtained results are in good agreement with the theoretical prediction of burst occurrence. On that basis, the variation of burst possibility and magnitude are investigated with key factors, including confinement level and the material's elastic, strength and fracture properties. The effect of the specimen's aspect ratio and size on the rock burst potential is elaborated and verified using virtual strain burst experiments, facilitating the linking of the proposed theoretical framework with the evaluation of in‐situ strain bursts in rock masses around underground openings.

Risk prediction of coal mine rock burst based on machine learning and feature selection algorithm

ABSTRACT With the intensification of coal mining in China, rock burst hazards have become increasingly frequent and serious. To comprehensively investigate the accident indicators during mining activities, identify the key triggering factors, and better prevent rockburst accidents, this study establishes a rockburst risk prediction index system containing 16 indicators in four subsystems: geological structure, mining technology, safety management, and employee situation. By using 118 cases as sample data, this study proposes to apply four feature selection methods (Boruta, PC, RF, BE) to reveal the relationship between these indicators and select the most salient indicators for risk prediction. Then four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Machine (GBM), SVM, and KNN are used to compare the effects of different feature selection methods on the prediction accuracy of risk level. The results show that Boruta performs best as a feature selection method for RF, GBM, and KNN, and the Boruta-RF model has better predictive performance than the others.

Uncovering the progressive failure process of primary coal-rock mass specimens: Insights from energy evolution, acoustic emission crack patterns, and visual characterization

The failure and instability of deep coal-rock mass structures, which are influenced by mining disturbances, are major contributors to disasters like rock bursts. This study involved conducting indoor experiments using uniaxial loading test and acoustic emission (AE) experiments on indigenous primary coal-rock mass (PCRM). We utilize chaotic bifurcation based on strain energy to characterize the progressive damage stages of PCRM, demonstrating the chaotic characteristics of energy evolution during the gradual destruction process. The degree of damage resulting from PCRM failure was evaluated by jointly applying P-wave velocity tomography and AE event localization. Additionally, the classification of crack patterns in PCRM was conducted using the RA-AF correlation analysis method. The segmentation boundaries of crack classification patterns were then redefined using the Gaussian mixture model (GMM) algorithm. The research findings indicate that the chaotic bifurcation model of strain energy classifies stress into four stages: the stable region (Ⅰ), the metastable region (Ⅱ), the bifurcation region (Ⅲ), and the chaotic region (Ⅳ). The presence of a low-velocity zone in P-waves may indicate crack accumulation and damage within the system. Additionally, areas with abnormal P-wave velocities exhibit numerous AE events. The crack classification patterns of the four stages undergo an evolution from predominantly tensile cracks to a mixture of tensile and shear cracks, and the GMM algorithm successfully identifies the optimal separation path for crack classification boundaries. We propose an adaptive kernel density estimation (AKDE) algorithm to quantify the spatial distribution of AE events, thus offering a visual representation of the damage patterns at various stages.

Influence of Subsoil and Building Material Properties on Mine-Induced Soil–Structure Interaction Effect

Soil–structure interaction (SSI) refers to the dynamic interaction between a structure and the surrounding soil on which it rests. The behavior of the soil can significantly affect the response of the building structure. In the context of civil engineering and structural analysis, SSI becomes particularly important when considering the response of structures to dynamic loads such as earthquakes or so-called paraseismic loads, e.g., mining tremors. Several factors contribute to SSI. Soil and building structure material properties, foundation type, and loading conditions are the most important parameters. The article concerns SSI in the case of mining rock bursts in Poland. The influence of changes in site material conditions and building material properties on the SSI phenomenon was investigated. A few variants of different properties of typical construction materials (brick, reinforced concrete, and cellular concrete) in the case of selected representative building structure were considered. The subsoil material properties from the wide range were also taken into account. Numerical three-dimensional finite element method (FEM) analysis was applied. The adopted models of the soil-structure system were verified by data from in situ experimental vibration measurements. A significant influence of the subgrade material and the building structure material on the SSI was demonstrated.

