Search Results (37)

Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are associated with certain limitations, such as a short early warning time and low accuracy of early warning. To enhance the timeliness of early warnings and bolster the safety of coal mines, a novel early warning model has been developed. In this paper, we present a framework for predicting the FEMR signal in deep future and recognizing the rock burst precursor. The framework involves two models, a guided diffusion model with a transformer for FEMR signal super prediction and an auxiliary model for recognizing the rock burst precursor. The framework was applied to the Buertai database, which was recognized as having a rock burst risk. The results demonstrate that the framework can predict 360 h (15 days) of FEMR signal using only 12 h of known signal. If the duration of known data is compressed by adjusting the CWT window length, it becomes possible to predict data over longer future time spans. Additionally, it achieved a maximum recognition accuracy of 98.07%, which realizes the super prediction of rock burst disaster. These characteristics make our framework an attractive approach for rock burst predicting and early warning. Full article

Ensuring the accurate prediction, prevention, and control of coal bursts in isolated working faces is crucial for ensuring safe mining operations. Coal bursts are typically caused by the accumulation of stress and energy released in coal seams and the overlying strata. This study focuses on the 76 isolated working faces at Shanxi Wuyang Mine, employing a combination of theoretical analysis, numerical simulation, and field monitoring. Through theoretical analysis, the study examines the influence of the spatial structure of the overlying strata on support stress and develops corresponding estimation functions. Additionally, bearing strength calculation formulas under varying confining pressures are derived. Numerical simulations are used to validate the effectiveness of borehole stress relief, while field monitoring further confirms the accuracy of the proposed model, leading to the development of the “overall–local” coal burst prediction method. The results demonstrate that the proposed method effectively assesses coal burst risks and, based on different coal burst types, recommends borehole stress relief and roof deep-hole blasting as primary mitigation strategies. These methods were successfully applied to the 76 isolated working faces at Wuyang Mine, yielding conclusions of overall stability with localized instability. This study provides new insights into coal burst prediction theory and offers practical guidance for preventive engineering in isolated working faces, demonstrating substantial engineering applicability. Full article

The risk level and disaster scale of rock bursts in deeply buried and highly stressed tunnels are commonly high, posing serious threats to their construction safety. This study employed a combination of on-site measurements and discrete-continuous coupled numerical simulations to analyze the geo-stress distribution characteristics of surrounding rock masses in the Xuefeng Mountain No.1 Tunnel. The evolution processes of rock burst failure in surrounding rock masses with different lithologies and buried at different depths were discussed. The risk of rock bursts along this long tunnel was predicted using the stress–strength ratio criterion and the energy method. The results showed that the principal stress values of surrounding rock masses in the Xuefeng Mountain No.1 Tunnel followed a distribution pattern of σ_x > σ_y > σ_z (where x, y, and z denoted the directions of tunnel cross-section and tunnel axis and the direction perpendicular to the ground), with average stress levels exceeding 20 MPa. It should be a typical tunnel dominated by horizontal tectonic stress. Stress concentration and elastic strain energy accumulation zones in this tunnel were mainly located at the bottom, and the largest displacements always occurred at the inverted arch. The main characteristics of rock burst failure in this tunnel included the sheet-like splitting of rock mass layers and the ejection of rock blocks. The risk evaluation of rock bursts across different sections of the tunnel, considering various rock types and buried depths, presented that these deeply buried slate and granite exhibited the highest risk level when assessed using the elastic strain energy index criterion. The comparative analysis between the elastic strain energy method and the stress–strength ratio criterion showed that the evaluation results obtained by the latter were more conservative. The findings of this study can provide a valuable reference for cognizing the geo-stress characteristics and predicting rock bursts in the surrounding rock masses of deep-buried and highly stressed tunnels. Full article

