基于改进的RS-TOPSIS模型的岩爆倾向性预测
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摘要
针对目前岩爆倾向性中预测模型权重确定存在不足导致模型精度不高的现状,为更准确地预测岩爆倾向性,提出综合运用粗糙集理论中的代数观和信息观,确定属性最优权重,并修正岩爆倾向性与评价指标之间的关系,建立岩爆等级理想点矩阵。根据岩爆发生条件,选取岩石脆性指数、切应力指标和弹性应变能指数3项指标作为岩爆判别指标,以国内外20组典型岩爆数据为样本,建立改进的粗糙集—理想点法(RS-TOPSIS)岩爆倾向性预测模型,并应用该模型对玲珑金矿等工程实际进行了岩爆倾向性预测。结果表明:改进后样本预测精度相比于改进前有了显著提高,所建立的模型对实际工程的岩爆倾向性预测效果良好,预测结果更准确。
In view of the present situation that the weight determination of the prediction model in rock-burst proneness is insufficient,which leads to the low accuracy of the model,it is not accurate to use the single rough set algebraic view or information view to determine the weight.In order to predict the rock-burst proneness more accurately,the optimal weight was determined according to the algebraic view and information view in the rough set theory. Because the ideal point range of no rock-burst and violent rock-burst is too large,the relationship between rock-burst proneness and evaluation index was corrected and the ideal point matrix of rock-burst grade was established. Half of the interval length of light rock-burst and medium rock-burst was taken as the corresponding interval length of no rock-burst and violent rock-burst,the upper limit and the lower limit were set respectively for the ideal point matrix. The "approximate ideal point" and "interval" indexes were used to optimize the ideal point.Brittleness coefficient,stress strength coefficient and elastic energy index were selected to construct attribute set according to the occurrence conditions of rock-burst.Taking 20 rock-burst cases as the training and testing samples,using the improved rough set method and the modified rock-burst intensity ideal point method to establish the improved RS-TOPSIS model of rock-burst proneness prediction. The model was applied to predict the rock-burst proneness of the Linglong gold mine et al.The results show that the prediction accuracy of the improved sample reaches 95%,which is a certain improvement from the accuracy of 80%.The model can easily and quickly predict the rock-burst proneness,and the prediction accuracy is more accurate.The model has a good effect on predicting the rock-burst proneness of the actual project,provides a more accurate method for predicting the rock-burst proneness of the underground engineering,and the predicted results tallies with the actual situation. The prediction model of rock burst proneness has certain practical value and good application prospects.
In view of the present situation that the weight determination of the prediction model in rock-burst proneness is insufficient,which leads to the low accuracy of the model,it is not accurate to use the single rough set algebraic view or information view to determine the weight.In order to predict the rock-burst proneness more accurately,the optimal weight was determined according to the algebraic view and information view in the rough set theory. Because the ideal point range of no rock-burst and violent rock-burst is too large,the relationship between rock-burst proneness and evaluation index was corrected and the ideal point matrix of rock-burst grade was established. Half of the interval length of light rock-burst and medium rock-burst was taken as the corresponding interval length of no rock-burst and violent rock-burst,the upper limit and the lower limit were set respectively for the ideal point matrix. The "approximate ideal point" and "interval" indexes were used to optimize the ideal point.Brittleness coefficient,stress strength coefficient and elastic energy index were selected to construct attribute set according to the occurrence conditions of rock-burst.Taking 20 rock-burst cases as the training and testing samples,using the improved rough set method and the modified rock-burst intensity ideal point method to establish the improved RS-TOPSIS model of rock-burst proneness prediction. The model was applied to predict the rock-burst proneness of the Linglong gold mine et al.The results show that the prediction accuracy of the improved sample reaches 95%,which is a certain improvement from the accuracy of 80%.The model can easily and quickly predict the rock-burst proneness,and the prediction accuracy is more accurate.The model has a good effect on predicting the rock-burst proneness of the actual project,provides a more accurate method for predicting the rock-burst proneness of the underground engineering,and the predicted results tallies with the actual situation. The prediction model of rock burst proneness has certain practical value and good application prospects.
引文
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[9]杨涛,李国维.基于先验知识的岩爆预测研究[J].岩石力学与工程学报,2000,19(4):429-431.Yang Tao,Li Guowei.Study on rockburst prediction method based on the prior knowledge[J].Chinese Journal of Rock Mechanics and Engineering,2000,19(4):429-431.
[10]宫凤强,李夕兵.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,2007,26(5):1012-1018.Gong Fengqiang,Li Xibing.Adistance discriminant analysis method for prediction of possibility and classification of rockburst and its application[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(5):1012-1018.