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

The localization results of acoustic emission (AE) events can reflect the location and pattern of burst-prone rock failures. However, event localization heavily depends on the quality of the original waveform of the sensor. Therefore, this study analyzed the AE waveform of a rock sample under compression to evaluate its failure localization and quality. From the research results, it could be seen that the initial failure was relatively calm, with clear take-off points, which can be better used for accurate AE event positioning. However, the later failure was severe, causing the take-off points of most sensors to be very unclear, and positioning methods that rely on take-off points cannot be used for positioning, let alone simply using the positioning results of the built-in software. This research result reminds researchers who use AE signals for event localization to first examine the quality and status of the original waveform, providing a basis for obtaining accurate localization results, in order to further accurately study the subsequent failure patterns. The above facts indicate that the initial failure is small and scattered, while the later failure is large and concentrated, with certain fractal characteristics.

Characterization of torsion plate energy-absorbing members and multi-objective optimization of mechanical properties

In order to prevent damage to the support equipment caused by rock burst, a torsion plate energy absorption member is designed to be used with hydraulic columns. Explicit dynamic simulation is used and compared with the tests. The equations of structural parameters to the mean value of the reaction force (Fmean), specific energy absorption (SEA), and mean squared deviation (σ) are constructed based on the response surface method. The structural parameters are optimized using MOPSO. The results indicate that the torsion plate exhibits excellent energy absorption characteristics. The error rate of the simulation compared to the test is less than 5%, indicating a high level of simulation accuracy. All three sets of response fitted equations have R2 above 0.97, indicating a good fit. The three evaluation indexes demonstrate a trend of increasing then decreasing as the torsion angle increases. The wall thickness is positively correlated with the influence of each parameter index, while the effect of fillet radius is reversed. The maximum relative error of the predicted value is 9.50%. After optimization, the Fmean is effectively increased by more than 78%, the SEA is increased by more than 24.72%. The σ is increased by 8.31%, the optimization effect is remarkable.

Grade prediction of rock burst based on PSO-RVM model

A broad and accurate rock burst prediction model is crucial for preventing rock burst disasters in engineering. The relevance vector machine (RVM) algorithm based on the particle swarm optimization (PSO) is proposed for its prediction in this paper. The PSO is used to optimize the kernel parameter inside the RVM, while the RVM is applied to complete the prediction task. Firstly, according to a series of existing classification standards and theoretical research of rock burst, three impact indicators and four rock burst grades are summarized. Next, the PSO-RVM model is trained by the cross-validation method with main indicators as input and rock burst grades as output. Then, the universality and accuracy of the proposed model are verified by different engineering samples. Finally, a specific engineering case is used to verify the practicality of the model, and the prediction accuracy of rock burst grade is up to 100%.

Study on the technical management practice of rock burst prevention and control - Case study of Yankuang Energy Group Company Limited

To ensure the on-site implementation of regulations and technical measures for rock burst prevention and control, taking Yankuang Energy Group Company Limited. as an example, an on-site technical management system for preventing and controlling rock burst in coal mines is established. 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”, and “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. Each system is interrelated, mutually promoting and supporting the other system. The on-site management mode includes five aspects and six links. These “five aspects” refer to the standardization of safe production, normalization of on-site operations, processization of system operation, quantification of antirisk ability, and demonstration of mine management. The “six links” on-site management mode includes the management system, source risk management and control, dynamic risk analysis, monitoring and control, on-site implementation and management, and emergency management. 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.

Comprehensive evaluation of coal burst risk using optimized linear weighted model

The assessment of coal burst risk is a complex and systematic process; the variations among the indicator systems and the stability of the evaluation models used can influence the results. In this study, an index system for the analytic hierarchy process was constructed based on 21 geomechanically influential factors on rock bursts. The multi-weight combination optimization model was used to synthesize the subjective weights derived by the four experts using AHP and the objective weights derived through the inter-criteria correlation method to obtain the unique optimization weights. After normalizing the original evaluation data, the Gram–Schmidt orthogonalization method was employed to eliminate correlations among factors. The optimized factor weights and data were subsequently input into a linearly weighted comprehensive evaluation model to determine the coal burst risk. The proposed method was applied to assess the coal burst risk of a coal seam in the Liang Jia Coal Mine. These results align with those of the actual coal mine scenario. Indeed, the proposed linear weighted comprehensive evaluation model provided enhanced accuracy and reliability with improved practicality compared to previously proposed methods.

