炸药与岩石智能匹配系统研究
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摘要
炸药与岩石匹配问题是当前土岩爆破领域中的一个重要研究课题。对此,国内外学者进行了长期的探讨和研究,并取得了一定的成就。但是目前仍然存在很多困难,由于岩石本身的复杂性和爆炸过程的瞬时性、复杂性以及其它大量的影响因素,导致岩石破碎过程也十分复杂,而以往的研究工作大多沿用经典理论,集中于表面现象的描述与解释,难以获得完整的、准确的和定量的解答。影响爆破效果的因素主要有岩石爆破特性、炸药爆炸特性以及爆破参数和工艺等,爆破的结果与爆破各影响因素之间的关系是多因素与多目标的复杂对应关系,目前从理论上还很难找到它们之间的确切联系。神经网络系统作为一种非线性映射的建模工具,尤其适合于多因素条件下的推理计算、预测。本文从人工智能的角度出发,采用神经网络和模糊理论,根据不同的岩石匹配不同性能参数的炸药。取得的主要成果如下:
(1)以岩石本身的可钻性为依据,兼顾考虑到其他影响钻机钻孔速度的各种因素,研究了爆破工程施工现场通过钻孔机械实时获取岩石性质,并结合数字照相技术获取地质构造信息。按此信息进行爆破可以避免由于地质勘探钻孔信息的不足而不能比较全面地反应岩石性质而引起的不良的爆破效果乃至爆破事故的发生,同时也可以在未能获得任何地质勘探信息的情况下安全地进行爆破施工。
(2)由于土岩介质和炸药的不确定性,爆炸过程中两者的相互作用就更为不确定,很难从其过程来研究炸药与岩石的匹配,而神经网络方法的特点是只考虑开始和结果,不计较中间过程,鉴于此,本文将神经网络方法应用于炸药与岩石的匹配系统中,兼顾到爆破要求,以阻抗匹配为理论基础,建立了基于神经网络和模糊综合评判的炸药与岩石智能匹配优化系统和基于模糊神经网络的炸药与岩石智能匹配优化系统。通过计算,该系统匹配所得的炸药性能参数与现有的炸药性能参数非常接近,误差均在10%以内,完全能满足实际工程的需要,具有较强的实际应用价值。
(3)在基于MATLAB下的神经网络工具箱平台,分别运用BP神经网络和RBF神经网络来建立台阶爆破参数优化模型,设计结果表明,采用神经网络模型进行优化设计,其误差率都小于10%,提高了设计效率。
(4)采用有限元分析软件ANSYS LS-DYNA模拟了在不同条件下的炸药与岩石匹配。模拟结果表明,当炸药与岩石的波阻抗相近时,岩体中产生的塑性区最大、炮孔壁上产生的峰值压力最大。
(5)分析了爆破与静爆剂联合作用预裂成缝过程,采用边界元方法计算分析了爆破与静爆剂联合作用的成缝机理;
(6)通过室内试验和现场预裂爆破试验,进一步验证了能量匹配理论。
The match of explosive and rock is an important research project in the field of rock and soil blasting currently. Many domestic and foreign scholars have been conducting discussion and study about this problem, and certain achievements are obtained. However, at present, there are still many difficulties, due to the complexity of rock, instantaneity and complexity of the process of blasting, as well as a lot of other influencing factors, the process of rock fragmentation is quite complex, yet previous studies are mostly based on classical theory, focus on the description and explanation of superficies, and can’t obtain complete, accurate and quantitative answers. The factors influencing blasting mainly consist of blasting characteristic of rock and explosive, as well as parameters and techniques of blasting, the connection between the result of blasting and influencing factors is a complex corresponding relation between many factors and many targets, the accurate connection between them is very difficulty to establish currently. As a non-linear mapping-modeling tool, neural network system is very suitable for reasoning and forecasting under the condition of many factors. From the perspective of artificial intelligence, neural network and fuzzy theory are used in this thesis; explosives of different properties are matched to rock of different properties. Main research results are as follows:
(1) Based on the rock drillability, taking the affecting factors of drilling machine into account, obtaining the nature of rock from drilling site and the geological structure by digital image is discussed. It can avoid bad blasting effects and even the blasting accident because of the lack of geological prospecting information which can’t express rock’s character comprehensively, even the blasting engineering can be constructed safely without any geological prospecting information.
