Rockburst Risk Research Articles

Study on the rockburst risk of alternate exterior entry under island coal pillar

The extraction of extremely thick coal seams through slicing mining requires leaving behind several island coal pillars in the top slicing, which could lead to rockburst in the bottom slicing. This article proposed a method of alternate exterior entry (AEE) for rockburst prevention based on the stress distribution of bottom slicing. The influences of coal pillar width [Formula: see text] and stress concentration factor [Formula: see text] on stress distribution were explored using two-dimensional stress imaging. The results indicated that K value had a greater effect on stress distribution comparing with B value. Both stress relaxed and stress rate angles increased linearly with an increase in K value. In order to get the optimized location of AEE in the coal mine stress rate angle was chosen as the stress extension angle, which was further verified by field data from 1210 coal faces. Finally, numerical simulation was used to explore the optimized location of special tail entry under dip island coal pillars for rockburst prevention. It was revealed that plan first was found to be most effective due to its presence in a stress-relaxed area, thus verifying the effectiveness of the AEE method for rockburst prevention.

Comprehensive evaluation of rockburst risk by multiparameter characteristics based on microseismic signals: A case study

AbstractNowadays, the seismological monitoring system in China is a valuable tool in the rockburst risk evaluation for deep coal mines. In the past, only parameters, like source location and energy, are widely used to estimate the risk level of rockburst. Sometimes, it is effective; however, some other important physical parameters, such as apparent stress drop, static stress drop, P‐wave velocity, and moment tensor, should also be included in order to improve the accuracy of risk assessment. In this study, these parameters are calculated using mine tremor signals recorded in the LW35001 workface of Liangbaosi Coal Mine. These calculations provide an overall identification of periodical stress distribution and rock mass energy‐releasing type under high concentrated stress. Via linear moment tensor inversion procedure, the source mechanism of mine tremors and stress state of the rock mass is determined whether it is risk or not to underground roadway. This comprehensive analysis provides a specific guidance for rockburst prevention for coal mine management, that is, knowing when and where measures must be taken to decrease the risk level or induce the occurrence of rockburst under control.

Compound disaster characteristics of rock burst and coal spontaneous combustion in island mining face: A case study

The island mining face in coal mines often encounters stress concentration and significant air leakage, elevating the risk of rock burst and spontaneous combustion of residual coal in the goaf. This study centers on the investigation of the 9305 island mining face within a mine situated in Shandong Province, China. Leveraging the features of coal spontaneous combustion and data from microseismic and gas monitoring throughout the mining process, a novel method for calculating safe mining speeds under compound disasters is introduced. The safe mining speed of the 9305 island mining face is stratified, and a prevention and control technology integrating long distance directional drilling for impact-spontaneous combustion compound disasters is proposed. Findings suggest that to mitigate spontaneous combustion of residual coal, considering the seam's spontaneous combustion tendency, the safe mining speed should exceed 3.7 m per day. Accounting for the distribution of microseismic events during coal seam extraction, the safe mining speed is maintained below 4.8 m per day in the unprotected mining area and capped at 8 m per day in the protected zone. Through a comprehensive analysis considering coal seam spontaneous combustion tendency, impact tendency, and time to coal seam oxidation to critical temperature, the safe mining speed for the island mining face ranges from 3.7 m per day to 4.8 m per day in the unprotected area and from 5.14 m per day to 8 m per day in the protected area. The proposed long distance directional drilling 'one hole, multiple purposes' scheme enables an integrated approach encompassing pressure relief before coal seam extraction, water injection during mining, and grouting post-mining. This method effectively prevents and controls the compound disasters of rock burst and coal spontaneous combustion, presenting innovative technical solutions for the safe extraction of coal resources.