Dynamic tensile properties of a novel high strength and high toughness bolt steel: Constitutive models and microstructures

With the continuous increase of depth in underground engineering, rock bursts or rock explosions can cause instantaneous shock load and result in rock engineering disasters. To solve these problems, a novel high strength and high toughness (HSHT) bolt steel with constant resistance and large deformation is proposed. This study aims to investigate the dynamic constitutive models and microstructure of HSHT steel under extreme loading conditions. A series of dynamic tension tests were conducted using a Split Hopkinson Tensile Bar (SHTB) system with a high-speed camera. The results revealed that the HSHT steel exhibited a positive strain rate effect. As the strain rate increased from 1000 s−1 to 5000 s−1, the yield strength and ultimate strength of the HSHT steel increased gradually. However, no obvious changes in the uniform elongation. Specifically, the yield strength, tension strength, and uniform elongation of strain rate at 5000 s−1 were 1100 MPa, 1170 MPa, and 30%, respectively. According to the results of the SHTB tests, a modified Johnson-Cook model was proposed. Additionally, we investigated the microstructure of HSHT steel at a high strain rate through microscopic characterization techniques. A large number of fine and deep dimples were observed near the fracture of HSHT steel under dynamic loading. Moreover, the HSHT steel demonstrates a significant presence of nano-twins, which effectively impedes crack propagation both within the grain interior and along grain boundaries. The above findings reveal the relationship between mechanical behavior and microstructure in HSHT steel within dynamic loading conditions, providing valuable insights for the design of HSHT steel in underground engineering.

Сейсмические события в Воркутском углепромышленном районе в 2023 году

In 2023, regional seismic stations recorded 25 seismic events in the Vorkuta coal industrial district of the Komi Republic, with a local magnitude ML from 1.8 to 2.8. A significant contribution to the detection of weak, ML ≤ 2, events was made by data obtained from the temporary seismic station of the IG FRC Komi SC UB RAS, installed as part of expeditionary work in the summer of 2023 in the Polar Urals.
 Among them the seismic event on August 1, 2023, which had a macroseismic effect, is of particular interest. Residents of several districts of Vorkuta felt tremors similar to an earthquake. Seismic records from 5 stations with epicentral distances from 40 to 1070 km were processed. The following parameters of the epicenter were obtained: t0 = 18:39:07, 67.529N, 64.001E, h = 0 km, Rminor = 3.9 km, Rmajor = 6.7 km, AzMajor = 90°, Kp = 9.0, ML = 2.8, Ms = 2.35. The instrumental epicenter of the event is located in Vorkuta, near the mine field of the Vorkuta mine. We classified the seismic event as a rock burst. Macroseismic intensity calculated in accordance with the Seismic intensity scale-2017 using 57 questionnaires and 163 definitions of sensor categories was I0 = 4.73 ± 0.02. The calculated value of the macroseismic depth of the source was H = 0.5 ± 0.4 km, which corresponds to the instrumental definition and to the range of depths of mine workings of the Vorkuta coal deposit.

Research on the Laws of Overlying Rock Fracture and Energy Release under Different Mining Speeds

Mining activities are key triggers for strong mine earthquakes and even rock bursts in coal mines. This study explores the impact of mining speed on the overlying strata’s deformation and energy release through theoretical analysis, numerical simulation, and the digital speckle method. The temporal and spatial evolution characteristics of the impact energy during mining are simulated. The digital speckle method illustrates a positive correlation between rapid mining and increased fracture block degree of overburden rock and roof separation, confirming that accelerated mining speed extends the fracture distance of the stope. Furthermore, numerical simulations establish that both the energy associated with overlying rock breaking and the frequency of energy occurrence events are amplified during rapid mining, in contrast to slow mining. This observation corroborates that escalating mining speed augments the energy dispensed by the breaking of the upper rock. Consequently, this escalation induces a transformation in the energy levels of mine earthquakes, culminating in a heightened incidence of large-energy mine earthquakes.