(2) Owing to the uncertainty of rock, soil and explosive, the interaction between them will be more uncertain in the process of blasting, so it is very difficult to study the matching of the rock and explosive. However, the neural network method, only paying attention to the beginning and ending and not the process, is adopted to solve the matching of explosive and rock effectively. So, taking the demand of blasting into account, the intelligent matching system of the explosive and rock based on fuzzy integrated judge and neural network and the intelligent matching system of the explosive and rock based on fuzzy neural network are set up in this thesis. From the application of the intelligent matching system, a conclusion can be drawn that the system can meet the needs of practical engineering, and it is worth being adopted. And the theory of energy matching of rock and explosive is proved by experiments of pre-splitting blasting.
(3) A bench blasting optimization model is created by using BP neural network and RBF neural network respectively under the platform of MATLAB-based neural network toolbox, the results show that the error rate is less than 10%, and the efficiency is improved by using neural network model to optimize the design.
(4) The matching of explosive and rock under different conditions is simulated by using analyzing finite element software ANSYS LS-DYNA. The results of the simulation shows that when the wave impedance of the explosive and the rock mass are very close, the plastically deforming area of the rock mass and the surge pressure on the wall of the hole are bigger than other conditions.
(5) The fracture forming in rock mass because of blasting and expansive cracking agent is discussed and the mechanism of fracture forming in rock mass is studied by boundary element.
(6) The theory of energy matching of rock and explosive is proved by experiments in lab and the engineering of pre-splitting blasting.
(1)以岩石本身的可钻性为依据,兼顾考虑到其他影响钻机钻孔速度的各种因素,研究了爆破工程施工现场通过钻孔机械实时获取岩石性质,并结合数字照相技术获取地质构造信息。按此信息进行爆破可以避免由于地质勘探钻孔信息的不足而不能比较全面地反应岩石性质而引起的不良的爆破效果乃至爆破事故的发生,同时也可以在未能获得任何地质勘探信息的情况下安全地进行爆破施工。
(2)由于土岩介质和炸药的不确定性,爆炸过程中两者的相互作用就更为不确定,很难从其过程来研究炸药与岩石的匹配,而神经网络方法的特点是只考虑开始和结果,不计较中间过程,鉴于此,本文将神经网络方法应用于炸药与岩石的匹配系统中,兼顾到爆破要求,以阻抗匹配为理论基础,建立了基于神经网络和模糊综合评判的炸药与岩石智能匹配优化系统和基于模糊神经网络的炸药与岩石智能匹配优化系统。通过计算,该系统匹配所得的炸药性能参数与现有的炸药性能参数非常接近,误差均在10%以内,完全能满足实际工程的需要,具有较强的实际应用价值。
(3)在基于MATLAB下的神经网络工具箱平台,分别运用BP神经网络和RBF神经网络来建立台阶爆破参数优化模型,设计结果表明,采用神经网络模型进行优化设计,其误差率都小于10%,提高了设计效率。
(4)采用有限元分析软件ANSYS LS-DYNA模拟了在不同条件下的炸药与岩石匹配。模拟结果表明,当炸药与岩石的波阻抗相近时,岩体中产生的塑性区最大、炮孔壁上产生的峰值压力最大。
(5)分析了爆破与静爆剂联合作用预裂成缝过程,采用边界元方法计算分析了爆破与静爆剂联合作用的成缝机理;
(6)通过室内试验和现场预裂爆破试验,进一步验证了能量匹配理论。
The match of explosive and rock is an important research project in the field of rock and soil blasting currently. Many domestic and foreign scholars have been conducting discussion and study about this problem, and certain achievements are obtained. However, at present, there are still many difficulties, due to the complexity of rock, instantaneity and complexity of the process of blasting, as well as a lot of other influencing factors, the process of rock fragmentation is quite complex, yet previous studies are mostly based on classical theory, focus on the description and explanation of superficies, and can’t obtain complete, accurate and quantitative answers. The factors influencing blasting mainly consist of blasting characteristic of rock and explosive, as well as parameters and techniques of blasting, the connection between the result of blasting and influencing factors is a complex corresponding relation between many factors and many targets, the accurate connection between them is very difficulty to establish currently. As a non-linear mapping-modeling tool, neural network system is very suitable for reasoning and forecasting under the condition of many factors. From the perspective of artificial intelligence, neural network and fuzzy theory are used in this thesis; explosives of different properties are matched to rock of different properties. Main research results are as follows:
(1) Based on the rock drillability, taking the affecting factors of drilling machine into account, obtaining the nature of rock from drilling site and the geological structure by digital image is discussed. It can avoid bad blasting effects and even the blasting accident because of the lack of geological prospecting information which can’t express rock’s character comprehensively, even the blasting engineering can be constructed safely without any geological prospecting information.
