img

Wechat

Adv. Search

Gold Science and Technology ›› 2018, Vol. 26 ›› Issue (1): 49-55.doi: 10.11872/j.issn.1005-2518.2018.01.049

Previous Articles     Next Articles

PCA-RF Model for the Classification of Rock Mass Quality and Its Application

LIU Qiang,LI Xibing,LIANG Weizhang   

  1. chool of Resources and Safety Engineering,Central South University,Changsha    410083,Hunan,China
  • Received:2016-12-12 Revised:2017-02-26 Online:2018-02-28 Published:2018-05-19

Abstract:

In order to determine the classification of rock mass quality more reasonably,PCA-RF classification model of rock mass quality was proposed which combined with principal component analysis and random forest algorithm.Five classification indexes were chosen which can fully reflect the rock mass quality category.The correlation analysis of indexes was calculated by principal component analysis,and three principal components were abtained by accumulated variance devoted rate,which can eliminate the correlation between each index and reduce the inputs of model.Then,the classification of rock mass quality was determined by random forest model.Twenty sets field data were chosen as training samples,and ten sets field data were chosen as testing samples.The generalization errors were estimated by cross-validation method.The results show that the classification results satisfyingly agree with the actual results at the average accuracy of 96.7%,and the probability distribution of classifications that can reflect the complexity of rock mass quality was calculated simultaneously,which can provide more detailed reference for engineering construction.

Key words: rock mass quality, principal component analysis, random forest, correlation of index, cross-validation, generalization errors, classification

CLC Number: 