Failure characteristics and pressure relief effectiveness of non-persistent jointed rock mass with holes

In tunnel engineering, the rock mass contains a significant number of irregularly distributed joints, and typically exhibits high energy accumulation, thereby posing a risk of rockburst occurrence. Therefore, it is of paramount importance to investigate the fracture propagation behavior in jointed rock masses and assess the impact of borehole pressure relief on mitigating rockburst occurrences for effective prevention and control measures. This paper focuses on failure characteristics and pressure relief effectiveness of non-persistent jointed rock mass with holes through laboratory testing and numerical simulation. In laboratory experiments, rock samples are prepared to include a range of crack dip angles and circular holes. Then, the crack propagation law of crack inclination and circular hole is studied by AE and DIC technology. The experimental results show that with the increase of fracture dip angle, the peak strength and energy change of the sample decrease first and then increase. Due to the existence of holes, the crack propagation direction of the original crack is changed. After drilling, the strain energy of the sample is obviously reduced, which shows that the drilling pressure relief effect is obvious, which can effectively reduce the energy accumulated inside the rock mass and reduce the risk of rockburst. Finally, the PFC numerical simulation software is used to analyze the micro-failure process and energy change law of the sample from three aspects: the relative position of cracks and holes, the diameter of boreholes and the spacing of boreholes. Further understanding of the energy dissipation law and mechanical behavior characteristics of jointed rock mass provides a reference for exploring the pressure relief effect of rock mass and preventing rockburst.

Research Progress on the Mechanisms and Control Methods of Rockbursts under Water–Rock Interactions

Rock bursts are among the most severe and unpredictable hazards encountered in deep rock engineering, posing substantial threats to both construction safety and project progress. This study provides a comprehensive investigation into how moisture infiltration influences the propensity for rock bursts, aiming to establish new theoretical foundations and practical methods for their prevention. Through the analysis of meticulous laboratory mechanical experiments and sophisticated numerical simulations, we analyzed the variations in the physical and mechanical properties of rocks under different moisture conditions, with a particular focus on strength, brittleness, and energy release characteristics. The findings reveal that moisture infiltration significantly diminishes rock strength and reduces the likelihood of brittle fractures, thereby effectively mitigating the risk of rock bursts. Additionally, further research indicates that in high-moisture environments, the marked reduction in rock burst tendency is attributed to increased rock toughness and the suppression of crack propagation. This study advocates for the implementation of moisture control measures as a pre-treatment strategy for deep rock masses. This innovative approach presents a viable and effective solution to enhance engineering safety and improve construction efficiency, offering a practical method for managing rock burst risks in challenging environments.

Stress Evolution and Rock Burst Prevention in Triangle Coal Pillars under the Influence of Penetrating Faults: A Case Study

The occurrence of rock bursts due to penetrating faults are frequent in China, thereby limiting the safe production of coal mines. Based on the engineering background of a 501 working face in a TB coal mine, this paper investigates stress and energy evolution during the excavation of this working face due to multiple penetrating faults. Utilizing both theoretical analysis and numerical simulations, this study reveals the rock burst mechanism within the triangular coal pillar influenced by the penetrating faults. Based on the evolution of stress within the triangular coal pillar, a stress index has been devised to categorize both the rock burst danger regions and the levels of rock burst risks associated with the triangular coal pillar. Furthermore, targeted stress relief measures are proposed for various energy accumulation areas within the triangular coal pillar. The results demonstrate that: (1) the superimposed tectonic stress resulting from the T6 and T5 penetrating faults exhibits asymmetric distribution and has an influence range of about 90 m in the triangular coal pillar, reaching a peak value of 11.21 MPa at a distance of 13 m from the fault plane; (2) affected by the barrier effect of penetrating faults, the abutment stress of the working face is concentrated in the triangular coal pillar, and the magnitude of the abutment stress is positively and negatively correlated with the fault plane barrier effect and the width of the triangular coal pillar, respectively; (3) the exponential increase in abutment stress and tectonic stress as the width of the triangular coal pillar decreases leads to a high concentration of static stress, which induces pillar burst under the disturbance of dynamic stress from fault activation; (4) the numerical simulation shows that when the working face is 150 m away from the fault, the static stress and accumulated energy in the triangle coal pillar begins to rise, reaching the peak at 50 m away from the fault, which is consistent with the theoretical analysis; (5) the constructed stress index indicates that the triangular coal pillar exhibits moderate rock burst risks when its width is between 73 to 200 m, and exhibits high rock burst risks when the width is within 0 to 73 m. The energy accumulation pattern of the triangular coal pillar reveals that separate stress relief measures should be implemented within the ranges of 50 to 150 m and 0 to 50 m, respectively, in order to enhance the effectiveness of stress relief. Blasting stress relief measures for the roof and coal are proposed, and the effectiveness of these measures is subsequently verified.

