岩爆分级预测的粗糙集-多维正态云模型
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摘要
准确有效的岩爆分级预测对避免或减少岩爆灾害具有重要意义。选取围岩最大切向应力σθ、岩石单轴抗压强度σc、岩石单轴抗拉强度σt和岩石弹性变形能指数Wet作为岩爆评价指标,将多维正态云模型作为岩爆等级的预测方法,利用粗糙集确定评价指标权重,以国内外15组岩爆实例作为模型构建样本,建立了粗糙集-多维正态云岩爆分级预测模型。首先采用模糊集聚类后再通过粗糙集逐级筛选评价指标的方法确定评价指标权重,根据多维正态正向云发生器计算得到评价指标的确定度;然后利用评价指标权重和评价指标确定度计算评价样本的综合确定度,根据最大隶属度原则,判定评价样本的岩爆等级。采用鑫华矿业矿岩样本对粗糙集-多维正态云岩爆分级预测模型进行了有效性验证,计算结果与工程实际相吻合,表明该模型具有较好的实用性。
Accurate and effective grading prediction of rockburst is of great significance to reduce rockburst disasters.Maximum tangential stress of surrounding rock σθ,uniaxial compressive strength of rock σc,uniaxial tensile strength of rock σt and elastic deformation energy index of rock Wetare selected as the evaluation indexes of rockburst.Multidimensional normal cloud model is used as the prediction method of rockburst grade.Rough set is used to determine the weight of evaluation indexes.15 sets of rockburst examples at home and abroad are used as model construction samples,and the grading prediction model of rockburst based on rough set-multidimensional normal cloud is established.Firstly,fuzzy clustering analysis is carried on the evaluation samples,the evaluation indexes are filtered through the rough set,and the weight of evaluation indexes is determined.The determination degree of evaluation indexes is calculated according to the multidimensional normal forward cloud generator.Then,the comprehensive determination degree of evaluation samples is calculated by using the weight of evaluation indexes and the determination degree of evaluation indexes.According to the principle of maximum membership degree,the rockburst grade of evaluation samples is determined.The rockburst samples of Xinhua Mining Company are used to validate the grading prediction model of rockburst based on rough set-multidimensional normal cloud.The calculated results are consistent with the actual situation,which further indicates that the grading prediction model of rockburst based on rough set-multidimensional normal cloud has great practicality.
Accurate and effective grading prediction of rockburst is of great significance to reduce rockburst disasters.Maximum tangential stress of surrounding rock σθ,uniaxial compressive strength of rock σc,uniaxial tensile strength of rock σt and elastic deformation energy index of rock Wetare selected as the evaluation indexes of rockburst.Multidimensional normal cloud model is used as the prediction method of rockburst grade.Rough set is used to determine the weight of evaluation indexes.15 sets of rockburst examples at home and abroad are used as model construction samples,and the grading prediction model of rockburst based on rough set-multidimensional normal cloud is established.Firstly,fuzzy clustering analysis is carried on the evaluation samples,the evaluation indexes are filtered through the rough set,and the weight of evaluation indexes is determined.The determination degree of evaluation indexes is calculated according to the multidimensional normal forward cloud generator.Then,the comprehensive determination degree of evaluation samples is calculated by using the weight of evaluation indexes and the determination degree of evaluation indexes.According to the principle of maximum membership degree,the rockburst grade of evaluation samples is determined.The rockburst samples of Xinhua Mining Company are used to validate the grading prediction model of rockburst based on rough set-multidimensional normal cloud.The calculated results are consistent with the actual situation,which further indicates that the grading prediction model of rockburst based on rough set-multidimensional normal cloud has great practicality.
引文
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[4]付士根,张兴凯,李红辉.超深竖井掘进岩爆特征及防治措施[J].中国安全生产科学技术,2016,12(12):48-52.Fu Shigen,Zhang Xingkai,Li Honghui.Characteristics and prevention control measures of rock burst in excavation of ultra-deep shaft[J].Journal of Safety Science and Technology,2016,12(12):48-52.
[5]李夕兵.岩石动力学基础与应用[M].北京:科学出版社,2014.Li Xibing.Rock Dynamics Fundamentals and Application[M].Beijing:Science Press,2014.
[6]郭生茂,刘涛,赵丽军,等.熵权物元可拓模型在岩爆危险性等级评价中的应用[J].有色金属:矿山部分,2015,67(4):89-93.Guo Shengmao,Liu Tao,Zhao Lijun,et al.Application of entropyweight and matter-element extension model in evaluation of rock burst risk leve[lJ].Nonferrous Metals:Mining Section,2015,67(4):89-93.
[7]王迎超,靖洪文,吉咸伟,等.深埋地下工程岩爆烈度分级预测的RS-功效系数模型[J].中南大学学报:自然科学版,2014,45(6):1992-1997.Wang Yingchao,Jing Hongwen,Ji Xianwei,et al.Model for classification and prediction of rock burst intensity in a deep underground engineering with rough set and efficacy coefficient method[J].Journal of Central South University:Science and Technology,2014,45(6):1992-1997.
