Development of Rockburst Research: A Comprehensive Review

Wu, Meng; Ye, Yicheng; Wang, Qihu; Hu, Ning

doi:10.3390/app12030974

Cited by 44 publications

(25 citation statements)

References 139 publications

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“…To reduce the damage of rock burst, a lot of research on rock burst index, discrimination and prediction has been performed in theory and experiments widely performed (Lan et al, 2022;Wu et al, 2022;Xue et al, 2022). Up till the present moment, the system of rock burst prediction method is not perfect, which can be fallen into 3 basic classifications.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rock Burst Intensity Based on Multi-Source Evidence Weight and Error-Eliminating Theory

Yan

Tian

et al. 2023

Preprint

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Rock burst is the main geological hazard in deep underground engineering. For the prediction of the intensity of rock burst, a model for prediction of rock burst intensity on the basis of multi-source evidence weight and error-eliminating theory was established. Four indexes including the ratio of rock′s compressive-tensile strength σc/σt , the stress coefficient of rock σθ/σc, the elastic energy index of rock Wet and integrality coefficient Kv were chosen as the prediction variables of rock burst, the index weights are calculated by different weighting methods, and fused with evidence theory to determine the final weight of each index. According to the theory of error-eliminating, taking no rock burst as the objective and using the error function to processed 18 sets of typical rock burst data at home and abroad, and the weight of evidence fusion as the normalized index limit loss value, and a model for prediction of rock burst intensity was build. It is verified by the actual situation and three other models. Finally, the model has been applied to rock burst prediction of Zhongnanshan tunnel ventilation shaft. The results show that evidence theory fuses multi-source index weights and improves the method of determining index weights. The index value is processed by Error-eliminating theory, and the limit value problem of index value normalization is optimized. The predicted results of the proposed model are consistent with the situation of Zhongnanshan tunnel. It improves the objectivity of the rock burst prediction process and provides a research idea for rock burst intensity prediction index.

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Section: Introductionmentioning

confidence: 99%

Prediction of Rock Burst Intensity Based on Multi-Source Evidence Weight and Error-Eliminating Theory

Yan

Tian

et al. 2023

Preprint

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“…Rock bursts and mining-induced seismicity are typical geodynamic disasters, often causing severe damage to shafts and roadways and heavy casualties (Cao et al 2023;Cheng et al 2023). Rock bursts have occurred in coal mines in over 20 large coal mining countries, including South Africa, Germany, Canada, Russia, and Australia (Wu et al 2022). On May 12, 2014, a rockburst accident occurred at a coal mine in Boone County, West Virginia, USA, killing two miners (Newman and Newman 2021).…”

Section: Introductionmentioning

confidence: 99%

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

Cheng

Qiao

2023

Preprint

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The assessment of rockburst risk (RBR) is the premise of rockburst prevention and control. It is the implementation criterion to guide various rockburst prevention and control measures. The existing static prediction and evaluation methods for RBR cannot be effectively combined with the results of underground dynamic monitoring. This study proposed a mining-induced seismicity information quantification method based on the fractal theory. Deep learning methods are used to construct a deep learning framework of rockburst risk (DLFR) based on the fractal dimension of microseismic information. Gray correlation analysis (GRA), information gain ratio (IGR), and Pearson correlation coefficient are used to screen and compare factors. Statistical evaluation indicators such as macro-F1, accuracy rate, and fitness curve were used to evaluate model performance. Taking Gaojiapu Coal Mine in the Binchang mining area as a case study, the performance of deep learning models such as BP Neural Network (BP), Support Vector Machine (SVM) and its optimized model based on particle swarm optimization (PSO) algorithm under this framework is discussed. The reliability and validity of the research results to verify by comparing the predicted results with the actual results. The research results show that the prediction results of RBR in DLFR are consistent with the actual results, and the model is reliable and effective. The mining-induced seismicity quantification can solve the problem of insufficient training samples for the RBR. With this, different pressure relief measures can be formulated based on the results of the RBR predictions to achieve "graded" precise prevention and control.

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“…It is a dynamic instability phenomenon that occurs when a rock mass or geological structure is subjected to high stress or is in a state of limit equilibrium. A rockburst, which still draws a lot of interest today, has a significant impact on rock stability in deep underground conditions [1][2][3]. Because rockbursts occur suddenly and intensely, they frequently result in harm, including death of workers, equipment damage, and even significant interruption and loss of revenue in underground deep excavation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Development of Rockburst Research: A Comprehensive Review

Cited by 44 publications

References 139 publications

Prediction of Rock Burst Intensity Based on Multi-Source Evidence Weight and Error-Eliminating Theory

Prediction of Rock Burst Intensity Based on Multi-Source Evidence Weight and Error-Eliminating Theory

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

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

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