Interaction characteristics between rock burst risk region and neighboring fractural plane: a numerical investigation based on moment tensor inversion and Kmeans++ clustering

Interaction characteristics between rock burst risk region and neighboring fractural plane: a numerical investigation based on moment tensor inversion and Kmeans++ clustering

Quantitative Assessment of Rock Burst Risk in Roadway Tunneling Considering Variation of Coal Mass Parameters

Quantitative Assessment of Rock Burst Risk in Roadway Tunneling Considering Variation of Coal Mass Parameters

Effect of super-high water materials backfilling on stress decrease and energy release during strip coal pillar mining: A case study

Mining strip coal pillars left due to strip mining is important to resource-exhausted coal mines in eastern China. Backfilling mining is an effective means to mine strip coal pillars, which could decrease high stress and high energy release. However, materials with super-high water content were not widely applied in deeply isolated coal pillar mining due to lower strength characteristics and durability. In the paper, taking panel c8301 as engineering background, theoretical analysis, numerical simulation, and field monitoring were used to study the feasibility and application effect of the super-high-water backfilling technology in deep isolated coal pillar mining. The results showed that: (1) Panel c8301 is a strip-filling working face with insufficient mining on both sides, and the mining of the panel will trigger the rebalancing of the overlying rock structure on both sides, which increases the rock burst risk. (2) Simulation results indicate that the super-high-water filling method, in comparison to the traditional caving method, significantly reduces peak stress and elastic energy from 112.3 MPa to 4.6 × 106 J to 79.2 MPa and 2.23 × 106 J, respectively. This represents reductions of 29.6 % and 51.5 %, effectively mitigating the impact of mining activities on overburden movement. (3) On-site measurement data confirmed that the measured equivalent mining height was 1.25 m. The total number of microseisms and the amount of released energy decreased significantly, More specifically, three big energy release instances (>1.8 × 105 J) were recorded during the first roof weighting stage and panel in square meters. Super-high-water filling technology has achieved remarkable results in the mining of strip coal pillars and has significant application prospects.

A self-supervision rockburst risk prediction algorithm based on automatic mining of rockburst prediction index features

A self-supervision rockburst risk prediction algorithm based on automatic mining of rockburst prediction index features

Applying machine learning approach in predicting short-term rockburst risks using microseismic information: a comparison of parametric and non-parametric models

Applying machine learning approach in predicting short-term rockburst risks using microseismic information: a comparison of parametric and non-parametric models

Influence of stress and geology on the most prone time of rockburst in drilling and blasting tunnel: 25 tunnel cases

Rockburst exhibits different occurrence time characteristics during drilling and blasting in tunnel excavation, posing challenges to the safe and efficient construction of tunnels. In this study, 25 tunnels with rockburst hazards were examined. By employing the clustering method, we analyzed the characteristics of the most prone time (MPT) for rockburst. Furthermore, we investigated the contribution degree and influence mechanism of stress and geological factors related to the MPT of rockburst. The outcomes revealed that distinct tunnels exhibit diverse rockburst-prone times, leading to varying hazards and losses. The higher the maximum principal stress and the angle between it and the tunnel axis, the earlier the rockburst occurs. Moreover, the MPT of rockburst is influenced by both lithological types, macro and micro rock structures. When the joint intersects with the maximum principal stress at a small angle, rockburst occurs earlier. The stress direction, UCS, attitude of the dominant joint, and stress magnitude stand out as the principal controlling factors. The findings of this research can serve as a basis for assessing the MPT of tunnel rockburst, timing rockburst risk control measures, and selecting appropriate mitigation strategies.