(2) Owing to the uncertainty of rock, soil and explosive, the interaction between them will be more uncertain in the process of blasting, so it is very difficult to study the matching of the rock and explosive. However, the neural network method, only paying attention to the beginning and ending and not the process, is adopted to solve the matching of explosive and rock effectively. So, taking the demand of blasting into account, the intelligent matching system of the explosive and rock based on fuzzy integrated judge and neural network and the intelligent matching system of the explosive and rock based on fuzzy neural network are set up in this thesis. From the application of the intelligent matching system, a conclusion can be drawn that the system can meet the needs of practical engineering, and it is worth being adopted. And the theory of energy matching of rock and explosive is proved by experiments of pre-splitting blasting.
(3) A bench blasting optimization model is created by using BP neural network and RBF neural network respectively under the platform of MATLAB-based neural network toolbox, the results show that the error rate is less than 10%, and the efficiency is improved by using neural network model to optimize the design.
(4) The matching of explosive and rock under different conditions is simulated by using analyzing finite element software ANSYS LS-DYNA. The results of the simulation shows that when the wave impedance of the explosive and the rock mass are very close, the plastically deforming area of the rock mass and the surge pressure on the wall of the hole are bigger than other conditions.
(5) The fracture forming in rock mass because of blasting and expansive cracking agent is discussed and the mechanism of fracture forming in rock mass is studied by boundary element.
(6) The theory of energy matching of rock and explosive is proved by experiments in lab and the engineering of pre-splitting blasting.
引文
[1]于亚伦,工程爆破理论与技术[M],北京:冶金工业出版社,2004
[2]刘殿中,工程爆破手册[M],北京:冶金工业出版社,2003
[3]李秦,国内工程爆破现状及发展前景的探讨[J],工程爆破,1999(3):82-85
[4]宋锦泉,汪旭光,段宝福,中国工程爆破发展现状与展望[J],铜业工程,2002(3):6-9
[5]王永青,汪旭光,乳化炸药能量密度与爆破效果的关系[J],有色金属,2003(3):7-11
[6]李夕兵,古德生,赖海辉,常规炸药与不同岩体匹配的可能途径[J],矿冶工程,1994(3):102-104
[7]宗琦,孟德君,炮孔不同装药结构对爆破能量影响的理论探讨[J],岩石力学与工程学报,2003(4):641-645
[8]杨年华,张志毅,王中黔等,硬岩隧道快速掘进的钻爆技术[J],工程爆破,2003(1):16-21
[9]史晓亮,段隆臣等,一种确定金刚石最优钻进规程的新方法[J],地质装备,2002(3):19-21
[10]周圣元,铜绿山铜铁矿深孔爆破技术[J],矿业研究与开发,1996(2):7-11
[11]杨小林,朱毅,煤矿采掘爆破中的块度问题[J],煤,1996(1):33-36
[12]孙建鼎,关于提高爆炸能量利用率的探讨[J],爆破,1996(2):20-24
