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黄金科学技术 ›› 2017, Vol. 25 ›› Issue (3): 98-107.doi: 10.11872/j.issn.1005-2518.2017.03.098

• 采选技术与矿山管理 • 上一篇    下一篇

基于组合预测与变精度粗糙模糊集的采空区稳定性评价

邓高1,杨珊 1,2*   

  1. 1.中南大学资源与安全工程学院,湖南  长沙   410083;
    2.中南大学安全科学与工程博士后流动站,湖南  长沙   410083
  • 收稿日期:2016-01-20 修回日期:2016-05-23 出版日期:2017-06-30 发布日期:2017-09-11
  • 通讯作者: 杨珊(1983-),男,湖北监利人,讲师,从事矿业经济与采矿系统工程研究工作。1652102421@qq.com
  • 作者简介:邓高 (1975-),男,湖南长沙人,博士研究生,从事资源与环境经济学以及企业管理研究工作。denggao@chinavalin.com
  • 基金资助:

    国家自然科学基金青年基金项目“基于人工智能的矿山技术经济指标动态优化”(编号:51404305)和中国博士后科学基金项目“辰州矿业采掘计划可视化编制与优化研究”(编号:2015M572269)联合资助

Stability Evaluation of Goafs Based on Combined Forecasting and Variable Precision Rough Fuzzy Set

DENG Gao1,YANG Shan 1,2   

  1. 1.School of Resources and Safety Engineering,Central South University,Changsha   410083,Hunan,China;
    2.Safety Science and Engineering Postdoctoral Research Station,Central South University,Changsha     410083,Hunan,China
  • Received:2016-01-20 Revised:2016-05-23 Online:2017-06-30 Published:2017-09-11

摘要:

为了准确评价矿山采空区稳定性,分析其影响因素,结合采空区顶板下沉位移前期检测数据,先利用组合预测理论对顶板下沉位移的多种模型预测结果进行了组合,然后根据组合预测值对采空区稳定性进行评判。以采空区稳定性评价作为决策属性,以其影响因素作为条件属性,利用变精度粗糙模糊集对采空区稳定性评价知识系统进行概率决策分析,得到13条β为70%的β-约简概率决策规则。结果表明:对于该矿山而言,采空区面积、地应力大小和临时支护方式是评判采空区稳定性的决定性影响因素,所得概率决策规则的分类质量为62.96%,分类质量较高,可为采空区稳定性判定及规律分析提供参考。

关键词: 采空区, 稳定性评价, 组合预测, BP神经网络, 隶属函数, 数学理论模型, 粗糙集, 模糊综合评制法

Abstract:

In order to accurately evaluate stability of mine goafs and analyze its main influential factors,according to the preliminary test data of goafs roof subsidence displacement,combination forecasting was applied to compose final predictive values of roof subsidence displacement by vary models,and then goafs stability was judged by resultant predictive values.Goafs stability was set as decision attribute,and its influential factors were set as condition attributes,variable precision rough fuzzy set was applied to probabilistically decision analyze assessment knowledge system of goafs stability,and 13 β- decision rules which β was 70% were got. It shows that goaf area,crustal stress size and temporary support way are decisive factors to goafs stability,and the classification quality of probabilistic decision rules is 62.96%,it is high,which can be references for stability judge and its law analysis.

Key words: goaf, stability evaluation, combined forecasting, BP neural network, membership function, mathematical model, rough set, fuzzy comprehensive evaluation method

中图分类号: 

  • TD325 
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