基于神经网络的爆破振动速度峰值预报
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摘要
以广东岭澳核电站二期工程20 m平台和核岛爆破开挖为实例,运用人工神经网络原理,以孔径、孔深、孔距、排距、最小抵抗线、最大单孔药量、最大段药量、堵塞长度、总药量、高程差和爆源距作为影响爆破振动速度的主要因素,建立BP神经网络模型,对质点爆破振动速度峰值进行预测。分析结果表明,运用提出的神经网络预测模型精确度明显高于传统的萨道夫斯基公式。
Because of the influence of charge parameters and rock properties,it is difficult to accurately predicate the vibration characteristics in the engineering blasting.Based on the 20 meters platform and nuclear island blasting excavation monitoring in the second phase of Linga′o nuclear power station,Guangdong Province,the artificial neural network is adopted to predict the peak velocity of blasting vibration.In the analysis,the charge hole diameter,distance,and depth,column distance between charge holes,line of least resistance,maximum charge of single hole,maximum charge weight per delay interval,clogging depth of hole,total charge,magnitude of relative altitude and explosive distance are considered to establish the back-propagation neural network model.The prediction results through artificial neural network are more accurate than those of Sadaovsk formula.
Because of the influence of charge parameters and rock properties,it is difficult to accurately predicate the vibration characteristics in the engineering blasting.Based on the 20 meters platform and nuclear island blasting excavation monitoring in the second phase of Linga′o nuclear power station,Guangdong Province,the artificial neural network is adopted to predict the peak velocity of blasting vibration.In the analysis,the charge hole diameter,distance,and depth,column distance between charge holes,line of least resistance,maximum charge of single hole,maximum charge weight per delay interval,clogging depth of hole,total charge,magnitude of relative altitude and explosive distance are considered to establish the back-propagation neural network model.The prediction results through artificial neural network are more accurate than those of Sadaovsk formula.
引文
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[18]王道平,张义忠.故障智能诊断系统的理论与方法[M].北京:冶金工业出版社,2001.(WANG Daoping,ZHANG Yizhong.Method and theory of failure intelligence system[M].Beijing:Metallurgical Industry Press,2001.(in Chinese))
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[20]中国科学院武汉岩土力学研究所.岭澳核电站二期工程基础负开挖爆破振动监测总结报告[R].武汉:中国科学院武汉岩土力学研究所,2005.(Institute of Rock and Soil Mechanics,Chinese Academy of Science.Report on monitoring blasting vibration in the second phrase for Lingao nuclear power station[R].Wuhan:Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,2005.(in Chinese))
[2]张继春,彭琼芳.岩体爆破地震波衰减规律的现场试验与分析[J].辽宁工程技术大学学报(自然科学版),2001,20(4):399–401.(ZHANG Jichun,PENG Qiongfang.Field experiment and its analyses of attenuation law for seismic waves resulting from rock blasting[J].Journal of Liaoning Technical University(Natural Science),2001,20(4):399–401.(in Chinese))
[3]姚尧.爆震的放大效应与二元回归分析[J].爆破,1992,9(4):5–8.(YAO Yao.Enlarged effect of blasting vibration and duality regression analyses[J].Blasting,1992,9(4):5–8.(in Chinese))
[4]卢文波,HUSTRULID W.质点峰值振动速度衰减公式的改进[J].工程爆破,2002,8(3):1–4.(LU Wenbo,HUSTRULID W.An improvement to the equation for the attenuation of the peak particle velocity[J].Engineering Blasting,2002,8(3):1–4.(in Chinese))
[5]冯夏庭.岩石可爆性神经网络研究[J].爆炸与冲击,1994,14(4):298–306.(FENG Xiating.A study on neural network on rock blast ability[J].Explosion and Shock Waves,1994,14(4):298–306.(in Chinese))