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[12]史秀志,周健,董蕾,等.未确知测度模型在岩爆烈度分级预测中的应用[J].岩石力学与工程学报,2010,29(增1):2720-2726.Shi Xiuzhi,Zhou Jian,Dong Lei,et al.Application of unascertained measurement model to prediction of classification of rock-burst intensity[J].Chinese Journal of Rock Mechanics and Engineering,2010,29(Supp.1):2720-2726.
[13]Pawlak Z,Grzmala-Busse J,Slowinski R,et al.Rough sets[J].Communicationsof theACM,1995,38(11):89-95.
[14]石峰,娄臻亮,张永清,等.一种改进的粗糙集属性约简启发式算法[J].上海交通大学学报,2002,36(4):478-481.Shi Feng,Lou Zhenliang,Zhang Yongqing,et al.A modified heuristic algorithm of attribute reduction in rough set[J].Journal of Shanghai Jiaotong University,2002,36(4):478-481.
[15]索中英,程嗣怡,袁修久,等.优势决策信息系统规则获取方法及应用[J].兵工学报,2015,36(3):539-545.Suo Zhongying,Cheng Siyi,Yuan Xiujiu,et al.Rule acquisition method and application of dominance decision-making information system[J].Acta Armamentarii,2015,36(3):539-545.
[16]徐玖平,吴巍.多属性决策的理论与方法[M].北京:清华大学出版社,2006:66-73.Xu Jiuping,Wu Wei.Multiple Attribute Decision Making Theory and Methods[M].Beijing:Tsinghua University Press,2006:66-73.
[17]王新民,樊彪,张德明,等.基于AHP和TOPSIS的充填方案综合评判优选[J].黄金科学技术,2016,24(5):1-6.Wang Xinmin,Fan Biao,Zhang Deming,et al.Syntheic judgement for filling scheme optimization based on AHPand TOPSIS methods[J].Gold Science and Technology,2016,24(5):1-6.
[18]谢本贤,陈沅江,史秀志.深部岩体工程围岩质量评价的IRMR法研究[J].中南大学学报(自然科学版),2007,38(5):987-992.Xie Benxian,Chen Yuanjiang,Shi Xiuzhi.IRMR method for evaluation of surrounding rock quality in deep rock mass engineering[J].Journal of Central South University(Science and Technology),2007,38(5):987-992.
[19]王新民,荣帅,赵茂阳,等.基于变权重理论和TOPSIS的尾砂浓密装置优选[J].黄金科学技术,2017,25(3):77-83.Wang Xinmin,Rong Shuai,Zhao Maoyang,et al.Concentration equipment optimization based on variable weight theory and TOPSIS[J].Gold Science and Technology,2017,25(3):77-83.
[20]周科平,雷涛,胡建华.深部金属矿山RS-TOPSIS岩爆预测模型及其应用[J].岩石力学与工程学报,2013,32(增2):3705-3711.Zhou Keping,Lei Tao,Hu Jianhua.RS-TOPSIS model of rock-burst prediction in deep metal mines and its application[J].Chinese Journal of Rock Mechanics and Engineering,2013,32(Supp.2):3705-3711.
[2]李夕兵.岩石动力学基础与应用[M].北京:科学出版社,2014:495-501.Li Xibing.Rock Dynamics Fundamentals and Application[M].Beijing:Science Press,2014:495-501.
[3]蔡嗣经,张禄华,周文略.深井硬岩矿山岩爆预测研究[J].中国安全生产科学技术,2005,1(5):17-20.Cai Sijing,Zhang Luhua,Zhou Wenlue.Research on prediction of rockburst in deep hard rock mines[J].Journal of Safety Science and Technology,2005,1(5):17-20.
[4]王元汉,李卧东,李启光,等.岩爆预测的模糊数学综合评判方法[J].岩石力学与工程学报,1998,17(5):493-501.Wang Yuanhan,Li Wodong,Li Qiguang,et al.Method of fuzzy comprehensive evaluations for rockburst prediction[J].Chinese Journal of Rock Mechanics and Engineering,1998,17(5):493-501.
[5]Park K H,Lee J G,Owatsiriwong A.Seepage force in a drained circular tunnel:An analytical approach[J].Canadian Geotechnical Journal,2008,45(3):432-436.
[6]龚剑,胡乃联,崔翔,等.基于AHP-TOPSIS评判模型的岩爆倾向性预测[J].岩石力学与工程学报,2014,33(7):1442-1448.Gong Jian,Hu Nailian,Cui Xiang,et al.Rockburst tendency prediction based on AHP-TOPSIS evaluation model[J].Chinese Journal of Rock Mechanics and Engineering,2014,33(7):1442-1448.