Fuzzy Cognitive Map for Evaluating Critical Factors Causing Rockbursts in Underground Construction: A Fundamental Study

The rockburst phenomenon in excavation endeavours reveals a multitude of complexities and obstacles that significantly impact both the technical and financial dimensions of project execution. Investigating critical rockburst factors in underground excavations is of considerable importance for addressing pivotal safety issues and operational complexities within the field of underground excavation projects. This research proposes an innovative approach based on an expert-based fuzzy cognitive map (FCM) framework, aiming to identify and prioritize the key critical rockburst factors prevalent in underground excavations and tunnelling. A tailored cognitive map of the parameters of problem was constructed, integrating 56 critical and critical factors meticulously curated by a team of seasoned managers, engineers, deputy managers, trainee engineers and assistant managers. The structured cognitive map was meticulously developed, considering the relative weights of the identified critical factors and their intricate interrelationships—all informed by the invaluable insights and expertise of seasoned engineers in the field. Subsequently, the cognitive map underwent a systematic solution process, whereby the causal relationships and influences amongst the identified critical factors were analysed and factored in. The outcomes of the comprehensive analysis unveiled several critical factors: lack of rockburst risk assessments, high in situ stress, presence of rock seams and weak layers, rock quality variations, and geological heterogeneity as the most paramount concerns demanding immediate attention and strategic intervention. By adopting the proposed FCM approach and leveraging the collective expertise of industry professionals, this research offers a robust and systematic framework for comprehensively assessing and addressing the key challenges associated with rockburst events in underground excavations and tunnelling projects, thereby fostering enhanced project performance and efficacy within the field.

Identification and prediction method for acoustic emission and electromagnetic radiation signals of rock burst based on deep learning

With the deep development of underground rock engineering, the threat of rock burst disasters is increasing. At present, the identification and prediction of rock burst mostly rely on the experience of field staff to determine the critical value and development trend, and there is a lack of efficient and intelligent methods for the utilization of massive data. Therefore, this paper constructs a rock burst signal recognition and prediction model based on deep learning methods to solve the above problems. In this paper, the acoustic emission (AE) and electromagnetic radiation (EMR) data of the site are first marked and input into the long-short-term memory-fully connected neural network model to realize the identification of rock burst danger signals. Then, the graph data of the AE and EMR sensor monitoring networks are constructed and input into the spatiotemporal graph convolutional network signal prediction model to predict future monitoring data. Finally, this paper uses the same dataset to compare and analyze several other commonly used deep learning models. The results show that the model constructed in this paper has the best performance in the identification and prediction of AE and EMR signals with rockburst risk. This study can provide theoretical reference for intelligent monitoring and early warning of rock burst in underground rock engineering.

Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion

In recent years, tunnel boring machines (TBMs) have become extensively utilized in underground engineering projects. However, as a prevalent type of dynamic geological hazard, rockburst poses a serious threat to the safety of personnel, equipment, and property. To ensure the safe advancement of TBMs, we integrate the data of microseismic monitoring, TBM excavation, and surrounding rock and establish multiple probabilistic assessment models of rockburst risk using probabilistic ensemble learning and quantum particle swarm optimization. In order to identify the most optimal model for geological engineers, a comprehensive evaluation system is devised based on metrics like accuracy, Cohen’s kappa, and F1-score. This system provides a thorough assessment of the models’ global and local generalization performance. The results indicate that the classification and regression tree-extremely randomized trees (CART-ERT) hybrid model achieves the highest score, demonstrating superior generalization performance with an accuracy of 93.06% and a Cohen’s kappa of 0.9074. Moreover, the F1-scores for no rockburst, low rockburst, moderate rockburst, and strong rockburst are 0.9412, 0.9444, 0.9189, and 0.9189, respectively. Based on the operational framework of the CART-ERT hybrid model, a user-friendly graphical user interface (GUI) system is developed. This significantly enhances the practicality and deployability of the model. Through application in a TBM diversion tunnel project in Xinjiang, the on-site early-warning accuracy of rockburst with the GUI system reaches 90%. Notably, the GUI system maintains high early-warning sensitivity and reliability for high-intensity rockburst such as moderate and strong ones. Lastly, to enhance the model’s interpretability, a variable importance measure (VIM) analysis on 14 features extracted from three types of heterogeneous data is conducted to assess their contributions to the early warning of rockburst. We discover that the cumulative energy change rate of microseismic events exhibits the highest importance score, indicating its crucial role in rockburst early warning.