  • TU457
[ 1 ] Cai M,Kaiser P K.Visualization of rock mass classification systems[J].Geotechnical and Geological Engineering,2006,24(4):1089-1102.
[ 2 ] 蔡美峰. 岩石力学与工程[M].北京:科学出版社,2002:10-25.Cai Meifeng.Rock Mechanics and Engineering[M].Beijing:Science Press,2002:10-25.
[ 3 ] 陈昌彦,王贵荣.各类岩体质量评价方法的相关性探讨[J].岩石力学与工程学报,2002,21(12):1894-1900.Chen Changyan,Wang Guirong.Discussion on the interrelation of various rock mass quality classification systems at home and abroad[J].Chinese Journal of Rock Mechanics and Engineering,2002,21(12):1894-1900.
[ 4 ] 江飞飞,李向东,盛佳,等.软弱破碎岩体工程地质调查与质量评价[J].黄金科学技术,2016,24(3):94-99.Jiang Feifei,Li Xiangdong,Sheng Jia,et al.Engineering geological investigation and quality evaluation of soft fractured rock[J].Gold Science and Technology,2016,24(3):94-99.
[ 5 ] 赵明阶,吴德伦.工程岩体的超声波分类及强度预测[J].岩石力学与工程学报,2000,19(1):89-92.Zhao Mingjie,Wu Delun.The ultrasonic identification of rock mass classification and rock mass strength prediction[J].Chinese Journal of Rock Mechanics and Engineering,2000,19(1):89-92.
[ 6 ] 董金奎,申延,邱俊刚.焦家金矿寺庄矿区岩体节理裂隙调查与矿岩稳定性分析[J].黄金科学技术,2012,20(2):58-61.Dong Jinkui,Shen Yan,Qiu Jungang.Investigation of joint fissure and analysis of ore-rock stability in Sizhuang mining of Jiaojia gold deposit[J].Gold Science and Technology,2012,20(2):58-61.
[ 7 ] Palmstrom A,Broch E.Use and misuse of rock mass classification systems with particular reference to the Q-system[J].Tunneling and Underground Space Technology,2006,21(6):575-593.
[ 8 ] 贾明涛,王李管.基于区域化变量及RMR评价体系的金川Ⅲ矿区矿岩质量评价[J].岩土力学,2010,31(6):1907-1912.Jia Mingtao,Wang Liguan.Evaluation of rock mass quality based on regionalization variable optimal estimation theory and RMR system in Jinchuan mine No.3[J].Rock and Soil Mechanics,2010,31(6):1907-1912.
[ 9 ] 邬爱清,汪斌.基于岩体质量指标BQ的岩质边坡工程岩体分级方法[J].岩石力学与工程学报,2014,33(4):699-706.Wu Aiqing,Wang Bin.Engineering rock mass classification method based on rock mass quality index BQ for rock slope[J].Chinese Journal of Rock Mechanics and Engineering,2014,33(4):699-706.
[ 10 ] 连建发,慎乃齐,张杰坤.分形理论在岩体质量评价中的应用研究[J].岩石力学与工程学报,2001,20(增):1695-1698.Lian Jianfa,Shen Naiqi,Zhang Jiekun.Application research on fractal theory in rock mass quality evaluation[J].Chinese Journal of Rock Mechanics and Engineering,2001,20(Supp.):1695-1698.
[ 11 ] 钱兆明,任高峰,褚夫蛟,等. 基于PCA法和Fisher判别分析法的岩体质量等级分类[J].岩土力学,2016,37(增2):427-432.Qian Zhaoming,Ren Gaofeng,Chu Fujiao,et al. Rock mass quality classification based on PCA and Fisher discrimination analysis[J].Rock and Soil Mechanics,2016,37(Supp.2):427-432.
[ 12 ] 李强.BP神经网络在工程岩体质量分级中的应用研究[J].西北地震学报,2002,24(3):220-224.Li Qiang.Study on the application of BP nervous network in classification of rock mass quality[J].Northwestern Seismological Journal,2002,24(3):220-224.
[ 13 ] 吴肖坤,刘敦文,江帆,等.基于特征值域的可拓学理论的工程岩体质量评价[J].黄金科学技术,2015,23(2):68-74.Wu Xiaokun,Liu Dunwen,Jiang Fan,et al. Extension theory of engineering rock mass quality evaluation based on the feature range[J].Gold Science and Technology,2015,23(2):68-74.
[ 14 ] 胡建华,尚俊龙,雷涛.基于RS-TOPSIS法的地下工程岩体质量评价[J].中南大学学报(自然科学版),2012,43(11):4412-4419.Hu Jianhua,Shang Junlong,Lei Tao.Rock mass quality evaluation of underground engineering based on RS-TOPSIS method[J].Journal of Central South University (Science and Technology),2012,43(11):4412-4419.
[ 15 ] Breiman L. Random forests[J].Machine Learning,2001,45(1):5-32.
[ 16 ] Peters J,Baets B D,Verhoest N,et al.Random forests as a tool for ecohydrological distribution modelling[J].Ecological Modelling,2007,207(2/3/4):304−318.
[ 17 ] Lee S L,Kouzani A Z,Hu E J.Random forest based lung nodule classification aided by clustering[J].Computerized Medical Imaging & Graphics,2010,34(7):535-542.
[ 18 ] 王茵茵,齐雁冰,陈洋,等.基于多分辨率遥感数据与随机森林算法的土壤有机质预测研究[J].土壤学报,2016,53(2):342-354.Wang Yinyin,Qi Yanbing,Chen Yang,et al.Prediction of soil organic matter based on multi-resolution remote sensing data and random forest algorithm[J].Acta Pedologica Sinica,2016,53(2):342-354.
[ 19 ] Pearson K. Principal components analysis[J].The London,Edinburgh and Dublin Philosophical Magazine and Journal,1901,6(2):566.
[ 20 ] 李夕兵,朱玮,刘伟军,等.基于主成分分析法与RBF神经网络的岩体可爆性研究[J].黄金科学技术,2015,23(6):58-63.Li Xibing,Zhu Wei,Liu Weijun,et al. Research on rock mass blastability based on principal component analysis and RBF neural network[J].Gold Science and Technology,2015,23(6):58-63.
[ 21 ] 周松林,茆美琴,苏建徽.基于主成分分析与人工神经网络的风电功率预测[J].电网技术,2011,35(9):128-132.Zhou Songlin,Mao Meiqin,Su Jianhui.Prediction of wind power based on principal component analysis and artificial neural network[J].Power System Technology,2011,35(9):128-132.
[ 22 ] 曹正凤. 随机森林算法优化研究[D].北京:首都经济贸易大学,2014.Cao Zhengfeng. Study on Optimization of Random Forests Algorithm[D].Beijing:Capital University of Economics and Business,2014.
[ 23 ] 中华人民共和国国家标准编写组.工程岩体分级标准:GB50218-94[S].北京:中国计划出版社,1995.The National Standards Compilation Group of People’s Republic of China.Standard for engineering classification of rock masses:GB50218-94[S].Beijing:China Planning Press,1995.
 
[1] MA Fengshan,GUO Jie,LI Kepeng,LU Rong,ZHANG Hongxun,LI Wei. Monitoring and Research for the Deformation of Mine Backfill and Roof Surrounding Rock when Exploiting Sanshandao Seabed Gold Mine [J]. Gold Science and Technology, 2016, 24(4): 66-72.
[2] LI Kepeng,MA Fengshan,GUO Jie,LU Rong,ZHANG Hongxun,LI Wei. Numerical Simulation of Mine Backfill and Surrounding Rock Deformation when Exploiting Sanshandao Seabed Gold Mine [J]. Gold Science and Technology, 2016, 24(4): 73-80.
[3] LIU Kewei,ZENG Qingtian,LIU Dong. 3D Visualization and Numerical Modeling of Complicated Geological Structure in Slope Engineering [J]. Gold Science and Technology, 2016, 24(2): 83-89.
[4] GUO Guangjun,LIU Mingjun,XU Yongbin,CHENG Wei,YE Yanling,ZHENG Xiaoli,GAO Haifen. Stability Classification of Engineering Rock Body in Jiaojia Gold Mine,Shandong Province [J]. J4, 2012, 20(4): 71-75.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!