Prediction and prevention technology of rock burst based on stress control theory

2013 ◽  
pp. 591-596
Author(s):  
Q Qi ◽  
S Zhao ◽  
N Zhang
Keyword(s):  
Control Theory ◽  
Rock Burst ◽  
Stress Control ◽  
Prediction And Prevention

scholarly journals Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiachuang Wang ◽  
Mingjian Huang ◽  
Jiang Guo
Keyword(s):  
Compressive Strength ◽  
Rock Burst ◽  
Uniaxial Compressive Strength ◽  
Cloud Model ◽  
High Stress ◽  
Prediction Method ◽  
Deformation Energy ◽  
Prediction And Prevention ◽  
Calculation Results ◽  
Factor Interactions

Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σc (I1), ratio of the uniaxial compressive strength to the tensile strength σc/σt (brittleness coefficient, I2), elastic deformation energy index Wet (I3), ratio of the maximum tangential stress to the uniaxial compressive strength σθ /σc (stress coefficient, I4) of the rock, depth of the roadway H (I5), and integrity coefficient of the rock mass Kv (I6) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.

scholarly journals Study on Overburden Rock Movement and Stress Distribution Characteristics under the Influence of a Normal Fault

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Quansen Wu ◽  
Peng Kong ◽  
Quanlin Wu ◽  
Xinggang Xu ◽  
Xingyu Wu ◽  
...  
Keyword(s):  
Rock Burst ◽  
Coal Pillar ◽  
Local Deformation ◽  
Overburden Rock ◽  
High Position ◽  
Rock Bursts ◽  
Fault Activation ◽  
Working Face ◽  
Prediction And Prevention ◽  
Geological Conditions

Fault activation triggers local deformation and dislocation, releasing a large amount of energy that can easily cause mining disasters, such as rock bursts and roadway instability. To study the changing characteristics of overburden structures and the evolution law of mining-induced stress as panel advances towards a fault from a footwall, two similar models were established, namely, a simulation experimental model and a numerical simulation model. In addition, the relationship among mining, mining stress, and rock bursts induced by fault activation was investigated. The results of this study reveal that when the working face is 30 m away from the fault, the high-position rock mass near the fault turns to the goaf where the fault is activated, and the two walls display relatively obvious dislocation. During the process of footwall panel mining to the fault, the abutment stress of the coal pillar tends to increase initially, followed by a decrease. When the working face is 20 m away from the fault, the abutment stress ahead of the working face reaches its maximum. When the width of the coal pillar is within the range of 10–40 m, the coal pillar accumulates a large amount of energy, and the working face affected by the fault easily induces a rock burst. Before fault activation, disturbances arising from the mining activities destroy the equilibrium stress environment of the rock system surrounding the fault, and the fault continuously accumulates energy. When the accumulated energy reaches a certain threshold, under the action of normal stress or shear stress, the fault will be activated, and a large amount of energy will be released, which can easily induce a rock burst. The research results in this paper provide a scientific basis for the classification, prediction, and prevention of rock bursts under similar geological conditions.

scholarly journals Dynamic Characteristics of Fault Structure and Its Controlling Impact on Rock Burst in Mines

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Tianwei Lan ◽  
Jiawei Sun ◽  
А. S. Batugin ◽  
Wenqi Zhao ◽  
Mancang Zhang ◽  
...  
Keyword(s):  
Coal Mining ◽  
Rock Burst ◽  
Treatment Plan ◽  
Geological Structure ◽  
Mining Engineering ◽  
Fault Structure ◽  
Safe Production ◽  
Geodynamic Environment ◽  
Controlling Mechanism ◽  
Prediction And Prevention

As one of the most serious shock dynamic disasters in coal mining, rock burst only occurs under the certain geodynamic environment. Geodynamic is the necessary requirement for the occurrence of rock burst, and the disturbance of mining engineering is the sufficient requirement. In terms of the fault structure, the method of geodynamic zoning is used to classify fault structure forms of rock burst in mines, and a model of geological structure is established to reveal the connection between fault structure and mine engineering. Besides, the influence of fault structure on rock burst is analyzed, and the controlling mechanism of the fault structure on the tectonic evolution of the mine area and the occurrence of rock burst is revealed. This research provides a treatment plan for the prediction and prevention of rock burst and guides the safe production in the coal mining engineering.

scholarly journals Application of big data technology in prediction and prevention of rock burst

2020 ◽  
Vol 1629 ◽  
pp. 012089
Author(s):  
Junwei Shi ◽  
Xiangrui Meng ◽  
Zhangliang Chen ◽  
Xueyi Wu
Keyword(s):  
Big Data ◽  
Rock Burst ◽  
Prediction And Prevention ◽  
Big Data Technology

scholarly journals The Rock Burst Hazard Evaluation Using Statistical Learning Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Chen ◽  
Jingkuan Gao ◽  
Yuanyuan Pu ◽  
Mingzhong Gao ◽  
Like Wei ◽  
...  
Keyword(s):  
Rock Burst ◽  
Statistical Learning ◽  
Learning Model ◽  
Impact Factors ◽  
Hazard Evaluation ◽  
Learning Approaches ◽  
Prior Distributions ◽  
Burst Hazard ◽  
Prediction And Prevention ◽  
Rock Burst Risk

The great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. In this study, Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. The results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. The Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data.

Quality charters or quality members? A control theory perspective on team charters and team performance.

2017 ◽  
Vol 102 (10) ◽  
pp. 1462-1470 ◽  
Author(s):  
Stephen H. Courtright ◽  
Brian W. McCormick ◽  
Sal Mistry ◽  
Jiexin Wang
Keyword(s):  
Control Theory ◽  
Team Performance
Sign in / Sign up

Export Citation Format

Share Document