The prediction of rock bursts is of paramount importance in ensuring the safety of coal mine production. In order to enhance the precision of rock burst prediction, this paper utilizes a working face of the Gengcun Coal Mine as a case study. The paper employs a three-year microseismic monitoring data set from the working face and employs a sensitivity analysis to identify three monitoring indicators with a higher correlation with rock bursts: daily total energy, daily maximum energy, and daily frequency. Three subsets are created from the 10-day monitoring data: daily frequency, daily maximum energy, and daily total energy. The impact risk score of the next day is assessed as the sample label by the expert assessment system. Sample input and sample label define the data set. The long short-term memory (LSTM) neural network is employed to extract the features of time series. The Bayesian optimization algorithm is introduced to optimize the model, and the Bayesian optimization–long short-term memory (BO-LSTM) combination model is established. The prediction effect of the BO-LSTM model is compared with that of the gated recurrent unit (GRU) and the convolutional neural network (1DCNN). The results demonstrate that the BO-LSTM combined model has a practical application value because the four evaluation indexes of the model are mean absolute error (MAE), mean absolute percentage error (MAPE), variance accounted for (VAF), and mean squared error (MSE) of 0.026272, 0.226405, 0.870296, and 0.001102, respectively. These values are better than those of the other two single models. The rock explosion prediction model can make use of the research findings as a guide. Full article

Strain rockburst is a severe failure phenomenon caused by the release of elastic strain energy in intact rocks under high-stress conditions. They frequently occur in deep tunnels, causing significant economic losses, casualties, and construction delays. Understanding the factors influencing this disaster is of significance for tunnel construction. This paper first proposes a novel three-dimensional (3D) discrete element numerical analysis method for rockburst numerical analysis considering the full stress state energy based on the bonded block model and the mechanics, brittleness, integrity, and energy storage of the surrounding rock. This numerical method is first validated via laboratory tests and engineering-scale applications and then is applied to study the effects of compressive and tensile strengths of rock mass, tunnel depth, and lateral pressure coefficient on strain rockburst. Meanwhile, sensitivity analyses of these influencing factors are conducted using numerical results and systematic analysis methods, and the influence degree of each factor on the rockburst tendency is explored and ranked. The results reveal that laboratory tests and actual engineering conditions are consistent with numerical simulation results, which validates the rationality and applicability of the novel rockburst analysis method proposed in this paper. With the increase in compressive strength, the stress concentration degree, energy accumulation level, maximum stress difference, and maximum elastic strain energy within the rock mass all increase, leading to a stronger rockburst tendency. Tunnel depth and the lateral stress coefficient are positively correlated with rockburst tendency. As the lateral pressure coefficient and tunnel depth increase, rockburst tendency exponentially increases, while the maximum stress difference and maximum elastic strain energy within the rock mass also increase. The influence degree of each factor is ranked from highest to lowest as follows: tensile strength, lateral pressure coefficient, compressive strength, and tunnel depth. The research results provide theoretical support and technical guidance for the effective prediction, prevention, and control of rock burst disasters in deep tunnels. Full article

The main frequency of microseismic signals has recently been identified as a dominant indicator for characterizing vibration events because it reflects the energy level of these events. Frequency information directly determines whether effective signals can be collected, which has a significant impact on the accuracy of predicting rock burst disasters. In this study, we adopted a characterizing method and developed a monitoring system for capturing rock failure events at various strata in an underground coal mine. Based on the rock break mechanism and energy release level, three types of rock failure events, namely, high roof breaking, low roof breaking, and coal fracture events, were evaluated separately using specific sensors and monitoring systems to optimize the monitoring accuracy and reduce the general cost. The captured vibration signals were processed and statistically analyzed to characterize the main frequency features for different rock failure events. It was found that the main frequency distribution ranges of low roof breaking, high roof breaking, and coal fracture events are 20–400 Hz, 1–180 Hz, and 1–800 Hz, respectively. Therefore, these frequency ranges are proposed to monitor different vibration events to improve detection accuracy and reduce the test and analysis times. The failure mechanism in a high roof is quite different from that of low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect both in the maximum amplitude and main frequency, which could be an indicator to distinguish failure locations in the vertical direction. This result can support the selection and optimization of the measurement range and main frequency parameters of microseismic monitoring systems. This study also discussed the distribution law of the maximum amplitude and main frequency of different events and the variation in test values with the measurement distance, which are of great significance in expanding the application of optimized microseismic monitoring systems for rock burst monitoring and prevention. Full article

