基于距离判别分析的矿山岩爆倾向性评价

doi:10.11872/j.issn.1005-2518.2021.05.190

Abstract

Abstract:

Rockburst as a common ground pressure disaster in deep mining，always affect the safety of underground staff，its unique suddenness makes the evaluation of rock explosion tendency become a subject that countless scholars continue to explore.Mathematical methods are widely used in this field.Distance judgment analysis is applied to academic and engineering by many scholars with its unique judgment algorithm.In order to scientifically and effectively apply the distance discriminant analysis method to evaluate the deep rockburst tendency grade of a certain mine，brought together a large number of domestic and international mine rock burst data，and combined with the mine deep situation，six rock burst tendency index were determined.9 sites were selected to be tested at the mine， and the aesthetic experiment was carried out to obtain the rockbrust index of the location of the belt.The Mahalanobis distance theory was used to establish a judgment criterion to determine rock burst tendency of the test data，and the accuracy of the judgment criterion was tested by the back-substitution misjudgment rate and the cross misjudgment rate.The evaluation results are consistent with the actual situation of the mine.The results show thatthe tendency of rockbrust at the site of X2，X3，X4，X5，X7，X8 and X9 to be measured at a metal mine is in the slight level of rockbrust grade，the tendency of rockbrust at the site of X6 is in the medium level of rockbrust grade，the tendency of rockbrust at the site of X1 is in the high level of rockbrust grade.The actual situation of the mine shows that high rockbrust phenomenon occur at the site of X1.Research shows the method has good applicability and effectiveness in the evaluation of mine rockburst tendency.

Key words: rockburst, distance discriminant analysis, Mahalanobis distance, rockbrust tendency, standard evaluation, rock mechanics, hard rock mine

CLC Number:

TD31

Guoyan ZHAO,Chengkai DANG,Huanxin LIU,Yang LIU,Quri XIAO,Yang LI,Liqiang CHEN,Wenjie MAO. Evaluation of Mine Rockburst Tendency Based on the Distance Discrimination Analysis[J].Gold Science and Technology, 2021, 29(5): 690-697.