[6]冯夏庭,王泳嘉,卢世宗.边坡稳定性的神经网络估计[J].工程地质学报,1995,3(4):54–61.(FENG Xiating,WANG Yongjia,LU Shizong.Neural network estimation of slope stability[J].Journal of Engineering Geology,1995,3(4):54–61.(in Chinese))
[7]林鲁生,冯夏庭,白世伟,等.人工神经网络在边坡滑移预测中的应用[J].岩土力学,2002,23(4):508–510.(LIN Lusheng,FENG Xiating,BAI Shiwei,et al.Application of artificial neural network to prediction of sliding slope[J].Rock and Soil Mechanics,2002,23(4):508–510.(in Chinese))
[8]冯夏庭,林韵梅.基于神经网络的巷道支护决策[J].煤炭学报,1995,20(1):34–38.(FENG Xiating,LIN Yunmei.Selection of support systems for underground opening in coal mines based on neural network[J].Journal of China Coal Society,1995,20(1):34–38.(in Chinese))
[9]冯夏庭,王泳嘉,姚建国.煤矿顶板矿压显现实时预报的自适应神经网络方法[J].煤炭学报,1995,20(5):455–460.(FENG Xiating,WANG Yongjia,YAO Jianguo.An adaptive neural network for real-time prediction of rock behavior in coal mines[J].Journal of China Coal Society,1995,20(5):455–460.(in Chinese))
[10]蔡德所,胡铁松,张继春.基于前馈网络的岩体爆破效应预测研究[J].岩土工程学报,1997,19(1):43–49.(CAI Desuo,HU Tiesong,ZHANG Jichun.Forecast research on effect of vibration and damage in rock mass blasting[J].Chinese Journal of Geotechnical Engineering,1997,19(1):43–49.(in Chinese))
[11]叶洲元,马建军,蔡路军,等.利用振动监测数据优化预测爆破质点振动速度[J].矿业研究与开发,2003,23(4):48–51.(YE Zhouyuan,MA Jianjun,CAI Lujun,et al.An optimized calculation of particle vibration velocity by means of the vibration data form blasting monitoring[J].Mining Research and Development,2003,23(4):48–51.(in Chinese))
[12]周志华,张金山,刘勤.基于人工神经网络的爆破振动速度峰值的预报[J].包头钢铁学院学报,2003,22(1):8–10.(ZHOU Zhihua,ZHANG Jinshan,LIU Qin.The prediction of blasting vibration velocity by neural network[J].Journal of Baotou University of Iron and Steel Technology,2003,22(1):8–10.(in Chinese))
[13]夏梦会,董香山,张力民,等.神经网络模型在爆破振动强度预测中的应用研究[J].有色金属(矿山部分),2004,56(3):25–27.(XIA Menghui,DONG Xiangshan,ZHANG Limin,et al.Application research of neural network model for forecasting the intension of blasting vibration[J].Non-ferrous Metal(Mining),2004,56(3):25–27.(in Chinese))
[14]王进学,郭勇,曲文峰.神经网络法预测爆破震动速度初探[J].有色矿冶,2003,19(1):13–16.(WANG Jinxue,GUO Yong,QU Wenfeng.Preliminary analysis of predicting the velocity of the blasting vibration by neural network[J].Non-ferrous Mining and Metallurgy,2003,19(1):13–16.(in Chinese))
[15]徐全军,张庆明,恽寿榕.爆破地震峰值的神经网络预报模型[J].北京理工大学学报,1998,18(4):472–475.(XU Quanjun,ZHANG Qingming,YUN Shourong.Neural network model for forecasting peak acceleration of blasting vibration[J].Journal of Beijing Institute of Technology,1998,18(4):472–475.(in Chinese))
[16]SINGH T N,SINGH V.An intelligent approach to prediction and control ground vibration in mines[J].Geotechnical and Geological Engineering,2005,23(3):249–262.
[17]从爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科技大学出版社,1998.(CONG Shuang.Application of neural network theory based on MATLAB toolbox[M].Hefei:University of Science and Technology of China Press,1998.(in Chinese))
[18]王道平,张义忠.故障智能诊断系统的理论与方法[M].北京:冶金工业出版社,2001.(WANG Daoping,ZHANG Yizhong.Method and theory of failure intelligence system[M].Beijing:Metallurgical Industry Press,2001.(in Chinese))
[19]陆伟民.人工智能技术及应用[M].上海:同济大学出版社,1998.(LU Weimin.Application of artificial intelligence technology[M].Shanghai:Tongji University Press,1998.(in Chinese))
[20]中国科学院武汉岩土力学研究所.岭澳核电站二期工程基础负开挖爆破振动监测总结报告[R].武汉:中国科学院武汉岩土力学研究所,2005.(Institute of Rock and Soil Mechanics,Chinese Academy of Science.Report on monitoring blasting vibration in the second phrase for Lingao nuclear power station[R].Wuhan:Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,2005.(in Chinese))
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