Rockburst Phenomena Research Articles

Analysis of the Coal Fluidization Mining Process with the Continuous-Discontinuous Coupled Particle-Block Method

In situ fluidization mining is a promising method which shows considerable potential for reducing the ecological influence impacts of coal mining. During fluidization mining, coal gangue will be separated from the ores, filled back, and returned to the excavation region, which, depending on the filling rate, greatly reduces the stratum deformation of strata, depending on the filling rate. Numerical simulations can assist engineers in evaluating stratum deformation and optimizing affordable designs with affordable costs. Accordingly, in this work, a continuous–discontinuous element method is adopted, which is a coupled particle–block finite-discrete element method with an explicit integration formulation, which is adopted for simulating the coal in situ fluidization mining–filling processes. The theory concept of an “equivalent mining height” is utilized to define the backfill mining height. The results partly reveal the influences of the filling rate on the stratum deformation, surface subsidence, and rock burst phenomena, all of which further indicate the advantages of the fluidization mining method.

Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. The output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen's kappa index. The applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real‐world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction.

Numerical Simulation of Impact Rockburst of Elliptical Caverns with Different Axial Ratios

Herein, a finite discrete element method was used to simulate the rockburst phenomenon of elliptical caverns with different axis ratios. Two situations were employed, namely when the disturbance direction is perpendicular and parallel to the ellipse. Based on the peak stress, maximum velocity, stress nephogram, and image fractal characteristics, the influence of axis ratio and direction of the disturbance on rockburst were analyzed. The results show that the samples with different axis ratios experienced the same process of quiet period, slab cracking period, and rockburst. The rockburst pit had V shape, and the failure modes of rockburst primarily included shear cracks, horizontal tension cracks, and vertical tension cracks. With the rise in axis ratio, the peak stress and maximum speed increased. Furthermore, the pressure area on the left and right sides of the sample cavern decreased when the disturbance direction was parallel to the short axis of the ellipse, while it increased for the sample with a disturbance direction perpendicular to the short axis. The fractal dimension value of the crack was gradually amplified with disturbance. The fractal dimension value of the sample whose disturbance direction was perpendicular to the minor axis of the ellipse was lower, and it was more difficult to damage.

Experimental and Numerical Simulation of Coal Rock Impact Energy Index Based on Peridynamics

In response to the increasing severity of the rock burst phenomenon and its relatively difficult prediction, peridynamics and indoor uniaxial compression experiments were used to calculate the changes of the internal elastic energy (t) and impact energy (c) for different rock masses during a loading process from an energy perspective. Two traditional indices for judging rock burst tendency—the rock elastic deformation energy index (WET) and the rock impact energy index (WCF)—were combined to define a new actual impact energy index (W) to more accurately determine the occurrence tendency of rock bursts. The LAMMPS software was used to simulate the internal energy changes of rock materials under pressure, and the results were compared with experimental results for verification. The results were as follows: (1) in the uniaxial compression experiments of different specimens, fine sandstone had the strongest impact resistance, followed by coarse sandstone, mudstone, bottom coal seam, and top coal seam, and the obtained material properties provide a reference for predicting the rock bursts of various rock types in practical engineering. (2) The values calculated using the actual impact energy index (W) and the simulation value were 1.75 and 1.77, respectively, which corresponded to a lower error than when the rock impact energy index (WCF) and the rock elastic deformation energy index (WET) were used individually. Thus, this index can better predict the rock burst. (3) The simulated specimen was subjected to a gradual increase in the internal stored elastic energy during compression, which gradually decreased after the ultimate compressive strength was exceeded. The degree of impact damage formed after macroscopic crushing occurred depended on its residual energy.

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

Failure behavior and acoustic emission characteristics of shale after subjection to high temperature

Uniaxial compression tests and acoustic emission (AE) monitoring on shale specimens with horizontal bedding planes and perpendicular bedding planes after subjection to high temperatures (25°C, 100°C, 150°C and 200°C) were performed to investigate the effect of high-temperature environment on shale in rock engineering. Results show that specimens in the two bedding directions experienced severe rockburst failure, and the increasing temperature made rockburst failure more intense. The rockburst phenomena for all the specimens were similar and consistent, including spalling, particle ejection and block collapse. Temperature had no significant effect on the failure mode of specimens with perpendicular bedding planes, while specimens with horizontal bedding planes turned to splitting with the increasing temperature. Fractal dimension of fragmentation increased with the increasing temperature, and that for specimens with perpendicular bedding planes increased much more than that for specimens with horizontal bedding planes. Before the peak, the cumulative AE counts can be divided into three stages by stress level of 40% and 80%.