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References 0

	Bai Mingzhou，Wang Lianjun，Xu Zhaoyi，2002.Study on a neural network model and its application in prediction the risk of rock blast［J］.Chinese Journal of Safety Sciences，12（4）：68-72.
	Bi Aorui，Luo Zhengshan，Wang Xiaowan，al et，2018.Extension model weighted by vague sets and entropy method and its engineering application［J］.Systems Engineering，36（2）：112-120.
	Chen Jianhong，Yu Caoyuan，Deng Dongsheng，2017.Risk assessment of bedded rock roadway roof fall based on AHP and matter-element TOPSIS method［J］.Gold Science and Technology，25（1）：55-60.
	Fengqiang Gon，Li Xibing，2007.A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application［J］.Journal of Rock Mechanicals and Engineering，26（5）：1012-1018.
	Huang Yuren，Mao Jianxi，Lin Chaoyang，al et，2014. The multi-criteria evaluation of rockburst proneness on deep buried large tunnel［J］.Journal of Railway Engineering Science，31（7）：89-94.
	Li C C，2020.Principles and methods of rock support for rockburst control［J］.Journal of Rock Mechanics and Geotechnical Engineering，11：1-15.
	Li Kegang，Li Mingliang，Qin Qingci，2020.Research on evaluation method of rock burst tendency based on improved comprehensive weighting［J］.Chinese Journal of Rock Mechanics and Engineering，39（1）：2751-2762.
	Li Ning，Wang Li Guan，Jia Mingtao，2017. Rockburst prediction based on rough set theory and support vector machine［J］.Journal of Central South University（Sciences and Technology），48（5）：1268-1275.
	Li Pengxiang，Chen Bingrui，Zhou Yangyi，al et，2019.Research progress of rockburst prediction and early warning in hard rock underground engineering［J］.Journal of China Coal Society，44（Supp.2）：447-465.
	Li Renhao，Gu Helong，Li Xibing，al et，2020.A PSO-RBF neural network model for rockburst Tendency prediction［J］.Gold Science and Technology，28（1）：134-141.
	Li Shulin，Feng Xiating，Wang Yongjia，al et，2001.Evaluation of rockburst proneness in a deep hard rock mine［J］.Journal of Northeastern university，22（1）：60-63.
	Li Tongtong，Wang Xi，Liu Huanxin，al et，2020.Research and application of T-FME rockburst propensity prediction model based on combination weighting［J］.Gold Science and Technology，28（4）：565-574.
	Sun Chensheng，2019. A prediction model of rock burst in tunnel based on the improved MATLAB-BP neural network［J］.Journal of Chongqing Jiaotong University （Natural Sciences），38（10）：41-49.
	Tang Zhili，Xu Qianjun，2020. Rockburst prediction based on nine machine learning algorithms［J］.Chinese Journal of Rock Mechanics and Engineering，39（4）：773-781.
	Tian Rui，Meng Haidong，Chen Shijiang，al et，2020.Comparative study on three rockburst prediction models of intensity classification based on machine learning［J］.Gold Science and Technology，28（6）：920-926.
	Wang Jin，Li Xibing，Yang Jinlin，2011.A weighted Mahalanobis distance discriminant analysis for predicting rock-burst in deep hard rocks test results ［J］.Journal of Mining and Safety Engineering，28（3）：395-400.
	Wen Z J，Wang X，Tan Y L，al et，2016.A study of rockburst hazard evaluation method in coal mine［J］.Shock and Vibration，pt.4：1-9.
	Wu Libin，Li Boying，Zhang Kongsheng，al et，2017.MATLAB Data Analysis Methods［M］.Beijing：China Machine Press：111-145.
	Xie Xuebin，Li Dexuan，Kong Lingyan，al et，2020.Rockburst propensity prediction model based on CROTIC-XGB algorithm［J］.Chinese Journal of Rock Mechanics and Engineering，39（10）：1975-1982.
	Xu Rui，Hou Kuikui，Wang Xi，al et，2020.Combined prediction model of rockburst intensity based on kernel principal component analysis and SVM［J］.Gold Science and Technology，28（4）：575-584.
	Yi Yongliang，Cao Ping，Pu Chengzhi，2010.Multi-factorial comprehensive estimation for Jinchuan’s deep typical rockburst tendency［J］.Science and Technology Review，28（2）：76-80.
	Zhao H B，Chen B R，2020.Data-driven model for rockburst pre-diction［J］.Mathematical Problems in Engineering，（4）：1-14.
	Zhao H B，Chen B R，Zhu C X，al et，2021.Decision tree model for rockburst prediction based on microseismic monitoring［J］.Advances in Civil Engineering，（3）：1-14.
	Zhou Koping，Gu Desheng，2004.Application of GIS-based neural network with fuzzy self-organization to assessment of rockburst tendency［J］.Journal of Rock Mechanicals and Engineering，23（18）：3093-3097.
	白明洲，王连俊，许兆义，2002.岩爆危险性预测的神经网络模型及应用研究［J］.中国安全科学学报，12（4）：68-72.
	毕傲睿，骆正山，王小完，等，2018.Vague集和熵综合赋权可拓评价模型及其工程应用［J］.系统工程，36（2）：112-120.
	陈建宏，覃曹原，邓东升，2017.基于AHP和物元TOPSIS法的层状岩体巷道冒顶风险评价［J］.黄金科学技术，25（1）：55-60.
	宫凤强，李夕兵，2007.岩爆发生和烈度分级预测的距离判别方法及应用［J］.岩石力学与工程学报，26（5）：1012-1018.
	黄玉仁，毛建喜，林朝阳，等，2014.深埋长大隧道岩爆倾向性多指标评价［J］.铁道工程学报，31（7）：89-94.
	李克钢，李明亮，秦庆词，2020.基于改进综合赋权的岩爆倾向性评价方法研究［J］.岩石力学与工程学报，39（1）：2751-2762.
	李宁，王李管，贾明涛，2017.基于粗糙集理论和支持向量机的岩爆预测［J］.中南大学学报（自然科学版），48（5）：1268-1275.
	李鹏翔，陈炳瑞，周杨一，等，2019.硬岩岩爆预测预警研究进展［J］.煤炭学报，44（增2）：447-465.
	李任豪，顾合龙，李夕兵，等，2020.基于PSO-RBF神经网络模型的岩爆倾向性预测［J］.黄金科学技术，28（1）：134-141.
	李庶林，冯夏庭，王泳嘉，等，2001.深井硬岩岩爆倾向性评价［J］.东北大学学报，22（1）：60-63.
	李彤彤，王玺，刘焕新，等，2020.基于组合赋权的T-FME岩爆倾向性预测模型研究及应用［J］.黄金科学技术，28（4）：565-574.
	孙臣生，2019.基于改进MATLAB-BP神经网络算法的隧道岩爆预测模型［J］.重庆交通大学学报（自然科学版），38（10）：41-49.
	汤志立，徐千军，2020.基于9种机器学习算法的岩爆预测研究［J］.岩石力学与工程学报，39（4）：773-781.
	田睿，孟海东，陈世江，等，2020.基于机器学习的3种岩爆烈度分级预测模型对比研究［J］.黄金科学技术，28（6）：920-927.
	王晋，李夕兵，杨金林，2011.深部硬岩岩爆评判的加权马氏距离判别法［J］.采矿与安全工程学报，28（3）：395-400.
	吴礼斌，李伯年，张孔生，等，2017.MATLAB数据分析方法［M］.北京：机械工业出版社：111-145.
	谢学斌，李德玄，孔令燕，等，2020.基于CRITIC-XGB算法的岩爆倾向等级预测模型［J］.岩石力学与工程学报，39（10）：1975-1982.
	许瑞，侯奎奎，王玺，等，2020.基于核主成分分析与SVM的岩爆烈度组合预测模型［J］.黄金科学技术，28（4）：575-584.
	衣永亮，曹平，蒲成志，2010.金川深部典型岩石岩爆倾向性多因素综合评判［J］.科技导报，28（2）：76-80.
	周科平，古德生，2004.基于GIS的岩爆倾向性模糊自组织神经网络分析模型［J］.岩石力学与工程学报，23（18）：3093-3097.