[13]韩有林,米欣,重铵油炸药及其在石灰石矿的应用[J],山东冶金,1997(4):61-62
[14]赖应得,论炸药和岩石的能量匹配[J],工程爆破,1995(2):22-26
[15]逄焕东,张金泉,掏槽爆破爆炸能量与损伤岩体破坏的能量匹配[J],爆破,2003(增刊):58-60
[16]徐颖,郝飞,丁亚光,小直径中深孔爆破直眼分段掏槽机理及参数探讨[J],矿山压力与顶板管理,2003(1):99-101
[17]吴立,关于岩体爆破研究的几点思考[J],地质科技情报,1999(增刊):97-99
[18] C. Hendricks,J. Peck, M.J. Scoble, Machine performanence monitoring in surface mines,Mining Engineering, v44, n3, Mar,1992, p243-250
[19]叶海旺,土岩爆破智能化系统研究[D],武汉理工大学博士学位论文,2003
[20] M.J. Scoble , J. Peck, C. Hendricks,Correlation between rotary drill performanence parameters and borehole geophysical logging,Mining Science & Technology, v8, n3, May, 1989, p301-312
[21] Zhu R.G., Wang X.F., Theoretical Analysis and Computation on the Stress Field and Size Distribution of Fragmentation by Rock Blasting, Proc.of the Second World Conf. On Non-Metallic Minerals, Beijing, China, 1998
[22] J. Peck, C. Hendricks, Application of GPS-based navigation systems on mobile equipment in open-pit mines, CIM Bulletin, 1997, Vol 90:114-119
[23]叶海旺.基于模糊神经网络的炸药与岩石匹配优化系统研究[J].爆破器材,2005,34(3):5～7
[24]叶海旺,叶结旺,朱瑞庚.基于模糊推理的炸药与岩石智能匹配系统研究[J].爆破,2003,20(4):5～7
[25]叶海旺.基于改进BP网络和模糊综合评判的炸药与岩石匹配优化[J].有色金属,2004,8(3):94～97
[26]史忠植,高级人工智能[M],北京:科学出版社,1998
[27]李人厚,智能控制理论和方法[M],西安:西安电子科技大学出版社,1999
[28] Nils.J Nilsson, Artificial Intelligence, Beijing: China Machine Press,1999
[29]王玉杰,爆破工程,武汉理工大学出版社,2007
[30]郑孟菊,俞统昌,张银亮,炸药的性能及测试技术[M],北京:兵器工业出版社,1990
[31]欧育湘,炸药学[M],北京:北京理工大学出版社,2006
[32]翁春林,叶加冕,工程爆破,北京:冶金工业出版社,2008
[33]郭学彬,张继春,爆破工程,北京:人民交通出版社,2007
[34]吕春绪,刘祖亮,倪欧琪,工业炸药[M],北京:兵器工业出版社,1994
[35]张守中,爆炸基本原理[M],北京:国防工业出版社,1988
[36]陆明,工业炸药配方设计,北京:国防工业出版社,2004
[37] Kremev L Y, Gelatinized Emulsions XV1. Effect of Neutral Inorganic Salts on Emulsification, Kolloid Z, 1958(20):546
[38] Svatopluk Zeman, Petr Kohlicek, Andrzej Maranda , A study of chemical micromechanism governrning detonation initiation of condensed explosive mixtures by means of differential thermal analysis,Thermochimica Acta, 2003, (398): 185 -194
[39] Turcotte R, Lightfoot P D, Fouchard R, Jones D E C,Themal hazard assessment of AN and AN-based ex-plosives, Journal of Hazardous Materials. 2003, ( A101 ): l -27
[40] Kenji Murata, Katsuhiko Takahashi , Yukio Kato. Precise measurements of underwater explosion phenomena by pressure sensor using fluoropolymer, Journal of Materials Processing Technology.1999,85:39-42
[41] Kato Yukio, Torii Akio, Ishida Toshio, Hattori Katsuhide,Correlation between detonation properties and underwater explosion performances of emulsion explosives , International jahrestagung-Fraunhofer-Institut fuer Treib-und Exp1osivstoffe. Fraunhofer-Inst fuer Treib-und Explosivstoffe,1991, 80: 1- l1