Strength, Deformation and Fracture Properties of Hard Rocks Embedded with Tunnel-Shaped Openings Suffering from Dynamic Loads

In rock engineering, the dynamic loads caused by mechanical action and rock blasting have an extremely significant influence on the stableness of surrounding rock masses. To examine the impact of dynamic load on the mechanical properties and fracturing characteristics of hard rocks as well as the failure responses of underground openings, a number of prismatic samples with holes of different numbers and configurations were prepared for dynamic tests employing an SHPB loading device. The experimental results demonstrate that the order of dynamic compressive strength of each group of samples under the impact nitrogen pressure of 0.45 MPa is: G3 > G2 > G5 > G4 > G7 > G6, and the dynamic deformation process of the samples is parted into three phases: linear elastic deformation, plastic deformation and post-peak deformation. A total of three categories of cracks, i.e., spalling cracks, shear cracks and tensile cracks, occur in the specimens. The failure mode of the samples having one or two holes arranged in a vertical direction is controlled by shear cracks, whilst that of the rest groups of pre-holed specimens belongs to tensile-shear failure. The existence of the fabricated holes in the samples significantly weakens the mechanical properties and affects the fracture evolution characteristics, which rely on the quantity and layout of the cavities in the specimens. The interesting results are also discussed and explained, and could supply some insight in the mechanisms of tunnel surrounding rock failure and rock dynamic hazards such as rock burst.

Rockburst classification based on cross reconstruction learning under small-sample condition

Rockburst is a prevalent geological hazard in deep geotechnical engineering, and its accurate prognostication is vital for prevention measures. Consequently, this research proffers a pioneering classification prediction methodology, namely Cross Reconstruction Learning (CR), underpinned by conventional machine learning algorithms and metric learning strategies. Initially, this technique partitions and restructures the original dataset, where each sample feature intersects and reconfigures with features from other samples within the set. During this amalgamation, new samples are assigned labels based on the degree of divergence or congruity between two sets of sample labels, thereby forming a new set of samples. Subsequently, an array of machine learning algorithms is utilized to train and test this new dataset. Ultimately, employing a universal class voting mechanism and decoding test set results through probability assignment, the predicted labels are converted back into rock burst outcomes, thereby determining the final prediction classification. The proposed model was trained on a database encompassing 239 instance samples, and its performance was validated against the currently proficient models (KNN, XGBoost, and Random Forest algorithms) employed in rock burst prediction. The outcome revealed a decline in the performance metrics of all three machine learning algorithms when interfaced with the Cross Reconstruction learning method, particularly the KNN algorithm, owing to the doubled feature dimensions in the combined dataset. However, the metrics of ensemble models, XGBoost and Random Forest, exhibited a notable improvement compared to the original classification models. On comparing multiple performance metrics, it was discovered that the CR-XGBoost model outperformed others across all evaluations, thereby offering significant guidance for practical engineering applications.

Influence of orientation of the intermediate principal stress on fracture reactivation in granite

