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Gold Science and Technology ›› 2021, Vol. 29 ›› Issue (3): 440-448.doi: 10.11872/j.issn.1005-2518.2021.03.144

• Mining Technology and Mine Management • Previous Articles    

Risk Recognition of Metal Mine Goaf Based on Relative Difference Function

Baoquan LIAO1,Yuxian KE1(),Chen QING2,Huaxi ZHANG1,Haoqi HUANG1,Lifa FANG1,Cheng WANG1,Tiejun TAO1,3   

  1. 1.School of Resources and Environmental Engineering，Jiangxi University of Science and Technology，Ganzhou 341000，Jiangxi，China
    2.Faculty of Foreign Studies，Jiangxi University of Science and Technology，Ganzhou 341000，Jiangxi，China
    3.College of Civil Engineering，Guizhou University，Guiyang 550025，Guizhou，China
  • Received:2020-08-05 Revised:2020-11-02 Online:2021-06-30 Published:2021-07-14
  • Contact: Yuxian KE E-mail:keyuxian@jxust.edu.cn

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Abstract:

Numerous of underground goaf left by metal mine mining not only bring a series of safety and environmental problems to the society，but also affect the development of mineral resources and the healthy and sustainable development of the national economy.The risk recognition of metal mine underground goaf is an important basis of its treatment，so accurately identify the danger of the goaf has become one of the problems to be solved urgently in the safety supervision of government departments and the safety production of mining enterprises.It has been difficult to accurately recognize metal mine underground goaf risk for many factors will influence the stability of underground goaf，and coexistence of quantitative and qualitative factors，and the existence of contradictions between these factors.In order to accurately identify the risk of metal mines underground goaf and manage the goaf economically and rationally，a risk recognition model of metal mine underground goaf based on relative difference function was established.First，a risk recognition index system of metal mine underground goaf containing 14 indexes was constructed according to the risk factors such as hydrogeological factors，goaf parameters，and other factors.Then the relative membership degree of the indexes to evaluation levels was calculated by relative difference function，and the combined weight of the indexes was determined by entropy weight method and analytical hierarchy process.Afterwards，the comprehensive relative membership degree and level characteristic value of metal mine underground goaf risk to different evaluation levels was calculated under four different combinations of distance parameter and optimization criteria parameter，and the level of metal mine underground goaf risk was then to be determined by the average level characteristic value.Furthermore，the whole process was applied to the risk recognition of eight underground goafs a tin mine and their calculated risk level was grade Ⅱ，grade Ⅱ，grade Ⅱ，grade Ⅰ，grade Ⅲ，grade Ⅰ，grade Ⅲ，grade Ⅰ，grade Ⅲ and grade Ⅰ，respectively.The calculation results fully consistent with the recognition results of uncertainty measurement and also well accordant with the practical situation，it also provides a helpful theoretical basis for the mine’s further treatment of underground goaf and safety production.The results show that the above established risk recognition model can self-verify the recognition results by changing its four combinations of its own parameters （distance parameter and optimization criteria parameter），which reflects the model’s overall control over the essential law of the unity and opposites of evaluation indexes.The model can improve the recognized reliability of underground goaf risk and its identification process is simple and efficient，which provides a new method for underground goaf risk recognition.

Key words: goaf, risk recognition, relative difference function, relative membership degree, combined weight, correlation degree, safety evaluation

CLC Number: 

  • X936

Fig.1

Relative difference function"

Fig.2

Evaluation indexes system of goaf risk identification"

Table 1

Classification criteria of quantitative evaluation indexes for goaf risk identification（Du et al.，2011；Gong et al.，2008）"

指标分级标准
Ⅰ级Ⅱ级Ⅲ级Ⅳ级
岩石质量指标x1/%［0，40］（40，50］（50，60］（60，100］
采空区埋藏深度x6/m（400，200］（200，100］（100，0］
采空区最大跨度x7/m（120，80］（80，40］（40，0］
采空区最大高度x8/m（30，20］（20，8］（8，0］
采空区平均高跨比x9（3，2］（2，1］（1，0］
采空区面积x10/m2（2 700，1 200］（1 200，800］（800，0］

Table 2

Classification criteria and assignment of qualitative indexes for goaf risk identification（Du et al.，2011；Gong et al.，2008）"

危险 级别赋值定性指标
地质构造x2

岩体结构

x3

地下可见水

x4

地下水体对围岩的影响x5相邻采空区情况x11矿柱尺寸和布置x12周围的开采影响x13工程布置x14
Ⅰ级［0，1］断层贯穿围岩松散结构长期有淋水围岩受水体影响较大影响范围内采空区面积较大，数量较多，相邻较近且比较集中，为采空区群无矿柱或布置不规范、矿柱已经严重受损受采场作业影响较大不合理
Ⅱ级（1，2］断层部分切割或褶皱影响大碎裂结构雨季有淋水围岩受水体影响影响范围内采空区面积大，数量多，但分布较为分散无矿柱或布置不规范、矿柱开始破坏受采场作业影响大部分合理
Ⅲ级（2，3］褶皱影响小层状结构

围岩可见

水迹

围岩受水体影响较小影响范围内采空区面积一般数量不多，且相邻较近有矿柱，但布置不规范受采场作业影响一般比较合理
Ⅳ级（3，4］无断层、褶皱完整块状结构无淋水水迹围岩周围无水体影响范围内无其他采空区，为孤立空区；或者空区位于6R之外（R为孤立空区半径）有矿柱，且布置规范无采场作业影响合理

Table 3

Survey and statistics on evaluation indexes for goaf risk identification"

采空区编号评价指标特征值
x1x2x3x4x5x6x7x8x9x10x11x12x13x14
1#380.80.82.82.81158515.04.83 1880.80.50.82.8
2#561.21.62.82.8115608.04.63 3350.81.60.84.0
3#350.80.82.82.81456214.52.42 5680.80.50.82.8
4#482.51.22.82.81807322.01.76 0181.50.50.82.8
5#431.20.82.82.82536016.52.63 5421.80.50.83.0
6#471.22.82.82.88316026.31.53 4002.50.51.72.8
7#550.51.23.53.52532615.85.32 6601.82.50.84.0
8#570.52.82.82.8909621.03.45 7291.51.81.54.0

Table 4

Results of goaf risk identification by relative difference function"

采空区编号H'H危险性级别
α=1，β=1α=2，β=1α=1，β=2α=2，β=2
1#1.9512.0811.5071.6821.806Ⅱ
2#1.7182.3081.5262.0431.899Ⅱ
3#2.0022.1612.3732.2152.188Ⅱ
4#1.4711.6011.0271.2021.326Ⅰ
5#2.9312.9083.0232.8502.928Ⅲ
6#1.2881.8781.0961.6131.469Ⅰ
7#2.8652.8242.4782.5462.678Ⅲ
8#1.4241.4011.5161.3431.421Ⅰ

Table 5

Comparison of recognition results between relative difference function and uncertainty measurement theory"

方法采空区危险性识别结果来源
1#2#3#4#5#6#7#8#
相对差异 函数ⅡⅡⅡⅠⅢⅠⅢⅠ本文
未确知测度 理论ⅡⅡⅡⅠⅢⅠⅢⅠ王纪鹏，2017
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