采矿与安全工程学报 ›› 2012, Vol. 29 ›› Issue (4): 527-533.

• 论文 • 上一篇    下一篇

基于粗糙集与遗传算法的岩爆倾向性预测方法研究

  

  1. 华北水利水电学院资源与环境学院,河南  郑州  450011
  • 收稿日期:2011-11-17 出版日期:2012-07-15 发布日期:2012-05-23
  • 作者简介:李亚丽(1978-),女,河南省宝丰县人,讲师,博士,从事地质灾害方面的研究。 E-mail:liyali@ncwu.edu.cn Tel:0371-69127351
  • 基金资助:

    国家自然科学基金项目(51109082)

    河南省教育厅自然科学研究计划项目(12A410001)

    华北水利水电学院高层次人才科研启动项目(201112)

Prediction Method for Rockburst Tendency Based on Rough Sets and Genetic Algorithm

  • Received:2011-11-17 Online:2012-07-15 Published:2012-05-23

摘要: 结合粗糙集理论与遗传算法,提出一种新的岩爆倾向性预测方法。以碳化矿床采场工作面岩爆倾向性预测为例,选取9种岩爆影响因素作为条件属性,将采场工作面岩爆倾向性结果作为决策属性,选取77组采场岩爆或非岩爆工程实例数据,构成岩爆倾向性预测样本集合。将样本集合随机分为训练样本和测试样本2个集合,对2个集合的属性值进行量化,建立训练样本决策表与测试样本决策表。采用遗传算法对训练样本决策表的条件属性进行约简,得出条件属性的最小约简和核。应用粗糙集理论从约简结果中提取精简的岩爆倾向性判别规则集。用获得的规则集对测试样本决策表中的岩爆倾向性进行预测,并与实际结果对比,验证了判别规则集的可行性和有效性。结果表明,基于粗糙集与遗传算法的岩爆倾向性预测方法更具客观性和科学性,有较高的工程实用价值。

关键词: 岩爆倾向性, 预测, 粗糙集, 遗传算法, 判别规则

Abstract: A new method based on rough sets and genetic algorithm was presented to predict the rockburst tendency in this paper. Taking the rockburst tendency of working face of the stope for example, the influencing factors of rockburst tendency were taken as the condition attributes, and the result of rockburst tendency of working face was taken as the decision attribute. The engineering data were chosen to make up the sample set. The sample set was randomly divided into two parts. One was the training sample set, and the other was the testing sample set. The attribute values of the two sets were quantified respectively, and then the decision tables of the training and testing samples were set up. Genetic algorithm was used to reduce the decision table of the training samples. Rough sets were used to extract the decision rules of rockburst tendency from the reduction results. Then the rockburst tendency of the testing samples were predicted by these decision rules. The results show that the predicted results agree well with the actual ones, which proves that the method is effective and available and can be applied to predict the rockburst tendency in actual engineering.

Key words: rockburst tendency, prediction, rough sets, genetic algorithm, decision rules