Rockburst Risk Research Articles

Support Optimization of Open TBM Tunneling in Luohe Formation Sandstone by CT Test and Numerical Simulation

As underground engineering extends into the western and deeper regions of China, more and more Luohe Formation sandstone layers will be encountered, which have weak cementation and high water content. It is a significant challenge to apply the open TBM, and the support system is crucial in determining whether TBM can excavate quickly and safely. Therefore, in order to optimize the support scheme, this paper analyzes the pore structure and porosity through CT scanning, the results indicate that the volume percentage of pores ≥34 μm is 2.3% and 1.5%, respectively, the large pore apertures are predominant, the surrounding rock has strong permeability, and there is a high risk of rock burst and roof collapse accidents, hence requiring reinforced support measures. On this basis, numerical simulations were conducted to evaluate the support effectiveness. The results show that replacing the “bolt + mesh” with a “bolt + cable + mesh + steel belt”, and replacing the top three bolts with 7.3 m anchor cables, can better control the deformation and provide sufficient thrust force for the TBM, ensuring excavation speed. After the implementation of this scheme at the Kekegai coal mine in Shaanxi, China, the TBM excavation speed increased by 70%, from the previous 10 m/day to 17 m/d, significantly reducing the project duration and construction costs.

Mechanism of rock burst in deep gob-side entry based on dynamic and static stress: a case study

Deep gob-side entry (DGSE) shows rock burst risk under the condition of kilometer depth and hard roof. Based on the established conditions of the Tengdong coal mine, theoretical analysis, microseismic (MS) monitoring, and numerical modeling were used to study the rock burst mechanism of DGSE. Results show that MS events mainly occurred on the solid side of DGSE and the intense dynamic load was mainly caused by the breaking of low hard roof strata, which can release 2.64 × 105 J elastic energy per meter. The surrounding stress of DGSE was asymmetrical due to the coal pillar yielding and hanging roof’s weight, and the load of coal pillar is negatively correlated with that of solid. Simulation shows the vertical stress evolution of coal pillar and solid shows significant diversity. Coal pillar’s vertical stress first drops sharply, and then increases gradually, finally stable at 10.6 MPa with the DGSE’s excavation. Contrarily, solid’s vertical stress gradually rises and was finally stable at 40.9 MPa. Under roof dynamic loading, the vibration velocity of the entry’s top was higher than that of the floor which was caused by the increase of the propagation distance and the reflection and diffraction effect of waves. The vibration velocity of the coal pillar was significantly higher than that of the solid which is because higher stress can lead to faster attenuation of vibration velocity. After dynamic loading, the coal pillar’s principal stress and principal stress difference decreased while that of the solid can be divided into two drop areas and one rising area. Periodic pressure relief that was carried out in the rising area can reduce the rock burst risk on the solid side of DGSE.

A Method for Dividing Rockburst Risk Zones—A Case Study of the Hegang Mining Area in China

Rockbursts are serious mine disasters. Through the division of rockburst risk zones, risks can be predicted in advance and measures can be implemented to prevent disasters. In this paper, taking the Hegang mining area as an example, we propose a method for dividing rockburst risk zones based on in-situ stress measurements. First, 24 survey points were established in the Hegang mining area to measure the in-situ stress. Second, based on the in-situ stress measurement data, eight representative prospecting lines were selected to establish a numerical model, and the distribution characteristics of the in-situ stress field at mining elevations of −330 m and −450 m in the Hegang mining area were obtained via the linear differential method. Afterward, division criteria for rockburst risk zones were proposed in accordance with the energy criterion and the minimum energy principle. Finally, the Hegang mining area was divided into rockburst risk zones in accordance with the in-situ microseismic monitoring data and simulation results for the in-situ stress field. Coal seam #3 was chosen as an example to illustrate rockburst risk-zone division in the Hegang mining area considering the division criterion proposed herein, and a rockburst risk zoning map of coal seam #3 in the Hegang mining area was finally obtained. The locations of the five rockbursts that have occurred in coal seam #3 of the Hegang mining area were marked on the risk zoning map and were found to have occurred in the threatened zone. Thus, it was shown that the proposed rockburst risk-zone division method is reasonable. Therefore, the results of this study could serve as a reference for the division of rockburst risk zones.

