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Rockburst dynamic prediction method based on BP neural network modeling

A BP neural network and rockburst technology, applied in biological neural network models, special data processing applications, instruments, etc., can solve problems such as inability to predict rockburst, and achieve high reliability, easy implementation, and high precision.

Inactive Publication Date: 2016-01-20
SANSHANDAO GOLD MINE SHANDONG GOLD MINING LAIZHOU
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Problems solved by technology

Traditional methods cannot accurately predict rock burst

Method used

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  • Rockburst dynamic prediction method based on BP neural network modeling
  • Rockburst dynamic prediction method based on BP neural network modeling
  • Rockburst dynamic prediction method based on BP neural network modeling

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Embodiment Construction

[0015] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0016] Such as figure 1 , figure 2 Shown, the present invention provides a kind of dynamic prediction method of rock burst based on BP neural network modeling, and its steps are as follows:

[0017] 1) Determine the impact factors of rock burst according to the actual situation of the mine, and collect the characteristic data of these influence factors, that is, obtain the initial chromosome;

[0018] Among them, the influencing factors include the following eight items:

[0019] (1) Thickness of the ore body: the thickness of the rock mass directly reflects the properties of the rock mass, and the change in the thickness of the rock layer represents the change in its accumulated energy;

[0020] (2) Dip angle of ore body: the dip angle of rock mass reflects the occurrence conditions of rock mass;

[0021] (3) Buried depth: The buried depth of th...

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Abstract

The present invention relates to a rockburst dynamic prediction method based on BP neural network modeling. The method comprises the steps of: determining and acquiring rockburst influence factors; performing quantification processing on qualitative description parts in influence factor indexes, and obtaining an initial population; performing BP neural network training on the eight acquired influence factors separately; optimizing a number of neurons, an algorithm learning rate and momentum factors by using a genetic algorithm, and obtaining an optimal hidden layer node number; and performing prediction on rockburst of a mine by using a BP neural network algorithm model obtained through training, and obtaining a risk level of the rockburst of the mine. The method provided by the present invention has relatively high reliability, overcomes the defect of no association between the rockburst and the influence factors of the rockburst in the current rockburst prediction process, implements middle and short term dynamic prediction on the rockburst, and can be widely applied to the field of mine rockburst prediction.

Description

technical field [0001] The invention relates to a method for predicting mine rock burst, in particular to a dynamic prediction method for rock burst based on BP neural network modeling. Background technique [0002] As a special manifestation of mine pressure, mine rock burst refers to a kind of ground pressure disaster caused by the joint action of mining stress, tectonic stress, ground stress and environmental disturbance when mining reaches a certain depth. In the process of rock burst, the ore body is often violently crushed and thrown into the roadway, thereby causing damage to the support, roadway, and working face, and even causing casualties. At the same time, the surrounding rock mass is also accompanied by disasters such as earthquakes. Most mines have varying degrees of rock burst threats. [0003] The research on rock burst mainly focuses on analyzing its formation mechanism and researching its prediction method. And various prediction methods need to be aimed...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/02
Inventor 何顺斌陶晓杰王峰曲伟霞方建平石明超徐婧冯瑞军
Owner SANSHANDAO GOLD MINE SHANDONG GOLD MINING LAIZHOU
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