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. Full article

Predicting rock bursts is essential for maintaining worker safety and the long-term growth of subsurface infrastructure. The purpose of this study is to investigate the precursor reactions and processes of rock instability. To determine the degree of rock damage, the research examines the time-varying acoustic emission (AE) features that occur when rocks are compressed uniaxially and introduces AE parameters such as the b-value, γ-value, and β_t-value. The findings suggest that the evolution of rock damage during loading is adequately reflected by the b-value, γ-value, and β_t-value. The relationships between b-value, γ-value, and β_t-value are studied, as well as the possibility of using these three metrics as early-warning systems for rock failure. Full article

A fault is a common geological structure encountered in underground coal mining. Interactions between the discontinuous structure of a fault and mining activities are the key factors in controlling the rock bursts induced by the fault. It is of great importance to study the rock burst mechanism of an extra-thick coal seam under the combined influence of reverse faults and coal mining for the prediction and prevention of rock burst. In this study, we establish a sliding dynamics model of rock mass in a fault zone and analyze the mechanical distribution of fault-induced rock bursts under the combined action of mining disturbances. Additionally, we utilize theoretical calculation and a 3D numerical simulation method to clarify the rockburst mechanism in an extra-thick coal seam controlled by a thrust fault under mining disturbance and a fault. The results showed that the distribution range of the shear stress increment in the fault footwall was larger than that in the hanging wall, revealing a skewed distribution. The fault dip angle and mining thickness exhibit significant influence on the structure around the fault. With increases in the dip angle of the fault and mining thickness, the maximum vertical stress and peak stress first increase and then decrease. A position 80 m away from the fault is the dividing line between the fault-non-affected area and the fault-affected area. The 13,200 working face of the Gengcun coal mine is used as a case study to study the influence of mining disturbances on microseismic events. The results of this study are in good agreement with the theoretical calculations and numerical simulation results. Full article

The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock bursts, which can cause damage to miners and lead to the catastrophic failure of engineering structures. Therefore, developing a new brittleness index capable of effectively predicting rock bursts is essential for the safe and efficient execution of engineering projects. In this research study, a novel mathematical rock brittleness index is developed, utilizing factors such as crack initiation, crack damage, and peak stress for sandstones with varying water contents. Additionally, the brittleness index is compared with previous important brittleness indices (e.g., B₁, B₂, B₃, and B₄) predicted using infrared radiation (IR) characteristics, specifically the variance of infrared radiation temperature (VIRT), along with various artificial intelligent (AI) techniques such as k-nearest neighbor (KNN), extreme gradient boost (XGBoost), and random forest (RF), providing comprehensive insights for predicting rock bursts. The experimental and AI results revealed that: (1) crack initiation, elastic modulus, crack damage, and peak stress decrease with an increase in water content; (2) the brittleness indices such as B₁, B₃, and B₄ show a positive linear exponential correlation, having a coefficient of determination of R² = 0.88, while B₂ shows a negative linear exponential correlation (R² = 0.82) with water content. Furthermore, the proposed brittleness index shows a good linear correlation with B₁, B₃, and B₄, with an R² > 0.85, while it shows a poor negative linear correlation with B₂, with an R² = 0.61; (3) the RF model, developed for predicting the brittleness index, demonstrates superior performance when compared to other models, as indicated by the following performance parameters: R² = 0.999, root mean square error (RMSE) = 0.383, mean square error (MSE) = 0.007, and mean absolute error (MAE) = 0.002. Consequently, RF stands as being recommended for accurate rock brittleness prediction. These research findings offer valuable insights and guidelines for effectively developing a brittleness index to assess the rock burst risks associated with rock engineering projects under water conditions. Full article

Geological hazards, such as the frequent occurrence of rock bursts in deep mining, emphasize the critical necessity for the early warning and prediction of dynamic fractures in coal and rock masses, as well as the destabilization of the surrounding rock. This study delves into the mechanisms of electromagnetic radiation (EMR) signals and their synchronous coupling with micro-seismic (ME) signals. EMR and ME signals from rock specimens were systematically collected during the uniaxial compression fracture process using a dedicated monitoring and acquisition system. Employing the wavelet analysis method, the original data underwent reconstruction and denoising, while the EMR and ME spectra, derived through fast Fourier transform, were subjected to detailed scrutiny. The comprehensive analysis unveiled that EMR signals arising from rock fractures exhibited precise timing synchronization with ME signals. Moreover, the dominant frequencies of both signals are closely aligned within the low-frequency band, indicating a remarkable degree of similarity and homology. These findings establish an experimental basis for the development of monitoring and early warning systems geared toward assessing damage to coal and rock masses using EMR and ME signals. Full article