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

One of the main challenges that deep mining faces is the occurrence of rockburst phenomena. Rockburst prediction with the use of machine learning (ML) is currently gaining attention, as its prognosis capability in many cases outperforms widely used empirical approaches. However, the required data for conducting any analysis are limited, while also having imbalances in their recorded instances associated with rockburst intensities. These, combined with the multiparametric nature of the phenomenon, can deteriorate the performance of the ML algorithms. This study focuses on the enhancement of the prediction performance of ML algorithms by utilizing the oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE). Five ML algorithms, namely Decision Trees, Naïve Bayes, K-Nearest Neighbor, Random Forest and Logistic Regression, were used in a series of parametric analyses considering different combinations of input parameters, such as the maximum tangential stress, the uniaxial compressive and tensile strength, the stress coefficient, two brittleness coefficients and the elastic energy index. All models kept their hyperparameters fixed, and were trained with the initial dataset, in which synthetic instances were added gradually aiming in the attenuation of a balanced dataset and its further expansion, until the number of synthetic instances reached the number of real data. The assessment of the SMOTE technique is given and its performance is evaluated though the different strategies adopted. The results indicate that SMOTE has a considerable positive effect in the accuracy of the overall classification and especially in the improvement of the within-class classification accuracy, even after the balancing of the dataset.

Integrated rockburst early warning model based on fuzzy comprehensive evaluation method

The development of reliable early rockburst warning models for underground mines is a challenging task considering the complex nature of rockburst phenomena. In this paper, a real-time, integrated multi-system, early rockburst warning model is developed to achieve the quantitative prediction of the rockburst probabilities at a specific time. The model input parameters are data derived from microseismic (MS) and acoustic emission (AE) monitoring systems. The early warning model is based on fuzzy comprehensive evaluation method (FCEM), confusion matrix and maximum membership degree principle. These tools integrate the advantages of both multi-system and multi-parameter into one unified model. During the early warning procedure, six early warning indices representing temporal, spatial and energy parameters are rationalized. Next, the weight of each index is determined from confusion matrix leading to a score value. Real-time early warning results can then be determined from the maximum membership degree principle. The results show that the proposed integrated MS-AE early warning model has the advantage of combining multiple systems and indices, and it has higher prediction accuracy than individual systems. The early warning model is successfully demonstrated with a case study from the Wudong Coal Mine in China. The proposed model improves the rockburst early warning efficiency and provides a solid foundation for ensuring the safety of personnel and equipment in underground mining.

Prediction and Assessment of Rock Burst Using Various Meta-heuristic Approaches

One of the utmost severe mining catastrophes in underground hard rock mines is rock burst phenomena. It can lead to damage to mine openings and equipment as well as trigger accidents or even threat to life as well. Due to this, a number of researchers are forced to study some easy-to-use alternative methods to predict the rock burst occurrence. Nevertheless, due to the extremely multifaceted relation between mechanical, geological and geometric factors of the mines, the conventional prediction methods are not able to produce accurate results. With the expansion of machine learning methods, a revolution in the rock burst occurrence has become imaginable. In present study, three machine learning methods, namely XGBoost, decision tree and support vector machine, are utilized to predict the occurrence of rock burst in various underground projects. A total of 134 rock burst events were gathered together from various published literatures comprising maximum tangential stress (MTS), elastic energy index (EEI), uniaxial compressive strength and uniaxial tensile stress (UTS) that have been used to develop various machine learning models. The performance of machine learning methods is evaluated based on the accuracy, sensitivity and specificity of the rock burst prediction.

