Mine Ground Pressure Monitoring and Early Warning Based on Deep Learning Data Analysis

Xiao, Yigai; Deng, Hongwei; Xie, Zhimou; Lu, Hongbin

doi:10.1155/2022/6255119

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Jian Zhou [13] used a hybrid technique combining an artificial neural network (ANN) and an artificial bee colony (ABC) to establish a complex relationship between susceptibility to ground pressure and its influencing factors, which could be used as an effective tool for predicting susceptibility to ground pressure. Yigai Xiao [14] proposed a metal mine pressure monitoring and warning method based on deep learning data analysis. The theoretical basis of rockburst was elaborated, the inducing factors of deep well rockburst were analyzed and the classification of rockburst was examined.…”

Section: Introductionmentioning

confidence: 99%

Study on the Distribution Trend of Rockburst and Ground Stress in the Hegang Mining Area

Pang

Xie

et al. 2023

Sustainability

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As the depth of coal mining increases, rockbursts become more severe, and multiple rockburst accidents have previously occurred, seriously affecting coal mine production safety. With the Hegang mining area as the research object and using field measurement and numerical simulation as the research methods, the geological and tectonic characteristics of each impact mine in the Hegang mining area are analyzed to obtain the tectonic stress field distribution characteristics of each mine and the tectonic stress field distribution trend in the Hegang mining area. The maximum horizontal principal stress and the minimum horizontal principal stress on the energy field in the overrun area of the retrieval working face is analyzed via numerical simulation, and the influence trend of the ground stress on the impact ground pressure is finally obtained. Results show that using the typical working face where rockburst occurs as the geological proto-type, the influence of the direction of the ground stress field, the maximum horizontal principal stress and the minimum horizontal principal stress on the energy field in the overrun area of the retrieval working face is analyzed via numerical simulation, and the influence trend of the ground stress on the impact ground pressure is finally obtained. When the angle is 70°~90°, an energy peak in the overrun area of the working face reaches its maximum. The ratio of the minimum horizontal stress to the vertical stress is positively correlated with the energy concentration in the overrun area of the working face, but its change has a minimal effect on the energy distribution in the overrun area of the working face. When this ratio is increased from 0.6 to 1.4, the peak energy of the simulated working face only increases by 8.22%, and the energy concentration area remains basically unchanged.

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Section: Introductionmentioning

confidence: 99%

Study on the Distribution Trend of Rockburst and Ground Stress in the Hegang Mining Area

Pang

Xie

et al. 2023

Sustainability

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Regional prediction and prevention analysis of rockburst hazard based on the Gaussian process for binary classification

et al. 2022

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Rockburst is a complex dynamic disaster in coal mining and affected by many factors. To accurately predict the rockburst hazard among complex influencing factors, a prediction model of rockburst hazard based on the Gaussian process for binary classification (GPC) was proposed after the identification of the intrinsic relationship between multiple factors of coal mines and rockburst. Through computerized machine learning and integrated intelligent analysis, the non-linear mapping of rockburst hazard and its influencing factors was established. The multi-factor pattern recognition model was constructed using artificial intelligence. The prediction criteria of the rockburst hazard probability and the hazard probability value of the prediction area unit were determined by applying neural network and fuzzy inference methods. In addition, the rockburst hazardous zone was classified, and the corresponding technical scheme for the prevention was put forward. The validity and feasibility of the regional prediction of rockburst hazard based on GPC were verified in the engineering practice. This method is highly targeted and can improve the accuracy and precision of rockburst prediction, thus contributing to the safe and efficient production of coal mines.

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Mine Ground Pressure Monitoring and Early Warning Based on Deep Learning Data Analysis

Cited by 2 publications

References 26 publications

Study on the Distribution Trend of Rockburst and Ground Stress in the Hegang Mining Area

Study on the Distribution Trend of Rockburst and Ground Stress in the Hegang Mining Area

Regional prediction and prevention analysis of rockburst hazard based on the Gaussian process for binary classification

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