[7]陈建宏,覃曹原,邓东升.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,2017,25(1):55-60.Chen Jianhong,Qin Caoyuan,Deng Dongsheng.Risk assessment of bedded rock roadway roof fall based on AHPand Matter-Element TOPSIS method[J].Gold Science and Technology,2017,25(1):55-60.
[8]Chen H J,Li N H,Ni D X,et al.Prediction of rockburst by artificial neural network[J].Chinese Journal of Rock Mechanics and Engineering,2003,22(5):762-768.
[9]杨涛,李国维.基于先验知识的岩爆预测研究[J].岩石力学与工程学报,2000,19(4):429-431.Yang Tao,Li Guowei.Study on rockburst prediction method based on the prior knowledge[J].Chinese Journal of Rock Mechanics and Engineering,2000,19(4):429-431.
[10]宫凤强,李夕兵.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,2007,26(5):1012-1018.Gong Fengqiang,Li Xibing.Adistance discriminant analysis method for prediction of possibility and classification of rockburst and its application[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(5):1012-1018.
[11]王吉亮,陈剑平,杨静,等.岩爆等级判定的距离判别分析方法及应用[J].岩土力学,2009,30(7):2203-2208.Wang Jiliang,Chen Jianping,Yang Jing,et al.Method of distance discriminant analysis for determination of classification of rockburst[J].Rock and Soil Mechanics,2009,30(7):2203-2208.
[12]史秀志,周健,董蕾,等.未确知测度模型在岩爆烈度分级预测中的应用[J].岩石力学与工程学报,2010,29(增1):2720-2726.Shi Xiuzhi,Zhou Jian,Dong Lei,et al.Application of unascertained measurement model to prediction of classification of rock-burst intensity[J].Chinese Journal of Rock Mechanics and Engineering,2010,29(Supp.1):2720-2726.
[13]Pawlak Z,Grzmala-Busse J,Slowinski R,et al.Rough sets[J].Communicationsof theACM,1995,38(11):89-95.
[14]石峰,娄臻亮,张永清,等.一种改进的粗糙集属性约简启发式算法[J].上海交通大学学报,2002,36(4):478-481.Shi Feng,Lou Zhenliang,Zhang Yongqing,et al.A modified heuristic algorithm of attribute reduction in rough set[J].Journal of Shanghai Jiaotong University,2002,36(4):478-481.
[15]索中英,程嗣怡,袁修久,等.优势决策信息系统规则获取方法及应用[J].兵工学报,2015,36(3):539-545.Suo Zhongying,Cheng Siyi,Yuan Xiujiu,et al.Rule acquisition method and application of dominance decision-making information system[J].Acta Armamentarii,2015,36(3):539-545.
[16]徐玖平,吴巍.多属性决策的理论与方法[M].北京:清华大学出版社,2006:66-73.Xu Jiuping,Wu Wei.Multiple Attribute Decision Making Theory and Methods[M].Beijing:Tsinghua University Press,2006:66-73.
[17]王新民,樊彪,张德明,等.基于AHP和TOPSIS的充填方案综合评判优选[J].黄金科学技术,2016,24(5):1-6.Wang Xinmin,Fan Biao,Zhang Deming,et al.Syntheic judgement for filling scheme optimization based on AHPand TOPSIS methods[J].Gold Science and Technology,2016,24(5):1-6.
[18]谢本贤,陈沅江,史秀志.深部岩体工程围岩质量评价的IRMR法研究[J].中南大学学报(自然科学版),2007,38(5):987-992.Xie Benxian,Chen Yuanjiang,Shi Xiuzhi.IRMR method for evaluation of surrounding rock quality in deep rock mass engineering[J].Journal of Central South University(Science and Technology),2007,38(5):987-992.
[19]王新民,荣帅,赵茂阳,等.基于变权重理论和TOPSIS的尾砂浓密装置优选[J].黄金科学技术,2017,25(3):77-83.Wang Xinmin,Rong Shuai,Zhao Maoyang,et al.Concentration equipment optimization based on variable weight theory and TOPSIS[J].Gold Science and Technology,2017,25(3):77-83.
[20]周科平,雷涛,胡建华.深部金属矿山RS-TOPSIS岩爆预测模型及其应用[J].岩石力学与工程学报,2013,32(增2):3705-3711.Zhou Keping,Lei Tao,Hu Jianhua.RS-TOPSIS model of rock-burst prediction in deep metal mines and its application[J].Chinese Journal of Rock Mechanics and Engineering,2013,32(Supp.2):3705-3711.
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