Rockburst proneness considering energy characteristics and sample shape effects

Accurate prediction of rockburst proneness is one of challenges for assessing the rockburst risk and selecting effective control measures. This study aims to assess rockburst proneness by considering the energy characteristics and qualitative information during rock failure. Several representative rock types in cylindrical and cuboidal sample shapes were tested under uniaxial compression conditions and the failure progress was detected by a high-speed camera. The far-field ejection mass ratio (FEMR) was determined considering the qualitative failure information of the rock samples. The peak-strength energy impact index and the residual elastic energy index were used to quantitatively evaluate the rockburst proneness of both cylindrical and cuboidal samples. Further, the performance of these two indices was analyzed by comparing their estimates with the FEMR. The results show that the accuracy of the residual elastic energy index is significantly higher than that of the peak-strength energy impact index. The residual elastic energy index and the FEMR are in good agreement for both cylindrical and cuboidal rock materials. This is because these two indices can essentially reflect the common energy release mechanism characterized by the mass, ejection velocity, and ejection distance of rock fragments. It suggests that both the FEMR and the residual elastic energy index can be used to accurately measure the rockburst proneness of cylindrical and cuboidal samples based on uniaxial compression test.

Study of the Multilevel Fuzzy Comprehensive Evaluation of Rock Burst Risk

Rock burst is a multifaceted phenomenon that involves various intricate factors. A precise evaluation of its risk encounters numerous challenges. To address this issue, the present paper proposed a multilevel fuzzy comprehensive evaluation model based on the Analytic Hierarchy Process–Fuzzy Comprehensive Evaluation (AHP-FCE) method. Three primary influencing factors and twelve secondary influencing factors that impact the rock burst risk were identified. The mechanisms by which each influencing factor affects the rock burst were analyzed and the membership degree for each factor was calculated accordingly. The weight of each influencing factor was determined through the AHP. To obtain a quantitative evaluation result, the evaluation model was calculated using the second-order fuzzy mathematics calculation method. The application of the model was demonstrated on the 310 working face of the Tingnan Coal Mine, and the evaluation results were consistent with those achieved through the use of the comprehensive index method and the probability index method. All of the results exhibited consistent alignment with the actual circumstances. The verification process confirmed the scientific, effective, and practical nature of the model.

Influence of Key Strata on the Evolution Law of Mining-Induced Stress in the Working Face under Deep and Large-Scale Mining

When there are multiple key strata in the overburden of a deep coal seam and the surface subsidence coefficient after mining is small, this indicates that the overlying key strata fail to break completely after mining. On this occasion, stress is easily concentrated in the working face, which in turn leads to the occurrence of dynamic disasters such as rock bursts. This study adopted a comprehensive analysis method of field monitoring and numerical simulations to explore the influence of the key stratum on the evolution law of mining-induced stress in the working face. A distributed optical fiber sensor (DOFS) and a surface subsidence GNSS monitoring system were arranged inside and at the mouth of the ground observation borehole, respectively. According to the monitoring results of strain obtained from the DOFS, the height of the broken stratum inside the overlying strata was obtained and according to the monitoring results of surface subsidence, the surface subsidence coefficient was proven to be less than 0.1, indicating that the high key stratum is not broken completely, but enters a state of bending subsidence instead. In order to reveal the influence of the key stratum on the mining-induced stress of the working face, two 3DEC numerical models with and without the key stratum were established for a comparative analysis. As the numerical simulation results show, when there are multiple key strata in the overburden, the stress influence range and the stress concentration coefficient of the coal seam after mining are relatively large. The study revealed the working mechanism of rock burst accidents after large-scale mining and predicted the potential area with a rock burst risk after mining of the working face, which was verified by field investigations. The research results are of great guiding significance for the identification of the working mechanism of rock bursts in deep mining condition and for their prevention and control.