Fuzzy Mathematics Comprehensive Forecasting Analysis of Metal Mine Rockburst Based on Multiple Criteriahere

The underground environment in deep mines is complicated and the risk of rockburst is high during excavation. Therefore, it is of great significance to study the tendency of rockburst during excavation. At present, scholars from various countries have analyzed rockburst phenomena from the aspects of strength, stiffness, energy, etc., and have proposed various assumptions and criteria. However, these assumptions and criteria often only consider individual factors, which will lead to one-sidedness and limitations. This article takes the deep mining process of a metal mine as the research background. Based on the results of each independent criterion, fuzzy mathematical theory is introduced to evaluate the rockburst intensity and fuzzy comprehensiveness of various types of rocks in the project. The research results show that the multi-criteria comprehensive evaluation of fuzzy mathematics can well integrate the analysis results of each criterion, and has higher consistency with the actual situation in engineering.

Numerical simulation of the possibility of seismic event occurrence in hard rock mine

This article presents the problem of seismic and rockburst hazard in underground hard rock mines. Types of rockbursts and their mechanisms as well as the causes of dynamic phenomena are discussed. The possibility of using numerical modeling to simulate the occurrence of a high-energy tremor, as a result of sudden shear rupture, is also shown. Results of numerical simulations are presented for a model mining field in a hard rock mine at high depth, in which the deposit is mined in a room-and-pillar mining system. General geological and mining conditions characteristic for Polish underground copper mines in Legnica-Glogow Copper Mining District belongs to KGHM Polska Miedz S.A. were adopted in the models. The typical lithological cross-section of the polish copper mines is characterized by rigid, high-strength rock layers in the roof, capable of accumulating elastic energy, while layers with much lower strength parameters are present in the floor. In the analyzed mining field, the remaining undisturbed rock, with a width of 40 m, was left. Numerical simulations were conducted in a plane strain state by means of Phase2 v. 8.0 software. An elastic-plastic model with softening was accepted for the rock mass. The results of numerical modeling showed that sudden (one computational step in the model) collapse of roof strata may occur over the excavated space on the edge of the remaining undisturbed rock, as a result of exceedance of shear strength, above all. This may cause a very high-energy tremor, and under the appropriate conditions, may result in the rockburst phenomenon.

Experimental study on the strain rockburst of calcareous sandstone containing joint surface

n order to study the failure law of rockburst phenomenon which happened frequently at rock mass with joints in deep fault zone of XinJiang water delivery tunnel. The field prediction and prevention of rockburst are guided, so that the construction process can be carried out safely and efficiently. The rockburst test of intact rock specimens and jointed rock samples was carried out by the modified true-triaxial apparatus (MTTA). Firstly, the high-speed photography and the acoustic emission (AE) system were applied to record the experimental process. The crack propagation and failure process were recorded and the acoustic emission signals of this process were analyzed. At the same time, the acoustic emission signals during crack propagation and failure are analyzed by wavelet analysis and fast Fourier transform (FFT). Then, the numerical simulation software named PFC was adopted to simulate the whole process of crack propagation in the rockburst of two kinds of specimens. And the simulation result is consistent with the experimental result. Some conclusions were drawn as follows: for calcareous sandstone with uniaxial compressive strength of 135.7 MPa, the rockburst of complete rock sample occurred at 129.6 MPa, and the joint rockburst occurred at 82.42 MPa. The more sudden time of joint rockburst was less than 0.1 s. During experiments, the main frequency band of complete sample is 62.5–250 MHz, while the main frequency band of jointed rockburst is lower and more concentrated at 62.5–125 MHz. The normalized energy proportion at the time of rockburst is focused at frequency part (62.5–250 MHz), and both types of rockburst energy exceed 80%, this signal can be used to judge the stage of stress adjustment in rocks. Therefore, it is considered that the existence of joint plane reduces the critical strength of rockburst and makes rockburst occur in a lower stress state. The distribution of the main frequency band can judge the extent of rock damage and help to predict rockburst disaster.

Data-Driven Model for Rockburst Prediction

Rockburst is an extremely complex dynamic instability phenomenon for rock engineering. Due to the complex and unclear mechanism of rockburst, it is difficult to predict precisely and evaluate reasonably the potential of rockburst. With the development of data science and increasing of case history from rock engineering, the data-driven method provides a good way to mine the complex phenomenon of rockburst and then was used to predict the potential of rockburst. In this study, deep learning was adopted to build the data-driven model of rockburst prediction based on the rockburst datasets collected from the literature. The data-driven model was built based on a convolutional neural network (CNN) and compared with the traditional neural network. The results show that the data-driven model can effectively mine the complex phenomenon and mechanism of rockburst. And the proposed method not only can predict the rank of rockburst but also can compute the probability of rockburst for each corresponding rank. It provides a promising and reasonable approach to predict or evaluate the rockburst.

