Prediction and Evaluation of Rockburst Based on Depth Neural Network

Zhang, Jin; Wang, Mengxue; Xi, Chuanhao

doi:10.1155/2021/8248443

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…This method was also successfully applied for the prediction of the surrounding rockburst tendency in the field. With the development of rock mechanics analysis software and risk analysis methods [57][58][59][60], Zhang et al [61] established a neural network model for rockburst prediction, divided a given region into areas with different levels of rockburst danger, and proposed corresponding treatment methods based on these different levels of rockburst danger for preventing rockburst disasters. Based on field measurement data and experimental data, combined with the existing rockburst situation, numerical simulation and neural network methods were used to classify rockbursts.…”

Section: Introductionmentioning

confidence: 99%

A Method for Dividing Rockburst Risk Zones—A Case Study of the Hegang Mining Area in China

Pang,

Yang,

Yang

et al. 2023

Sustainability

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Rockbursts are serious mine disasters. Through the division of rockburst risk zones, risks can be predicted in advance and measures can be implemented to prevent disasters. In this paper, taking the Hegang mining area as an example, we propose a method for dividing rockburst risk zones based on in-situ stress measurements. First, 24 survey points were established in the Hegang mining area to measure the in-situ stress. Second, based on the in-situ stress measurement data, eight representative prospecting lines were selected to establish a numerical model, and the distribution characteristics of the in-situ stress field at mining elevations of −330 m and −450 m in the Hegang mining area were obtained via the linear differential method. Afterward, division criteria for rockburst risk zones were proposed in accordance with the energy criterion and the minimum energy principle. Finally, the Hegang mining area was divided into rockburst risk zones in accordance with the in-situ microseismic monitoring data and simulation results for the in-situ stress field. Coal seam #3 was chosen as an example to illustrate rockburst risk-zone division in the Hegang mining area considering the division criterion proposed herein, and a rockburst risk zoning map of coal seam #3 in the Hegang mining area was finally obtained. The locations of the five rockbursts that have occurred in coal seam #3 of the Hegang mining area were marked on the risk zoning map and were found to have occurred in the threatened zone. Thus, it was shown that the proposed rockburst risk-zone division method is reasonable. Therefore, the results of this study could serve as a reference for the division of rockburst risk zones.

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Section: Introductionmentioning

confidence: 99%

A Method for Dividing Rockburst Risk Zones—A Case Study of the Hegang Mining Area in China

Pang,

Yang,

Yang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the prediction of rockburst hazards in mines, data from multiple sources are analyzed, and then machine learning algorithms are used to continuously learn from previous rockburst events and train computer models. The study of "neural network + machine learning" artificial intelligence prediction techniques allows monitoring and predicting the likelihood of rockburst hazards in coal mines (Wang et al, 2021b;Ke et al, 2021;Zhang et al, 2021). In order to accurately predict rockburst hazards under complex conditions, a rockburst hazard prediction model based on the Gaussian process for binary classification (GPC) was proposed (Hui and Zhang, 2020;Davis et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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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Research on the Identification of Precursor Information of Limestone Rockburst under Triaxial Unloading Conditions

et al. 2022

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Based on the energy evolution of limestone and acoustic emission evolution law, this paper takes the limestone unloading energy evolution law and acoustic emission cumulative ringing count, cumulative energy, RA value, acoustic emission information entropy, and acoustic emission damage as precursor discrimination indicators for the occurrence of rock explosion. Study of the impact of the unloading rate on the rock explosion precursors indicates that with increasing unloading rate there are more dramatic changes in the degree of each precursor discrimination indicators and rock ruptures within a short period of time before the completion of the indicators. The establishment of the precursor response coefficient reveals that the precursor response is the strongest when the rock rupture acoustic emission RA value time is the earliest. The remaining four precursor discrimination indicators of rock rupture with different loading paths show fluctuations in the identification capacity of the state. The research results can provide some reference for identifying rockburst precursor information.

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Prediction and Evaluation of Rockburst Based on Depth Neural Network

Cited by 5 publications

References 30 publications

A Method for Dividing Rockburst Risk Zones—A Case Study of the Hegang Mining Area in China

A Method for Dividing Rockburst Risk Zones—A Case Study of the Hegang Mining Area in China

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

Research on the Identification of Precursor Information of Limestone Rockburst under Triaxial Unloading Conditions

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