Fracture (fault) reactivation can lead to dynamic geological hazards including earthquakes, rock collapses, landslides, and rock bursts. True triaxial compression tests were conducted to analyze the fracture reactivation process under two different orientations of σ2, i.e. σ2 parallel to the fracture plane (Scheme 2) and σ2 cutting through the fracture plane (Scheme 3), under varying σ3 from 10 MPa to 40 MPa. The peak or fracture reactivation strength, deformation, failure mode, and post-peak mechanical behavior of intact (Scheme 1) and pre-fractured (Schemes 2 and 3) specimens were also compared. Results show that for intact specimens, the stress remains nearly constant in the residual sliding stage with no stick-slip, and the newly formed fracture surface only propagates along the σ2 direction when σ3 ranges from 10 MPa to 30 MPa, while it extends along both σ2 and σ3 directions when σ3 increases to 40 MPa; for the pre-fractured specimens, the fractures are usually reactivated under all the σ3 levels in Scheme 2, but fracture reactivation only occurs when σ3 is greater than 25 MPa in Scheme 3, below which new faulting traversing the original macro fracture occurs. In all the test schemes, both ε2 and ε3 experience an accumulative process of elongation, after which an abrupt change occurs at the point of the final failure; the degree of this change is dependent on the orientation of the new faulting or the slip direction of the original fracture, and it is generally more than 10 times larger in the slip direction of the original fracture than in the non-slip direction. Besides, the differential stress (peak stress) required for reactivation and the post-peak stress drop increase with increasing σ3. Post-peak stress drop and residual strength in Scheme 3 are generally greater than those in Scheme 2 at the same σ3 value. Our study clearly shows that intermediate principal stress orientation not only affects the fracture reactivation strength but also influences the slip deformation and failure modes. These new findings facilitate the mitigation of dynamic geological hazards associated with fracture and fault slip.

Roadway rock burst prediction based on catastrophe theory

In order to quantitatively calculate the critical depth and critical load of mines affected by rock burst, and to achieve effective prevention and control of rock burst in coal mines, this paper proposes a mechanical model for predicting the occurrence of rock burst in coal mine roadways based on catastrophe theory. Additionally, a theoretical calculation formula for initiating rock burst is derived. The first step was to establish a mechanical analysis model, which directly correlated with the in-situ stress, physical and mechanical characteristics of the coal-rock mass, and engineering structural parameters. Following this, a mechanical instability criterion was derived for the key load-bearing circle within the surrounding rock of the roadway. In the final step, the critical depth and load for rock burst initiation were verified for 25 distinct coal mines in China that were prone to rock burst hazards. The research results demonstrate that the discrepancy between the theoretically calculated critical depth and the actual measured statistical values was less than 35%. In addition, the difference between the theoretically determined critical depth and the value calculated by Pan Yishan was less than 32%. Notably, the ratio of the theoretically calculated critical load to the uniaxial compressive strength of the coal-rock mass ranged from 0.38 to 1.93. This aligns with empirical data on rock burst occurrences, as set out in the engineering classification standards for rock masses. These research outcomes substantiated the practical utility of the proposed theory, thereby laying a robust theoretical groundwork for the quantitative control of rock burst.

Propagation and impacts on roadway of mining-induced far-field strong tremors: insights from numerical simulations

Mining-induced far-field mine tremors, which often cause strong ground tremors, are receiving more attention due to their increasing occurrence. Investigating the rock burst risk of roadway caused by those tremors is crucial to ensure production safety. In this study, Variational Mode Decomposition was used to investigate the wave characteristics of strong mine tremors. The propagation and attenuation of these tremors were explored using the dynamic analysis of Flac3d. The amplification factor was introduced to assess the impact of these tremors on roadways. Plastic zone volume increment and Brittle Shear Ratio (BSR) are used to assess the roadway failure and the rock burst potential caused by these tremors, respectively. The main findings are as follows: 1) Compared with the main frequency of near-field mine tremor waveform, the far-field mine tremors waveform are mainly low frequency below 5 Hz; 2) In the simulation, peak vibration velocities of P-wave and S-wave follow a power-law decay as the propagation distance increases, with P-wave attenuating faster than S-wave; 3) Under similar conditions, P-wave induce higher vibration velocities than S-wave, but S-wave generally exhibit a greater amplification factor than P-wave; 4) When the direction of dynamic load is consistent with the direction of maximum principal stress, the rock burst potential of roadway is higher, which explains the phenomenon that the rock burst potential of roadway under S-wave loading is higher than that under P-wave.