Rock burst assessment in multi-seam mining: a case study

Shen, Wei; Li, Dou; He, Hu; Zhu, Guang-an

doi:10.1007/s12517-017-2979-z

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…Among them, parallel coal pillars run parallel to the working face. According to the positional relationship between the overlying coal pillars and the roadway on the lower working face, they can be divided into overlapping type, inward-stagger type, and outwardstagger type, such as the Malan mine and Shangwan mine [32]. Diagonal coal pillars intersect with the lower working face at different angles, such as Xinzhouyao mine and Sanhejian mine [33].…”

Section: Classification Of Close-distance Multiseam Coal Pillars and mentioning

confidence: 99%

“…Sun et al [31] studied the stability of the overlying remaining coal pillars by using the elastic-plastic theory and the slip line field theory. According to the particularity of multiseam mining, Shen et al [32] established the energy density risk index to evaluate the risk of multiseam mining by using the microseismic data. Previous studies have shown that overlying coal pillars under the condition of close-distance multiseam coal pillars were the main factor causing disaster.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Strong Strata Behaviors in the Close‐Distance Multiseam Coal Pillar Mining

Bao

Song

et al. 2021

Shock and Vibration

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According to the new stress distribution pattern and the strong strata behaviors as the characteristics of the coal pillars in the close-distance multiseam coal pillar mining, the common characteristics of different types of overlying coal pillars were summarized and analyzed. Moreover, a theoretical model for the mechanism of strong strata behaviors in the close-distance multiseam coal pillar mining was established, which was validated by the monitoring data of seismic computed tomography CT, microseism, and electromagnetic radiation (EMR). Furthermore, the results of the study indicated that the main factors affecting the strong strata behaviors were the static stress concentration caused by the overlying coal pillars and the dynamic disturbance caused by the fracturing and slipping of the overlying coal pillars and roof under the influence of mining. In the case of Xinzhouyao coal mine, the transmitted stress and lateral support pressure of the overlying coal pillars accounted for 78.3% and 16% of the vertical concentrated stress, respectively, and the areas closer to the overlying coal pillars were more susceptible to dynamic load disturbances. The monitoring results of seismic computed tomography CT and EMR demonstrated the static load stress concentration area was distributed near the overlying coal pillar, and the stress concentration degree was greater in the area of superimposed lateral support pressure and advanced support pressure. Moreover, microseismic spatial positioning revealed that the high-energy microseismic events were mainly concentrated near the overlying large coal pillars and roof. The on-site multiparameter detection results were highly consistent with the characteristics of actual strata behaviors and the conclusions of the theoretical model. This method could provide a reference for the quantitative calculation of stress distribution under similar conditions and the identification of the danger zone of strata behaviors.

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Section: Classification Of Close-distance Multiseam Coal Pillars and mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of Strong Strata Behaviors in the Close‐Distance Multiseam Coal Pillar Mining

Bao

Song

et al. 2021

Shock and Vibration

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“…Xiaobaodang Coal Mine is located in Yushen Coalfield and occurrence of multiple coal seams, although the coal seam belongs to medium‐deep‐buried coal seam, the thickness of coal seam is large, the strength of overburden strata is low, repeated mining of multiple coal seams and other factors, so that the surface subsidence is serious and the surface cracks are more developed. Most of the water‐flowing fracture zones lead to the ground, and serious plastic deformation often occurs in overburden strata and coal pillars 40–45 . Therefore, the plastic slip line field theory is also suitable for Xiaobaodang Coal Mine.…”

Section: Engineering Practicementioning

confidence: 99%

Optimization of staggered distance of coal pillars in multiseam mining: Theoretical analysis and numerical simulation

Sun

Cheng³

2020

Energy Science & Engineering

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Aimed at solving problems related to water retention and loss reduction in multiseam mining, a mechanical model of staggered distance mining was established. First, elastic and plastic slip line field theories were used to calculate the reasonable staggered distance expressions of multiseam mining. The staggered distance schemes of multiseam mining were optimized using the numerical simulation software FLAC3D. By performing an experiment comparing similar materials, the influence on water retention and loss reduction by optimal and unfavorable staggered distance schemes in multiseam mining was explored. The results showed that the σzz curve was distributed in the shape of a “peak” of the staggering distance coal pillars, the Z displacement curve was a “Л,” and the surface subsidence curve was a “W.” Each of these displayed the rule of “decrease then increase.” In staggered distance multiseam mining, the optimal staggered distance value for coal seam Nos. 2 and 3 was 40 m, while the most unfavorable staggered distance value was 0 m. The corresponding subsidence coefficients of the optimal staggered distance values were 0.29, 0.23, and 0.19, under the condition of keeping the optimal staggered distance value for coal seam Nos. 1 and 2 unchanged. These results could be beneficial in reduction of surface subsidence and may also have specific practical engineering significance.

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“…Based on the literature investigation and on-the-spot measurement of tunnel, the following indexes are selected to describe a tunnel section [27][28][29][30][31][32][33]: the Russian criterion value C 1 (C 1 � σ θmax /σ C , i.e., the ratio of maximum tangential stress of chamber σ θmax to rock uniaxial compressive strength σ C ), rock brittleness coefficient C 2 (C 2 � σ C /σ t , i.e., ratio of uniaxial compressive strength σ C to uniaxial tensile strength of rock σ t ), the grade of groundwater condition C 3 and rock integrity coefficient C 4 (Kv value). e above indexes reflect the different attributes of the tunnel section, respectively.…”

Section: Application In Rockburst Predictionmentioning

confidence: 99%

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Zheng

Zhong

Fang

et al. 2019

Advances in Civil Engineering

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A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

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Rock burst assessment in multi-seam mining: a case study

Cited by 20 publications

References 19 publications

Investigation of Strong Strata Behaviors in the Close‐Distance Multiseam Coal Pillar Mining

Investigation of Strong Strata Behaviors in the Close‐Distance Multiseam Coal Pillar Mining

Optimization of staggered distance of coal pillars in multiseam mining: Theoretical analysis and numerical simulation

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

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