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岩爆等级	破坏现象	案例个数
无岩爆（N）	无岩爆发生	63
轻微岩爆（L）	岩体或矿体表面的局部破坏和岩块弹出、巷道围岩有局部破坏和少量岩块弹出，有轻微声发射现象，但对支架和设备无严重损害	59
中等岩爆（M）	巷道围岩出现迅速的脆性破坏，并有大量岩石碎块和粉尘抛出，形成气浪冲击，可使数米长的巷道塌落，有较强声发射现象	61
强岩爆（H）	造成长达数十米的地段上支架破坏和巷道塌落，机器及设备受到损坏，有很强的爆裂声	64

样本编号	D/m	UCS/MPa	UTS/MPa	B1	B2	Wet
X1	600	168.07	11.60	14.48	0.87	5.69
X2	705	154.76	5.28	29.31	0.93	3.69
X3	780	139.57	7.25	19.25	0.90	4.23
X4	780	126.19	4.93	25.59	0.92	3.28
X5	870	125.38	6.79	18.46	0.89	3.67
X6	915	154.89	3.05	50.78	0.96	5.72
X7	960	188.35	6.90	27.29	0.92	6.08
X8	626	40.14	7.16	5.60	0.59	2.67
X9	765	165.59	6.79	24.38	0.92	5.90

样本编号	d²（x，H）	d²（x，M）	d²（x，L）	d²（x，N）
X1	1.9969	3.0366	3.2408	10.0211
X2	10.9615	3.8975	1.705	10.2784
X3	2.0452	1.1582	0.6168	6.0869
X4	4.8751	1.2118	0.7458	3.5889
X5	2.1594	1.0515	0.5322	5.6335
X6	80.99	9.0697	12.0749	24.5004
X7	17.2164	10.2383	4.7081	23.3607
X8	31.6168	71.7355	4.6551	8.9458
X9	6.7819	4.2689	2.5627	12.5589

样本编号	岩爆等级	样本编号	岩爆等级
X1	强岩爆（H）	X6	中等岩爆（M）
X2	轻微岩爆（L）	X7	轻微岩爆（L）
X3	轻微岩爆（L）	X8	轻微岩爆（L）
X4	轻微岩爆（L）	X9	轻微岩爆（L）
X5	轻微岩爆（L）

Evaluation of Mine Rockburst Tendency Based on the Distance Discrimination Analysis

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