Quantitative analysis of rockburst in the surrounding rock masses around deep tunnels

Rockbursts in deep tunnels subjected to high crustal stress were investigated using a general particle dynamics (GPD) code with the Holmquist–Johnson–Cook damage model (HJC). Under dynamic excavation disturbance, several numerical tests were carried out to investigate the dynamic failure patterns of surrounding rock masses with different preexisting cracks around deep tunnels. The numerical results showed that the GPD code can satisfactorily simulate the phenomenon of rockburst caused by crack evolution, including predicting the ejection velocity, rockburst location and ejected rock block volume. Moreover, the analysis of the circumferential stress in the surrounding rock of deep tunnels at the Jinping II Hydropower Station shows the efficiency of the GPD code. In addition, four typical rockburst, which occurred under different geological conditions at the Jinping II Hydropower Station, were analyzed by using the GPD code. The numerical simulations suggested that the GPD code can efficiently reproduce the 2D and 3D crack growth in the surrounding rock, which eventually leads to the occurrence of rockburst.

Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the rockburst risk in deep mines and tunnels and factors effective on this phenomenon. The model was established and validated through the use of a data set extracted from previously conducted studies. The data set involves a total of 196 reliable rockburst cases. The use of smart systems was used to classify and determine patterns in this research using model development. The hybrid FA–ANN model provides a solution for determining different classes of hazard under different conditions. The capability of these developed systems was implemented to determine the four types of levels defined for this phenomenon. The results of these systems led to new solutions to classify this phenomenon by success rates. Each system, given its performance, yields a unique error. Finally, by combining the number of correctly classified classes and their error values, the success rates in the classification of rockburst phenomena in mines and underground tunnels were evaluated.

Machine learning methods for rockburst prediction-state-of-the-art review

One of the most serious mining disasters in underground mines is rockburst phenomena. They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment. This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence. However, due to the highly complex relation between geological, mechanical and geometric parameters of the mining environment, the traditional mechanics-based prediction methods do not always yield precise results. With the emergence of machine learning methods, a breakthrough in the prediction of rockburst occurrence has become possible in recent years. This paper presents a state-of-the-art review of various applications of machine learning methods for the prediction of rockburst potential. First, existing rockburst prediction methods are introduced, and the limitations of such methods are highlighted. A brief overview of typical machine learning methods and their main features as predictive tools is then presented. The current applications of machine learning models in rockburst prediction are surveyed, with related mechanisms, technical details and performance analysis.

Static and Dynamic Influence of the Shear Zone on Rockburst Occurrence in the Headrace Tunnel of the Neelum Jhelum Hydropower Project, Pakistan

Rockburst is an unstable failure of a rock mass which is influenced by many factors. During deep excavations, the presence of nearby geological structures such as minor faults, joints, and shear zones increases the likelihood of rockburst occurrence. A shear zone has been observed in the headrace tunnel in the Neelum Jhelum Hydropower Project, Pakistan, which has played an important role in major rockburst events in the project’s history. A rockburst is a seismic event that involves the release of a great amount of energy as the dynamic wave radiated from the seismic source reaches the excavation boundary. In this paper, the FLAC 2D explicit numerical code has been used to simulate the dynamic phenomenon of rockburst near the shear zone in a headrace tunnel. The behavior of the rock mass around the tunnel has been studied under both static and dynamic loading. According to modeling results, rockburst significantly affected the upper left quadrant of the tunnel similar to the actual failure profile with a depth of approximately 5 m. The dynamic impact of rockburst has also affected the loading conditions of the support system in the adjacent tunnel. This study elucidates one of the most important rockburst controlling factors through numerical analysis and recommends yielding support measures that can withstand the dynamic impacts of rockburst in deep, hard rock tunnels.