Automatic recognition of effective and interference signals based on machine learning: A case study of acoustic emission and electromagnetic radiation

Integrated monitoring technology using acoustic emission (AE) and electromagnetic radiation (EMR) is a promising mean for monitoring coal and rock dynamic disasters. However, due to the complex underground environment, blasting, drilling and mechanical operations may generate interference signals that affect the accuracy of early warning. It is essential to study the automatic recognition of effective and interference signals for AE and EMR. For this reason, a field test of synchronous AE-EMR monitoring was conducted in a coal mine. Further, the time domain, frequency domain and fractal characteristics of effective and interference signals for AE and EMR were analyzed, and sensitive characteristics were obtained by the Relief algorithm. Based on this, automatic recognition models of effective and interference signals were established by Fisher's linear discriminant method, support vector machine and ensemble learning method, respectively. Field application showed that support vector machine performed higher recognition accuracy. The AE and EMR warning indicators were obtained based on the signal recognition model, and the results show that the indicators reflect the risk of rock burst more accurately and earlier than the original indicators before the removal of interference signals.

Comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation signals of rock burst based on deep learning

Microseismic, acoustic emission, and electromagnetic radiation monitoring methods are often used to monitor rock burst disasters in coal mines. In the process of coal mining, the time series characteristics and amplitude characteristics of microseismic, acoustic emission, and electromagnetic radiation data are mainly used to identify rockburst risk, but the results of risk identification through the three monitoring methods are quite different. Consequently, the accurate and comprehensive early warning of rock burst risk is still an urgent problem to be solved. The development of deep learning provides a new means for intelligent early warning of rock burst risk. In this paper, a comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation (MS-AE-EMR) signals of rock bursts was proposed based on a deep learning algorithm. This method uses long short-term memory recurrent neural networks (LSTM-RNNs) to intelligently identify the MS-AE-EMR precursor signal of rock burst risk, predicts the MS-AE-EMR signal by a convolution neural network (CNN), analyses the MS-AE-EMR precursor signal of rock burst risk through the data analysis method and obtains the risk coefficient of rock burst. Moreover, by using the MS-AE-EMR original signal and risk coefficient, it trains the multi-input CNN and inputs the predicted signal into the trained multi-input CNN to obtain the predicted risk coefficient of rock burst. Analysing the risk coefficient completes the comprehensive early warning of the MS-AE-EMR signal of rock burst. After field verification, the RNN-based comprehensive early warning method of the MS-AE-EMR signal can respond positively to rock burst risk and capture the information in advance. Therefore, this method is of great significance for accurate monitoring and early warning of rock burst in coal mines.

AdaBoost-driven multi-parameter real-time warning of rock burst risk in coal mines

The stope dynamic disaster, which occurs around the mining space and is represented by stress-type rock burst and fracture-type rock burst, seriously affects the safety production of coal mine. How to effectively forewarn rock burst risk to reduce the disaster caused by rock burst is an urgent problem to be solved in stope. In this paper, the spatiotemporal parameters that affect the occurrence of stope dynamic disaster are collected, and the big data of stope state is established. Due to the complex temporal and spatial parameters of rock bursts with different intensities occurrence and their varying degrees, it is difficult for some machine learning methods to excavate their internal relationships and make accurate warning. In this paper, (1) a big data platform is used to record and integrate the multiple parameters of stope dynamic disaster, and data preprocessing technology is used to de-noise and standardize them. (2) Based on the fused historical data, a strong classifier is iteratively found by the classification algorithm AdaBoost to identify the existence of the rock burst risk, so as to achieve the purpose of accurately and timely warning of rock burst risk. The research of this paper uses big data mining technology and machine learning method to carry out intelligent perception warning of the rock burst risk in real time. The experimental results show that the proposed method has a good effect and is of great significance to the prevention and control of rock burst disaster in stope.

