Underground metal mine goaf hazard degree grading evaluation method

Abstract

The invention discloses an underground metal mine goaf hazard degree grading evaluation method, which comprises the following steps: firstly, selecting 16 indexes to construct an underground metal mine goaf hazard degree grading evaluation index system, dividing hazard degrees into four grades, and determining a grading standard; then, determining the comprehensive weight of each index by adoptingan AHP, entropy weight method and game theory combined weighting method; establishing a matter element extension model for goaf risk degree evaluation; substituting the weight value of each evaluation index into the matter element extension model to obtain each index correlation degree and a comprehensive correlation degree value which belong to each risk degree grade; and finally, determining the danger degree level according to the maximum correlation degree principle. According to the method, the goaf hazard level can be evaluated comprehensively, objectively and accurately, the evaluationprocess is clear, the evaluation result is objective, and a new method can be provided for grading evaluation of the underground metal mine goaf hazard level.

Description

technical field [0001] The invention belongs to the field of underground mine safety, and in particular relates to a method for grading and evaluating the risk of goafs in underground metal mines. Background technique [0002] Gobs are one of the major sources of danger in underground metal mining. There are a large number of goafs in many large underground metal mines in my country. The existence of a large number of goafs deteriorates mining conditions and increases the amount of hidden resources, which can easily lead to major disasters such as large-scale caving in underground goafs, surface subsidence, and roadway deformation, resulting in serious casualties and equipment damage. Therefore, on the basis of implementing fine detection of goafs, it is of great practical significance to carry out risk classification evaluation of goafs for formulating goaf treatment measures and ensuring safe production in mines. [0003] Among the published results, methods such as AHP, ...

AI Technical Summary

Problems solved by technology

However, due to the differences in the location, spatial form, volume, engineering geological conditions, etc. of each goaf, there are many factors involved, which brings difficulties to the risk classification and evaluation of goafs

In addition, various evaluation methods also have their own limitations, resulting in evaluation results that cannot accurately, objectively and comprehensively reflect the risk level of the goaf

For example, when the weight is determined by the AHP, it is easily affected by subjective factors; when the weight is determined by the entropy weight method, it relies too much on objective information and ignores the attributes of the index itself; the BP neural network method is easy to fall into local minimum points and slow convergence speed; Extenics evaluation often only uses one method to determine the weight, resulting in the evaluation results inconsistent with the actual situation

Therefore, it is difficult to accurately and quickly evaluate the risk level of gobs by using a certain evaluation method.

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