基于Data Mining技术的平顶山矿区不同赋存深度采动煤岩体巷道稳定性研究

doi:10.13722/j.cnki.jrme.2017.1120

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (469 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to explore the stabilities and differences of roadways in mining coal rock at different depths， the multiple linear regression and neural network model based on the Data Mining technology was used to perform the formula fitting and the weight analysis to the influencing factors of roof separation according to the monitoring data at different depths(700 m，850 m，1 050 m) within the area of Pingdingshan mine. The time series predictions to the roof separation and bolt stress have been carried out and the range of mining disturbance，roadway deformation and stress variation characteristics at different depths are revealed preliminarily. The distance from the mining face and the bolt stress was found to have the greatest influence on the roof separation，but the weights of them decrease by nearly 50% with the increasing of depth. The time series predictions of the roof separation and bolt stress show that the separation displacement of roadway roof and the bolt stress increase sharply at the depth of 1 050m with the advancement of mining face. At this depth，the predicted maximum value of bolt stress is 15 MPa，which is 2–3.5 times of the other two roadways，and the predicted maximum value of the roof separation displacement is 80 mm，which is 6–8 times of the other two roadways. With the increasing of depth，the variation of deformation and stress of roadways caused by the mining disturbance are more and more intensified， indicating that the close monitoring of stability and the proper measures of stability control should be applied for the roadway at depth over 1 000 m during the mining process.

Key words： mining engineering data mining depth roadway stability abscission layer deformation bolt stress

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	PENG Yuan1
	2
	ZHANG Ru1
	2
	WANG Man3
	4
	GAO Mingzhong1
	2
	XU Xiaolian5
	LI Anqiang1
	2
	ZHANG Zetian1
	2
	JIA Zheqiang1
	2

Cite this article:

PENG Yuan1,2,ZHANG Ru1, et al. Application of Data Mining in assessing the roadway stability of mining coal rock at different depths of mines in Pingdingshan#br#[J]. , 2018, 37(4): 949-960.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2017.1120 OR https://rockmech.whrsm.ac.cn/EN/Y2018/V37/I4/949