Numerical simulation of rock bursts triggered by blasting disturbance for deep-buried tunnels in jointed rock masses

Frequent dynamic disturbances, such as explosions, vibrations, and earthquakes, may trigger the risk of rock burst disasters around neighbouring deep-buried tunnels. In this study, the PFC software was used to simulate rock bursts triggered by blasting stress waves (dynamic disturbance) to determine their effects on deep-buried tunnels in jointed rock masses. This study investigated the effects of different ground stress values and joint angles on rock bursts in tunnels under intense dynamic disturbance. An analysis of different ground stress values and joint angles revealed the changes in the ejection velocities of damaged rocks and kinetic energy under intense dynamic disturbance. The results show that higher ground stress can alter the proportion of stress waves converted into strain energy and kinetic energy. Joint angles affect the location of the rock burst. The closer the joint angle is to 90°, the greater the damage to surrounding rocks caused by stress waves. Higher ground stress and joint angles increase the risk and intensity of rock bursts, causing significant damage to surrounding rocks, which makes existing tunnels more vulnerable to impact. The findings of this research have important guiding significance to ensure the secure protection of tunnels against rock bursts.

Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm

Rockburst is a kind of common geological disaster in deep tunnel engineering. It has the characteristics of causing great harm and occurring at random locations and times. These characteristics seriously affect tunnel construction and threaten the physical and mental health and safety of workers. Therefore, it is of great significance to study the tendency of rockburst in the early stage of tunnel survey, design and construction. At present, there is no unified method and selected parameters for rockburst prediction. In view of the large difference of different rockburst criteria and the imbalance of rockburst database categories, this paper presents a two-step rockburst prediction method based on multiple factors and the stacking ensemble algorithm. Considering the influence of rock physical and mechanical parameters, tunnel face conditions and excavation disturbance, multiple rockburst criteria are predicted by integrating multiple machine learning algorithms. A combined prediction model of rockburst criteria is established, and the results of each rockburst criterion index are weighted and combined, with the weight updated using the field rockburst record. The dynamic weight is combined with the cloud model to comprehensively evaluate the regional rockburst risk. Field results from applying the model in the Grand Canyon tunnel show that the rockburst prediction method proposed in this paper has better applicability and higher accuracy than the single rockburst criterion.

Multi-parameter comprehensive early warning of coal pillar rockburst risk based on DNN

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard.

Cluster Analysis of Moment Tensor Solutions and its Application to Rockburst Risk Assessment in Underground Coal Mines

Cluster Analysis of Moment Tensor Solutions and its Application to Rockburst Risk Assessment in Underground Coal Mines

Assessment and Prediction of Rockburst Risk Zones Under Different Structural Plane Shapes

Assessment and Prediction of Rockburst Risk Zones Under Different Structural Plane Shapes

Optimization of Rockburst Risk Control Measures for Deeply Buried TBM Tunnels: A Case Study

Choosing reasonable control measures for different intensity rockburst risks not only effectively prevents and mitigates rockburst risks but also reduces time and engineering investment costs. Due to the limitations of the tunnel boring machine’s structure and working conditions, tunnels excavated by TBMs are highly susceptible to rockbursts. What is even worse is that there are very few measures to control the rockburst risk in these tunnels. Implementing reasonable control measures from the limited mitigation measures to control and mitigate rockburst in TBM tunnels is an urgent problem that warrants a solution. In this paper, a large number of on-site rockburst risk control cases and a large amount of MS monitoring data (the total mileage of MS monitoring is approximately 7 km, lasting for 482 days) are used to derive a reasonable scheme to control the rockburst risk of different intensities in twin TBM tunnels. First, the rockburst early warning effect of the two headrace tunnels of the Neelum–Jhelum hydropower station based on microseismic monitoring is analyzed. Second, based on highly accurate rockburst warning results, 94 rockburst risk control cases are applied to analyze the control effect of different control measures at different intensities of rockburst risk. Then, by combining factors such as the time cost and expense cost of different control measures, more reasonable control measures for different intensity rockburst risks are proposed: for slight rockburst risk, normal excavation is preferred; for moderate rockburst risk, horizontal destress boreholes are preferred; and for intense rockburst risk, a combination of measures of shortening daily advance and horizontal destress boreholes is preferred. The research results can provide a reference for other TBM excavation projects to carry out rockburst risk prevention and mitigation.