In order to accurately judge the tendency of rock burst disaster and effectively guide the prevention and control of rock burst disaster, a rock burst intensity-grade prediction model based on the comprehensive weighting of prediction indicators and Bayesian optimization algorithm–improved-support vector machine (BOA-SVM) is proposed for the first time. According to the main factors affecting the occurrence and intensity of rock burst, the rock stress coefficient (σ_θ/σ_c), brittleness coefficient (σ_c/σ_t) and elastic energy index (W_et) are selected to construct the rock burst prediction indicator system. On the basis of the research of other scholars, according to the main performance and characteristics of rock burst, rock burst is divided into four intensity levels. The collected and sorted 120 sets of rock burst case data at home and abroad are taken as learning samples, and the T-SNE algorithm is used to perform dimensionality-reduction visualization processing on the sample data, visually display the distribution of samples of different grades, evaluate the representativeness of the sample data and prejudge the feasibility of the machine learning algorithm to distinguish different rock burst intensity levels. The combined improved analytic hierarchy process (IAHP) and Delphi method determine the subjective weight of the indicators; the combined entropy weight method and CRITIC method determine the objective weight of the indicator, and use the harmonic mean criterion of information theory to synthesize the subjective weight and objective weight of the indicator to obtain the comprehensive weight of the indicators. After weighted prediction indicators, a rock burst intensity-grade prediction model is constructed based on the support vector machine, and the hyperparameters of three types of support vector machines are improved by using the Bayesian optimization algorithm. Then, the prediction accuracy of different models is calculated by the random cross-validation method, and the feasibility and effectiveness of the rock burst intensity-grade prediction model is verified. In order to evaluate the generalization and engineering applicability of the proposed model, 20 groups of rock burst case data from the Maluping mine and Daxiangling tunnel are introduced to predict the rock burst intensity grade. The results show that the accuracy of the rock burst intensity-grade prediction model based on comprehensive weighting and BOA-SVM is as high as 93.30%, which is of higher accuracy and better effect than the ordinary model, and can provide warning information with a higher fault tolerance rate, which provides a new way of thinking for rock burst intensity-grade prediction. Full article

Liquid nitrogen fracturing has emerged as a promising technique in fluid fracturing, providing significant advantages for the utilization and development of geothermal energy. Similarly to hydraulic fracturing in reservoirs, liquid nitrogen fracturing entails a common challenge of fluid–rock interaction, encompassing the permeation and diffusion processes of fluids within rock pores and fractures. Geomechanical analysis plays a crucial role in evaluating the transfer and diffusion capabilities of fluids within rocks, enabling the prediction of fracturing outcomes and fracture network development. This technique is particularly advantageous for facilitating heat exchange with hot dry rocks and inducing fractures within rock formations. The primary objective of this study is to examine the effects of liquid nitrogen fracturing on hot dry rocks, focusing specifically on granite specimens. The experimental design comprises two sets of granite samples to explore the impact of liquid nitrogen cooling cycles on the mode I fracture characteristics, acoustic emission features, and rock burst tendency of granite. By examining the mechanical properties, acoustic emission features, and rock burst tendencies under different cycling conditions, the effectiveness of liquid nitrogen fracturing technology is revealed. The results indicate that: (1) The ultimate load-bearing capacity of the samples gradually decreases with an increase in the number of cycling times. (2) The analysis of acoustic emission signals reveals a progressive increase in the cumulative energy of the samples with cycling times, indicating that cycling stimulates the release of stored energy within the samples. (3) After undergoing various cycling treatments, the granite surface becomes rougher, exhibiting increased porosity and notable mineral particle detachment. These results suggest that the cyclic application of high-temperature heating and liquid nitrogen cooling promotes the formation of internal fractures in granite. This phenomenon is believed to be influenced by the inherent heterogeneity and expansion–contraction of internal particles. Furthermore, a detailed analysis of the morphological sections provides insights into the structural changes induced by liquid nitrogen fracturing in granite samples. Full article

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. Full article

In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshitai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance. Full article