Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground

Kan, Jiliang; Li, Dou; Li, Xuwei; Cao, Jinrong; Bai, Jinzheng; Chai, Yanjiang

doi:10.1016/j.undsp.2022.02.002

Cited by 16 publications

(3 citation statements)

References 37 publications

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“…The system is capable of Real-time dynamic monitoring of microseismic signals with energy greater than 100J in the study area. It can accurately calculate the energy, time, and location of mining-induced microseismicity (Kan et al 2022).…”

Section: Geological Settingmentioning

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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Section: Geological Settingmentioning

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 peak particle velocity ( PPV ) of blasting vibration is commonly used as the key indicator for evaluating the induced vibration in the drilling and blasting method [ 7 , [11] , [12] , [13] , [14] , [15] , [16] ]. However, due to the abrupt nature of blasting events, some parameters are difficult to measure accurately.…”

Section: Introductionmentioning

confidence: 99%

Research on prediction of PPV in open-pit mine used RUN-XGBoost model

Sun,

Yang,

Yang

et al. 2024

Heliyon

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“…As shown in Table 1, conventional ML algorithms, neural networks, and many AIbased soft computing models have been introduced to predict PPV. However, while the PPV of open-pit mines has been investigated in past decades [9][10][11][12][13][14][15][16][17], there is less ground vibration prediction research regarding underground mines, and the prediction accuracy is lower [18,19]. Unlike open-pit mines, the surface structures of underground mines are constructed around the ore body.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

Tao,

Chen,

Xiao

et al. 2024

Applied Sciences

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Ground vibrations induced by underground mining blasting has a significant impact on the stability and safety of surface buildings near mines. Due to the thick rock layers overlying underground mines, there is presently limited accuracy in regard to predicting ground vibrations induced by underground mine blasting. Therefore, this study aims to improve the accuracy of predicting ground vibrations induced by underground blasting by comprehensively measuring the peak particle velocity (PPV) in all three directions and independently considering on the impact of vertical distance. Random forest regression (RFR), bagging regression (BR), and gradient boosting regression (GBR) were used to regress the X-axis PPV (X-PPV), Y-axis PPV (Y-PPV), and Z-axis PPV (Z-PPV) based on blasting records measured at an iron mine. In addition, a genetic algorithm, gray wolf optimizer (GWO), and a particle swarm optimization were used to optimize the parameters of the RFR, BR, and GBR. The comparison results show that GWO-GBR is the optimal model for the prediction of the X-PPV (R2 = 0.8072), Y-PPV (R2 = 0.9147), and Z-PPV (R2 = 0.9265), respectively. Thus, the GWO-GBR model proposed in this study is considered a highly reliable model for predicting ground vibrations induced by underground mine blasting to ensure the safety of the mines’ surroundings.

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Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground

Cited by 16 publications

References 37 publications

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

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

Research on prediction of PPV in open-pit mine used RUN-XGBoost model

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

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