Investigation into Occurrence Mechanism of Rock Burst Induced by Water Drainage in Deep Mines

Confined aquifers widely exist in the strata of the Ordos mining area. Water drainage before mining is an effective measure to avoid water inrush disasters caused by the connectivity between mining-induced roof fractures and aquifers. However, rock burst disasters occur frequently in the mining process of many water drainage working faces. The statistics show that the surrounding rock of nine water drainage working faces in five mines has suffered different degrees of rock burst, which seriously restricts the safe, efficient production and sustainable development of ten-million-ton modern mines in China. Based on this, taking the 22,106 longwall working face (LW22106) of the Shilawusu Mine, Ordos, as the engineering background, this paper investigates the occurrence mechanism of water drainage on rock burst in the roof water-rich area by using theoretical analysis, similar material simulation, microseismic measurement and other methods. The main conclusions are as follows: (1) After the drainage of the water-rich area, the pressure relief zone, pressurized zone and pressure stabilization zone are formed in sequence from the center to both sides. The width of the pressure relief zone is consistent with that of the water-rich area, and the width of the pressurized zone is about 35 m on one side. (2) When the mining is passing the pressurized zone, the coal rock mass is under the joint influence of gravity stress, mining disturbance stress and drainage transfer stress. The superimposed stress generally exceeds 2.5 times the uniaxial compressive strength (UCS) of the coal rock mass, and the maximum reaches 3.24 times, far exceeding the critical value of rock burst (1.5 times UCS), which is the main reason to induce rock burst. (3) The dynamic change in the rock burst risk areas before and after drainage in water-rich area no. 4 has been predicted, and the number of risk areas increases from 4 before drainage to 13 after drainage. Since the stress superposition effect differs due to different mining speeds, it is proposed that the dynamic regulation of mining speed in the front and at the back of the drainage area is an effective and efficient method for rock burst prevention and control. The research results will provide a theoretical basis and technical support for the prevention and control in the roof water-rich area of deep mines.

Experimental investigation and failure characteristics of slit-cut method in rockburst prevention

Rockburst has become one of the most serious threats to the safety of workers, equipment, and excavations in deep underground engineering. However, the prevention of rockburst risk remains an unsolved problem. The slot-cut method was put forward, aiming at the problem of strain burst for tunnel projects. The mechanism and method of slot-cut prevention of delayed rockburst were discussed by laboratory tests, theoretical analysis, and numerical simulation. The mechanical responses, failure patterns and failure indexes of granite specimens were analysed. The rockburst process and deformation evolution law of test specimens with different crack parameters were studied in great detail. Based on the experimental results, a comprehensive discrimination of rockburst proneness was explored. The results demonstrate that the peak stress and damage index of pre-cracked granite initially decreases and subsequently increases with crack angle, and the pre-peak and post-peak elastic modulus with different crack numbers decreases linearly, indicating that a change in the crack number significantly affects the bearing capacity of granite. The failure mode of the granite specimen belongs to the tension-shear composite type. Meanwhile, it was found that both the brittleness index and ejection energy with different cracks show an U-shaped for the pre-cracked granites, confirming that the crack angle of 60° has been found to be the threshold for rockburst proneness. Ultimately, the combination of the rockburst energy index describing rock ejection qualitatively and the optimized residual energy index considering elastic energy at post-peak stage quantitatively provides a judgment criteria to comprehensively characterize the rockburst proneness of granite by the slit-cut method. When compared with original index, the results of the comprehensive discrimination exhibited good accuracy. Therefore, the experimental phenomenon and judgment criteria prove that the slit-cut technology can be well applied to the rockburst prevention.