Dynamic buckling mechanism of pillar rockbursts induced by stress waves

Rockbursts are sudden and violent rock failures that can lead to huge production and equipment losses, injury or death of mining workers. Buckling has been regarded as one of the key mechanisms of rockbursts, which are often induced by dynamic loads from mining excavations, such as drilling and blasting in underground mining. The paper attempts to investigate the dynamic buckling mechanism of pillar rockbursts in underground mining, by considering rockbursts as a dynamic stability problem of underground rock structures. The results include: (1) A new explanation of the “sudden and violent” phenomenon of rockbursts, characterized by exponential growth of the amplitudes of transverse displacement responses, even in the presence of rock damping; (2) Identification of the critical role in inducing rockbursts of dynamic loads that bear frequencies approximately double the natural pillar frequency; (3) The greater influence on rockburst occurrence of the amplitude of dynamic component relative to the static component of loads; and (4) Quantification of the relative effects of stress waveform of dynamic loads on pillar rockbursts, which are in decreasing order if other parameters remain constant: rectangular, sinusoidal, and exponential waveforms. Application examples are provided and limitations of the approach are discussed. This research is motivated by the on-going and ubiquitous occurrence of rockbursts in underground excavations all around the world. In contrast to conventional methods that use rock specimens or rock materials to study rockbursts, this investigation emphasizes the structural effects on rockbursts, which has potential applications in hard rock mining engineering.

Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach

Based on reported statistics, rockburst phenomenon is the main cause of many casualties and accidents occurred during the construction of deep underground structures. Therefore, its prediction in initial stages of design has a remarkable role on enhancement of safety. In this paper, two models have been developed for rockburst evaluation using the C5.0 decision tree classifier. The first model has been applied for prediction of rockburst occurrence and the second model for prediction of rockburst intensity. These models have been developed based on a database including 174 rockburst case histories. In both models, stress coefficient, rock brittleness coefficient, and the elastic strain energy index are the predictive variables. These models are easy to use and do not require extensive knowledge. Based on decision rules derived from these models, the rockburst occurrence and intensity can be evaluated easily. The results revealed that the proposed approach is a useful and robust technique for long-term prediction of rockburst.

Application of Seismic Monitoring and Numerical Modelling in the Assessment of Possibility of Seismic Event Occurrence in the Vicinity of Ore Remnant

Seismic and rockburst hazard is one of the basic problems associated with deposit exploitation in many underground mines. Rockbursts are responsible for many mining accidents, and their effects, which include damaged excavations, destroyed equipment and machinery, generate financial losses and disrupt the operational continuity of the mining facility. Dynamic phenomena occurrence is one of the major natural hazards in Polish underground copper mines in the Legnica-Glogow Copper Belt (LGCB). The degree of seismic hazard in the LGCB results, among others, from the great depth of the copper deposit exploitation, high-strength rock layers in the roof strata and the ability of rock mass to accumulate elastic energy, as well as from an increasing amount of mining works to be carried out in difficult geological and mining conditions, for instance in the vicinity of remnants. The purpose of this paper is to show the influence of ore remnant on the possibility of seismic event occurrence by seismic activity analysis and numerical modelling. The possibility of using numerical modelling to back-calculate the occurrence of a seismic event due to sudden shear rupture is also presented. Analyses were conducted for the case study of the Polkowice-Sieroszowice Polish underground copper mine for the room-and-pillar mining system with room deflection. For the selected mining field, quantitative analysis of seismic activity was performed in connection with the assessment of the mining situation in this field. The location of tremor epicentres in the context of the existing geological and mining situation was also analysed, with special attention paid to the impact of remnants and dynamic phenomena in their vicinity. Subsequent investigations focused on the back analysis of deposit exploitation by room and pillar mining system with roof deflection in the selected mining field. Numerical simulations were conducted in a plane deformation state by means of Phase2 v. 8.0 software, which is based on the finite element method. The results of seismic and numerical analyses show that undisturbed rock remnants may have a negative impact on the seismic and rockburst hazard in the mining field. The analyses also show that on the edge of a rigid remnant, sudden fracturing of roof strata may occur, as a result of exceedance of shear strength (shear rapture). This may cause a high-energy mining tremor, and under appropriate conditions, may result in a rockburst phenomenon.