基于灰色关联分析的遗传神经网络在水下爆破中质点峰值振动速度预测研究
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摘要
水下爆破是一个复杂的、非线性的动态能量释放过程,其涉及到的影响因素众多。为了充分利用少量的实测数据,较准确地预测水下爆破质点峰值振动速度,引入灰色关联分析理论,并结合遗传神经网络较强的非线性映射优势和全局化的搜索能力,建立基于灰色关联分析的遗传神经网络模型(GRA-GA-BP)。该模型利用灰色关联分析理论,充分挖掘小样本潜在信息特征,较合理地确定了影响爆破振动速度的主要因素,解决了神经网络在多变量复杂系统中输入变量无法自动寻优的难题,从而增强了神经网络的适应能力和稳定性。采用该模型对广东台山核电站1期工程大襟岛水下爆破开挖质点峰值振动速度进行预测,并与传统的遗传神经网络及萨道夫斯基公式预测结果进行对比,发现GRA-GA-BP模型的预测值与实测值吻合更好,预测误差更稳定。研究方法可为小样本、多因素影响下类似工程质点峰值振动速度预测提供借鉴。
Underwater blasting is a complicated,nonlinear,and dynamic process of energy release.It is affected by many factors,and its process has not been fully investigated at present.In order to accurately predict the peak particle velocity induced by underwater blasting based on a small amount of field measurements,the GRA-GA-BP model is established based on the grey relational analysis theory combining with the genetic neural network which has the nonlinear mapping and global searching capabilities.In the model,the potential information of the small sample is fully discovered,and the main factors affecting the vibration velocity are reasonably determined based on the grey relational analysis theory.Moreover,the problems of the neural network unable to automatically select and optimize input variables in complicated and multivariate systems are solved,which enhances the adaptability and stability of the genetic neural network.Finally,the GRA-GA-BP model is adopted to predict the peak particle velocity induced by underwater blasting at Dajin Island in the first phase of Taishan nuclear power station.Compared with the results obtained by traditional genetic neural network and the Sadaovsk formula,the prediction error of the GRA-GA-BP model is smaller and more stable.Therefore,the proposed procedure provides an appropriate way to predict the peak particle velocity induced by underwater blasting.
Underwater blasting is a complicated,nonlinear,and dynamic process of energy release.It is affected by many factors,and its process has not been fully investigated at present.In order to accurately predict the peak particle velocity induced by underwater blasting based on a small amount of field measurements,the GRA-GA-BP model is established based on the grey relational analysis theory combining with the genetic neural network which has the nonlinear mapping and global searching capabilities.In the model,the potential information of the small sample is fully discovered,and the main factors affecting the vibration velocity are reasonably determined based on the grey relational analysis theory.Moreover,the problems of the neural network unable to automatically select and optimize input variables in complicated and multivariate systems are solved,which enhances the adaptability and stability of the genetic neural network.Finally,the GRA-GA-BP model is adopted to predict the peak particle velocity induced by underwater blasting at Dajin Island in the first phase of Taishan nuclear power station.Compared with the results obtained by traditional genetic neural network and the Sadaovsk formula,the prediction error of the GRA-GA-BP model is smaller and more stable.Therefore,the proposed procedure provides an appropriate way to predict the peak particle velocity induced by underwater blasting.
引文
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[2]佟锦嶽,石教往,熊长汉,等.水下工程爆破对环境影响规律研究(上)[J].爆破,2000,17(3):6-12.TONG Jin-yue,SHI Jiao-wang,XIONG Chang-han,et al.Study on the law of influence of underwater engineering blasting on environment[J].Blasting,2000,17(3):6-12.
[3]RAJENDRAN R.Linear elastic shock response of plane plates subjected to underwater explosion[J].International Journal of Impact Engineering,2001,25(5):493-506.
[4]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.
[5]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 Meng-hui,DONG Xiang-shan,ZHANG Li-min,etal.Application research of neural network model for forecasting the intension of blasting vibration[J].Non-ferrous Metal,2004,56(3):25-27.
[6]赵华兵,龙源,宋克健,等.爆破振动速度预测方法及其影响因素[J].工程爆破,2012,18(1):24-27.ZHAO Hua-bing,LONG Yuan,SONG Ke-jian,et al.Predictive methods and influence factors of blasting vibration velocity[J].Engineering Blasting,2012,18(1):24―27.
[7]林从谋,逄焕东,王其升,等.隧道掘进爆破地震峰值神经网络预报研究[J].岩土力学,2004,25(增刊):125―126.LIN Cong-mou,PANG Huan-dong,WANG Qi-sheng,et al.Study on neural network prediction of peak amplitude of blasting ground vibration for tunneling[J].Rock and Soil Mechanics,2004,25(Supp.):125―126.
[8]苏国韶,宋咏春,燕柳斌.岩体爆破效应预测的一种新方法[J].岩石力学与工程学报,2007,26(增刊1):3509―3514.SU Guo-shao,SONG Yong-chun,YAN Liu-bin.A new method for forecasting of blasting effect in rock mass[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(Supp.1):3509―3514.
[9]史秀志,董凯程,邱贤阳,等.基于支持向量机回归爆破振动速度预测分析[J].工程爆破,2009,15(3):28―30.SHI Xiu-zhi,DONG Kai-cheng,QIU Xian-yang,et al.Analysis of the PPV prediction of blasting vibration based on support vector machine regression[J].Engineering Blasting,2009,15(3):28―30.
[10]范孝锋,周传波,陈国平.爆破震动影响因素的灰关联分析[J].爆破,2005,22(2):100―102FAN Xiao-feng,ZHOU Chuan-bo,CHEN Guo-ping.The influential factors of blasting vibration by grey correlation analysis[J].Blasting,2005,22(2):100―102.
[11]刘红岩,刘国振,杨军,等.基于有限元数值计算的爆破震动强度分析[J].岩土力学,2006,27(6):977―980.LIU Hong-yan,LIU Guo-zhen,YANG Jun.Analysis of blasting vibration intensity based on finite element numerical calculation[J].Rock and Soil Mechanics,2006,27(6):977―980.
[12]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.
[13]郝彬彬,李冲,王春红.灰色关联度在矿井突水水源判别中的应用[J].中国煤炭,2010,36(6):20―22.HAO Bin-bin,LI Chong,WANG Chun-hong.Application of grey correlation degree in the identification of sources of mine water bursting[J].China Coal,2010,36(6):20―22.
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