Effect of strain rate on mechanical response and failure characteristics of horizontal bedded coal under quasi-static loading

Strain rock burst is one of the main types of rock bursts. Studying the mechanical response and acoustic emission characteristics of coal under quasi-static loading is significant to control and prevent strain rock bursts. In this paper, coal’s strength, deformation, energy evolution, and failure characteristics were analyzed with different strain rates under quasi-static loading. The strength characteristics of coal show a strain rate effect to a certain extent and the elastic modulus decreases first and then increases with stain rate increasing. Moreover, the elastic strain energy of coal samples always accounts for a high proportion before failure and the failure of coal presents a combined failure mode of tensile and shear under the dominance of tensile failure. The contribution of the shear failure to coal failure increases correspondingly when strain rate increases. Under quasi-static loading, There is a range where the strain rate effect does not appear, named as strain rate effect invisible area. The high static loading stress, and direct action of high strain rate loading should be avoided to reduce the risk of rock burst, especially for isolated coal pillars. The research achievements deepen the understanding of strain rock burst and provides critical support for the prevention of strain rock burst induced by high static loading.

Evaluation of rockburst risk in deep tunnels considering structural planes based on energy dissipation rate criterion and numerical simulation

Risk evaluation of rockbursts is crucial for ensuring the safety and stability of deep-tunnel construction. Structural planes play an important role in the potential for rockbursts to occur, and more related studies are needed. In this paper, a new rockburst criterion based on energy dissipation rate (EDR) index was developed and the role of structural planes in rockburst potential was studied. Firstly, a method of generating structural planes and a viscoplastic constitutive model with internal variables were introduced for numerical simulation using FLAC3D. Then, tunnelling under different hydrostatic pressures was simulated to obtain the values of EDR index and stress-strength/strength-stress ratio indices, and EDR criterion was estimated based on stress-strength/strength-stress ratio criteria. The results show that EDR criterion is: 0∼1 Pa/s (no risk), 1∼3 Pa/s (low risk), 3∼6 Pa/s (medium risk), >6 Pa/s (high risk). Subsequently, the case study of rockbursts in the drainage tunnel of Jinping II hydropower station was conducted considering a nearby fault to validate EDR criterion. The simulation results using EDR are basically consistent with actual rockbursts in terms of the potential location, intensity and time. Finally, the numerical simulations of tunnel excavation considering a joint set with different dip angles were implemented, and the influences of joints on rockbursts were investigated using EDR. Analysis results show that rockbursts are prone to occur on tunnel walls parallel and perpendicular to joints; potential rockburst locations are controlled by local joints and are less affected by geostress distribution. Joints may increase the intensity, delay the occurrence time, and control the pit boundaries of rockbursts. Obtained EDR contours can be used to infer damaged zones of strainbursts and strain-structure slip rockbursts and understand inducing mechanism of rockbursts in Jinping II drainage tunnel. The findings of this study provide insight into the role of structural planes on rockbursts. The presented methodology can be applied to rockburst evaluation and prevention during the construction of deep tunnels.

Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory

Microseismic monitoring is a common monitoring tool in the mining production process; for supervising a huge amount of microseismic data, effective analysis tools are necessary. In this study, the monitoring results of microseismic events at the Maoping lead-zinc mine in Yiliang County, Yunnan Province, and the spatiotemporal distribution characteristics of microseismic events are analyzed. We analyze the temporal characteristics of microseismic events using fractal theory, combining the change in fractal dimension with the rock burst incubation process. We also construct an observation area model for event anomalies based on the spatial distribution characteristics of microseismic events. The results show that the growth of the fractal dimension is consistent with the trend of the incubation process before rock burst, and the larger the fractal dimension, the higher the rock burst risk. The observation model, based on the density of microseismic events, can effectively refine the rock burst discrimination range and facilitate subsequent observations. An effective and feasible method of microseismic analysis is provided.