[1] IEA国际能源署. 煤炭市场中期报告[R]. 巴黎：IEA国际能源署，2014.(IEA International Energy Agency. Interim report on the coal market[R]. Paris：IEA International Energy Agency，2014.(in Chinese))
[2] 谢和平，高峰，鞠杨. 深部岩体力学研究与探索[J]. 岩石力学与工程学报，2015，34(11)：2 161–2 178.(XIE Heping，GAO Feng，JU Yang. Research and development of rock mechanics in deep ground engineering[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(11)：2 161–2 178.(in Chinese))
[3] 周宏伟，谢和平，左建平. 深部高地应力下岩石力学行为研究进展[J]. 力学进展，2005，35(1)：91–99.(ZHOU Hongwei，XIE Heping，ZUO Jianping. Developments in researches on mechanical behaviors of rocks under the condition of high ground pressure in the depths[J]. Advances in Mechanics，2005，35(1)：91–99.(in Chinese))
[4] 仵彦卿. 岩石孔隙率随地层深度变化规律研究[J]. 西安理工大学学报，2000，16(1)：6–8.(WU Yanqing. Research on the law of porosity of rock with depth[J]. Journal of Xi'an University of Technology，2000，16(1)：6–8.(in Chinese))
[5] 周宏伟，谢和平，左建平，等. 赋存深度对岩石力学参数影响的实验研究[J]. 科学通报，2010，(34)：3 276–3 284.(ZHOU Hongwei，XIE Heping，ZUO Jianping，et al. Experimental study of the effect of depth on mechanical parameters of rock[J]. Chinese Science Bulletin，2010，(34)：3 276–3 284.(in Chinese))
[6] VON KARMAN T. Festigkeitsversuche unter allseitigem Druck[J]. 1911，55：1 749–1 757.
[7] PATERSON M S. Experimental deformation and faulting in wombeyan marble[J]. Geological Society of America Bulletin，1958，69(4)：465.
[8] KIYOO M. Deformation and fracture of rocks under confining pressure(2)：elasticity and plasticity of some rocks[J]. Earthquake Research Institute，University of Tokyo，1965，42：349–379.
[9] MALAN D F. Time-dependent behaviour of deep level tabular excavations in hard rock[J]. Rock Mechanics and Rock Engineering，1999，32(2)：123–155.
[10] WOOD A M M. Tunnels for roads and motorways[J]. Quarterly Journal of Engineering Geology and Hydrogeology，1972，5(1/2)：111–126.
[11] 冯夏庭. 智能岩石力学导论[M]. 北京：科学出版社，2000：28–32.(FENG Xiating. Intelligent rock mechanics[M]. Beijing：Science Press，2000：28–32.(in Chinese))
[12] 唐晓萍. 数据挖掘与知识发现综述[J]. 电脑开发与应用，2002，15(4)：31–32.(TANG Xiaoping. Summerize of data mining(DM) and knowledge discovery database(KDD)[J]. Computer Development and Applications，2002，15(4)：31–32.(in Chinese))
[13] 钟晓，马少平，张钹，等. 数据挖掘综述[J]. 模式识别与人工智能，2001，14(1)：48–55.(ZHONG Xiao，MA Shaoping，ZHANG Bo，et al. Data mining：a survey[J]. Pattern Recognition Artificial Intelligence，2001，14(1)：48–55.(in Chinese))
[14] HE M，SOUSA L R E，MIRANDA T，et al. Rockburst laboratory tests database — Application of data mining techniques[J]. Engineering Geology，2015，185：116–130.
[15] 郭立. 深部硬岩岩爆倾向性动态预测模型及其应用[博士学位论文][D]. 长沙：中南大学，2004.(GUO Li. Dynamic prediction model for rockburst tendency of deep hard rock and its application[Ph. D. Thesis][D]. Changsha：Central South University，2004.(in Chinese))
[16] 马平波，冯夏庭，张治强，等. 基于数据挖掘的深部采场岩爆知识的自动获取[J]. 东北大学学报：自然科学版，2000，21(6)：630–633.(MA Pingbo，FENG Xiating，ZHANG Zhiqiang，et al. Self- extraction of rockburst know ledge of mining at great depthusing data mining[J]. Journal of Northeastern University：Natural Science，2000，21(6)：630–633.(in Chinese))
[17] 葛启发. 岩爆综合预测集成智能系统研究[博士学位论文][D]. 沈阳：东北大学，2006.(GE Qifa. Study of overall prediction of rockburst using integrated intelligent system[Ph. D. Thesis][D]. Shenyang：Northeastern University，2006.(in Chinese))
[18] 冯夏庭，马平波. 基于数据挖掘的地下硐室围岩稳定性判别[J]. 岩石力学与工程学报，2001，20(3)：306–309.(FENG Xiating，MA Pingbo. Identifying stability of underground openings based on data mining[J]. Chinese Journal of Rock Mechanics and Engineering，2001，20(3)：306–309.(in Chinese))
[19] 郑钢镖. 特厚煤层大断面煤巷顶板离层及锚固效应研究[博士学位论文][D]. 太原：太原理工大学，2006.(ZHENG Gangbiao. Study on the roof bedding separation and anchoring effect of extremely thick coal seam large section roadway[Ph. D. Thesis][D]. Taiyuan：Taiyuan University of Technology，2006.(in Chinese))
[20] 孔恒，马念杰，王梦恕，等. 基于顶板离层监测的锚固巷道稳定性控制[J]. 中国安全科学学报，2002，12(3)：55–58.(KONG Heng，MA Nianjie，WANG Mengshu，et al. Controlled stability of rock bolting roadways based on roof abscission layer monitor[J]. China Safety Science Journal，2002，12(3)：55–58.(in Chinese))
[21] 许家林，钱鸣高. 岩层控制关键层理论的应用研究与实践[J]. 中国矿业，2001，10(6)：54–56.(XU Jialin，QIAN Minggao. Study and application of dominant stratum theory for control of strata movement[J]. China Mining Magazine，2001，10(6)：54–56.(in Chinese))
[22] CHEN L D，SAKAGUCHI T，FROLICK M N. Data mining methods，applications，and tools[J]. Information Systems Management，2000，17(1)：65–70.
[23] 吴载斌，王斌会. 数据挖掘软件的介绍及其评价[J]. 计算机时代，2002，(7)：3–4.(WU Zaibin，WANG Binhui. Introduction and evaluation of data mining software[J]. Computer Era，2002，(7)：3–4.(in Chinese))
[24] CABY E C. Data mining using SAS applications[M]. Boca Raton：Chapman and Hall/CRC，2003：260–261.
[25] 刘严. 多元线性回归的数学模型[J]. 沈阳工程学院学报：自然科学版，2005，1(增1)：128–129.(LIU Yan. Mathematical model of multiple liner regression[J]. Journal of Shenyang Institute of Engineering：Natural Science，2005，1(Supp.1)：128–129.(in Chinese))
[26] 肖庆华. 岩石力学与工程中的数据挖掘技术应用[博士学位论文][D]. 南京：河海大学，2004.(XIAO Qinghua. Application of data mining technology in rock mechanics and engineering[Ph. D. Thesis][D]. Nanjing：Hohai University，2004.(in Chinese))
[27] 徐春晖，徐向东. 前馈型神经网络新学习算法的研究[J]. 清华大学学报：自然科学版，1999，(3)：1–3.(XU Chunhui，XU Xiangdong. Research of new learning method of feed-forward neural network[J]. Journal of Tsinghua University：Science and Technology，1999，(3)：1–3.(in Chinese))
[28] 朱永全，张清. 时间序列分析在隧道施工监测中的应用[J]. 岩石力学与工程学报，1996，15(4)：353–359.(ZHU Yongquan，ZHANG Qing. Application of time series analysis method to measured displacement in tunneling[J]. Chinese Journal of Rock Mechanics and Engineering，1996，15(4)：353–359.(in Chinese))
[29] 万小明，廖冬芽. 时序分析在地下洞室变形监测数据处理中的应用[J]. 水利技术监督，2010，18(1)：61–63.(WAN Xiaoming，LIAO Dongya. Application of time series analysis in data processing of deformation monitoring of underground cavern[J]. Technical Super Vision in Water Resources，2010，18(1)：61–63.(in Chinese))
[30] 布罗克韦尔. 时间序列与预测[M]. 2版. 北京：人民邮电出版社，2009：231–235.(BROCK Weil. Introduction to time series and forecasting[M]. Beijing：Posts and Telecom Press，2009：231–235.(in Chinese))