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

Microseismic data are widely employed for assessing rockburst risks; however, significant disparities exist in the monitoring capabilities of seismic networks across different mines, and none can capture a complete dataset of microseismic events. Such differences introduce unfairness when applying the same methodologies to evaluate rockburst risks in various mines. This paper proposes a method for assessing the monitoring capability of seismic networks applicable to heterogeneous media in mines. It achieves this by integrating three gradient boosting algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). Initially, the isolation forest algorithm is utilized for preliminary data cleansing, and feature engineering is constructed based on the relative locations of event occurrences to monitoring stations and the working face. Subsequently, the optimal hyperparameters for three models are searched for using 8508 microseismic events from the a Coal Mine in eastern China as samples, and 18 sub-models are trained. Model weights are then determined based on the performance metrics of different algorithms, and an ensemble model is created to predict the monitoring capability of the network. The model demonstrated excellent performance on the training and test sets, achieving log loss, accuracy, and recall scores of 7.13, 0.81, and 0.76 and 6.99, 0.80, and 0.77, respectively. Finally, the method proposed in this study was compared with traditional approaches. The results indicated that, under the same conditions, the proposed method calculated the monitoring capability of the key areas to be 11% lower than that of the traditional methods. The reasons for the differences between these methods were identified and partially explained.

Novel rockburst prediction criterion with enhanced explainability employing CatBoost and nature-inspired metaheuristic technique

Rockburst is a major challenge to hard rock engineering at great depth. Accurate and timely assessment of rockburst risk can avoid unnecessary casualties and property losses. Despite the existence of various methods for rockburst assessment, there remains an urgent need for a comprehensive and reliable criterion that is easy to both apply and interpret. Developing a new rockburst criterion based on simple parameters can potentially fill this gap. With its advantages, this criterion can facilitate a more effective and efficient prediction of rockburst potential, thereby contributing significantly to enhancing safety measures. In this paper, combined with the internal and external factors of rockburst, four control variables (i.e., integrity index, stress index, brittleness index, and elastic energy index) were selected to be incorporated into a comprehensive rockburstability index (RBSI). Based on 116 sets of rockburst cases, the rockburst potential was accurately quantified and predicted using the categorical boosting (CatBoost) model and the nature-inspired metaheuristic African vultures optimization algorithm (AVOA). In its performance validation, the criterion achieved the highest accuracy of 90.48%, verifying the reliability and effectiveness of the proposed RBSI criterion. Additionally, an interpretive method was applied to analyze the variable influence on the criterion, facilitating the explanation of predictions and the analysis of the formula’s robustness under different conditions. In general, compared with existing criterion methods involving relevant indicators, the newly proposed RBSI criterion enhances the accuracy of rockburst potential prediction, and it can effectively and swiftly evaluate the preliminary risk of rockburst. Lastly, a graphical user interface was developed to provide a clear visualization of the assessment of rockburst potential.

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.

Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach

Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach

Microseismic source location using deep learning: A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks. However, the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy. Intelligent techniques, such as the full convolutional neural network (FCNN), can capture spatial information but struggle with complex microseismic sequence. Combining the FCNN with the long short-term memory (LSTM) network enables better time-series signal classification by integrating multi-scale information and is therefore suitable for waveform location. The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction. In this study, we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus. Initially, the method of short-time-average/long-time-average (STA/LTA) arrival time picking was employed to augment spatiotemporal information. Subsequently, oversampling the on-site data was performed to address the issue of data imbalance, and finally, the performance of LSTM-FCNN was tested. Meanwhile, we compared the LSTM-FCNN model with previous deep-learning models. Our results demonstrated remarkable location capabilities with a mean absolute error (MAE) of only 7.16 m. The model can realize swift training and high accuracy, thereby significantly improving risk warning of rockbursts.