[8]胡炜,杨兴国,周宏伟,等.基于灰色关联分析的岩爆预测方法研究[J].人民长江,2011,42(9):38-42.Hu Wei,Yang Xingguo,Zhou Hongwei,et al.Study on rock burst prediction method based on gray relational analysis theory[J].Yangtze River,2011,42(9):38-42.
[9]张光存,高谦,杜聚强,等.基于人工神经网络及非线性回归的岩爆判据[J].中南大学学报:自然科学版,2013,44(7):2977-2981.Zhang Guangcun,Gao Qian,Du Juqiang,et al.Rockburst criterionbased on artificial neural networks and nonlinear regression[J].Journal of Central South University:Natural Science Edition,2013,44(7):2977-2981.
[10]陈祖云,张桂珍,邬长福,等.支持向量机在岩爆预测中的应用[J].金属矿山,2009(11):100-104.Chen Zuyun,Zhang Guizhen,Wu Changfu,et al.Application of the support vector machine in prediction of rock burst[J].Metal Mine,2009(11):100-104.
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[13]贾义鹏,吕庆,尚岳全,等.基于证据理论的岩爆预测[J].岩土工程学报,2014,36(6):1079-1086.Jia Yipeng,Lyu Qing,Shang Yuequan,et al.Rockburst prediction based on evidence theory[J].Chinese Journal of Geotechnical Engineering,2014,36(6):1079-1086.
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[15]杨国勇,陈超,高树林,等.基于层次分析-模糊聚类分析法的导水裂隙带发育高度研究[J].采矿与安全工程学报,2015,32(2):206-212.Yang Guoyong,Chen Chao,Gao Shulin,et al.Study on the height of water flowing fractured zone based on analytic hierarchy process and fuzzy clustering analysis method[J].Journal of Mining&Safety Engineering,2015,32(2):206-212.
[16]赵小柠,马昌喜.基于范例推理的灾害性地震应急物资需求预测研究[J].中国安全科学学报,2012,22(8):3-9.Zhao Xiaoning,Ma Changxi.Research on predicting emergency material demand after disastrous earthquake based on case-based reasoning[J].China Safety Science Journal,2012,22(8):3-9.
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[18]谢建中,杨育,陈倩,等.基于改进BASS模型的短生命周期产品需求预测模型[J].计算机集成制造系统,2015,21(1):48-56.Xie Jianzhong,Yang Yu,Chen Qian,et al.Demand forecasting model for short life cycle products based on improved BASS model[J].Computer Integrated Manufacturing Systems,2015,21(1):48-56.
[19]吴静,吴晓燕,高忠长.基于模糊聚类和粗糙集的仿真可信性模糊综合评估[J].系统工程与电子技术,2010,32(4):770-773.Wu Jing,Wu Xiaoyan,Gao Zhongchang.Fuzzy comprehensive evaluation of simulation credibility based on fuzzy clustering analysis and rough sets theory[J].Systems Engineering and Electronics,2010,32(4):770-773.
[20]过江,张为星,赵岩.岩爆预测的多维云模型综合评判方法[J].岩石力学与工程学报,2018,37(5):1199-1206.Guo Jiang,Zhang Weixing,Zhao Yan.A multidimensional cloud model for rockburst prediction[J].Chinese Journal of Rock Mechanics and Engineering,2018,37(5):1199-1206.
[21]王昭洋.城市湖库水体富营养化评价与水华治理决策方法研究[D].北京:北京工商大学,2016.Wang Zhaoyang.The Research on Eutrophication Assessment and Governance Decision for Water Bloom in Lakes and Reservoirs[D].Beijing:Beijing Technology and Business University,2016.
[22]李陶,李付伟,李向新,等.地震灾害风险的正态云模型综合评价--以毕节市为例[J].中国安全科学学报,2015,25(10):166-171.Li Tao,Li Fuwei,Li Xiangxin,et al.Comprehensive assessment of earthquake hazard risk based on normal cloud model:a case study of Bijie City[J].China Safety Science Journal,2015,25(10):166-171.
[23]陈轶俊,刘敦文,朱鼎耀,等.基于云模型的舰艇弹药库房综合安全评价[J].中国安全生产科学技术,2015,11(2):138-144.Chen Yijun,Liu Dunwen,Zhu Dingyao,et al.Comprehensive safety evaluation of ship ammunition depot based on cloud mode[lJ].Journal of Safety Science and Technology,2015,11(2):138-144.
[24]王迎超,靖洪文,张强,等.基于正态云模型的深埋地下工程岩爆烈度分级预测研究[J].岩土力学,2015,36(4):1190-1193.Wang Yingchao,Jing Hongwen,Zhang Qiang,et al.A normal cloud model-based study of grading prediction of rockburst intensity in deep underground engineering[J].Rock and Soil Mechanics,2015,36(4):1190-1193.
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