Classification of Open Pit Iron Mine Rock Mass Blastability Based on Concept Lattice and Rough Set

被引:0
作者
Shuliang Wu
Shan Yang
Qingya Wang
机构
[1] East China University of Technology,State Key Laboratory of Nuclear Resources and Environment
[2] East China University of Technology,School of Earth Sciences
[3] Central South University,School of Resources and Safety Engineering
来源
Geotechnical and Geological Engineering | 2020年 / 38卷
关键词
Rock mass blastability; Concept lattice; Dominance rough set; Index reduction; Classification rules;
D O I
暂无
中图分类号
学科分类号
摘要
In order to improve the efficiency of classification of rock mass blastability, a method for classification of rock mass blastability based on concept lattice and rough set was proposed. Four control factors of rock mass blastability, i.e. specific gravity, static tensile strength, integrity coefficient of rock mass, and dynamic strength under impact, were selected as the evaluation indexes. The concept lattice was used to analyze and reduced the index, and two irreducible indexes of specific gravity and dynamic strength under impact were obtained, then the rules of classification of rock mass blastability were generate through rough set. The rules of classification of rock mass blastability were applied to the classification of rock mass blastability in Nanfen open pit iron mine, and compared with the other three classification methods. The results show that the classification results using method for classification of rock mass blastability based on concept lattice and rough set in accordance with which from the other classification methods. This method reduces the classification index, and does not need to determine the index weight. It improves the efficiency of classification of rock mass blastability.
引用
收藏
页码:449 / 458
页数:9
相关论文
共 53 条
[1]  
Alipour A(2018)An application of fuzzy sets to the blastability index (BI) used in rock engineering Periodica Polytechn Civ Eng 62 580-589
[2]  
Mokharian M(2010)Prediction of the blastability designation of rock masses using fuzzy sets Int J Rock Mech Min Sci 47 1126-1140
[3]  
Chehreghani S(2015)A decision-theoretic rough set approach for dynamic data mining IEEE Trans Fuzzy Syst 23 1958-1970
[4]  
Azimi Y(2014)Blastability quality system (BQS) for using it, in bedrock excavation Struct Eng Mech 51 823-845
[5]  
Osanloo M(2015)Unascertained average clustering for classification of rock mass blastability and its application J Cent South Univ 46 2157-2161
[6]  
Aakbarpour-Shirazi M(2015)Application of PCA, SVR, and ANFIS for modeling of rock fragmentation Arab J Geosci 8 6881-6893
[7]  
Bazzazi AA(2008)Classification for rockmass blastability based on attribute recognition theory Metal Mine 5 32-34+48
[8]  
Chen H(2015)Blastability evaluation for rock mass fragmentation in Iran central iron ore mines Int J Min Sci Technol 25 59-66
[9]  
Li T(2010)Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks Eng Comput 47 476-480
[10]  
Luo C(2006)A method for rock-mass blastability classification based on weighted clustering analysis J Univ Sci Technol Beijing 28 324-329