[42] Heathcote N L, Kennedy M, Thompson F. Testing and trials of emu1sion explosives in UK coalmines. Mining Englneer.1990,149 (340): 263-268
[43] Kurokawa Koichi, Hashimoto Kenji, Tabuchi Masaharu,Experimental study on the effect of performances of explosives on rock fracture,Explosion and Explosives ( English translation of Kogyo Kayaku) 1992, 53 (5) :261-267
[44] Rao V Mohan, Ghosh P K, Vijayaraghavan V, Chandrakumar N,Void size measurement in emulsion explosives : a noninvasive approach using NMR imaging,Langmuir,2000,16 (6):2985 -2986
[45]中国力学学会工程爆破专业委员会编,爆破工程[M],北京:冶金工业出版社,1992
[46]陶颂霖,凿岩爆破[M],北京:冶金工业出版社,1986
[47]汪旭光,乳化炸药[M],北京:冶金工业出版社,2008
[48]王瑞龙等,铵油炸药混装车在黑岱沟露天煤矿的应用[J],露天采矿技术,2006(1):38-39
[49]王清华,宋领,乳化炸药混装车水孔爆破的优越性[J],爆破器材,2002,Vol.31,No.5:10-12
[50]熊代余等,系列乳化炸药现场混装车的研制与应用[J],爆破器材,2006(4):12-16
[51]李志江,乳化炸药混装车爆破技术开采面板堆石坝料的研究[D],武汉理工大学硕士学位论文,2003
[52]赵江农,刘明德,BC型铵油炸药混装车及其装药量计量误差[J],矿山机械,2001(2):11-13
[53]杨疾风,BCRH-15型乳化炸药混装车故障浅析[J],海南矿冶,2000,Vol.10,No.2:19-20
[54]苑璞,张来库,乳化炸药混装车在齐大山铁矿的应用[J],中国矿业,2000(49):153-154
[55]万红彬等,运用混装车降低爆破大块率的措施研究[J],工程爆破,2004,Vol.21,No.2:37-38,42
[56]王清华,宋领,乳化炸药混装车在三峡工程爆破中的应用[J],水利水电施工,2002(4):77-79
[57]谢和平,陈忠辉,岩石力学[M],北京:科学出版社,2004
[58]张永兴,岩石力学[M],北京:中国建筑工业出版社,2004
[59]黄醒春,岩石力学[M],北京:高等教育出版社,2005
[60]张志呈,肖正学等,裂隙岩体爆破技术[M],成都:四川科学技术出版社,1999
[61]宁恩渐,采掘机械[M],北京:冶金工业出版社,1980
[62]王荣祥等,矿山工程设备技术[M],北京:冶金工业出版社,2005
[63]阳生权,阳军生,岩体力学[M],北京:机械工业出版社,2008
[64]凌贤长等,岩体力学[M],哈尔滨:哈尔滨工业大学出版社,2002
[65]高金川,岩土工程勘察与评价[M],武汉:中国地质大学出版社,2005
[66]阎世信等,山地地球物理勘探技术[M],北京:石油工业出版社,2000
[67]赵国隆,勘探工程技术[M],上海:上海科学技术出版社,2003
[68]管志宁,地磁场与磁力勘探,北京:地质出版社,2005
[69]郭守忠,水利水电工程勘探与岩土工程施工技术[M],北京:中国水利水电出版社,2002
[70]单娜琳,工程地震勘探[M],北京:冶金工业出版社,2006
[71]徐九华等,地质学[M],北京:冶金工业出版社,2007
[72]采矿设计手册编写委员会,采矿设计手册(矿产地质卷)[M],北京:中国建筑工业出版社,1989
[73]钮强,岩石爆破机理[M],沈阳:东北工学院出版社,1990
[74]刘宗平,冲击凿岩工具及其理论基础[M],北京:地质出版社,1987
[75]全国公路工程地质科技情报网,公路工程地质勘探使用技术[M],北京:人民交通出版社,2004
[76]赖海辉,钻孔工程问答[M],北京:冶金工业出版社,1985
[77]采矿设计手册编写委员会,采矿设计手册(矿产开采卷)[M],北京:中国建筑工业出版社,1987
[78]郭子庭,吴从师,炸药与岩石的全过程匹配[J],矿冶工程,1993,Vol.13,No.3:11-15
[79]朱剑英,智能系统非经典数学方法[M],武汉:华中科技大学出版社,2001
[80]胡守仁,神经网络导论[M],长沙:国防科技大学出版社,1993
[81]赵振宇,徐用懋,模糊理论和神经网络的基础和应用[M],北京:清华大学出版社,1997
[82]伍世虔,徐军编,动态模糊神经网络:设计与应用[M],北京:清华大学出版社,2008
[83]杨纶标,高英仪,模糊数学原理及应用[M],广州:华南理工大学出版社,2001
[84]李柏年,模糊数学及其应用[M],合肥:合肥工业大学出版社,2007
[85]梁保松,曹殿立,模糊数学及其应用[M],北京:科学出版社,2007
[86]宋晓秋,模糊数学原理与方法[M],徐州:中国矿业大学出版社,2004
[87]叶海旺,朱瑞赓,基于模糊综合评判的岩石分级系统研究[J],武汉理工大学学报,2003(05),P49-51
[88]李安贵,模糊数学及其应用,北京:冶金工业出版社,2005
[89]李鸿吉,模糊数学基础及实用算法,北京:科学出版社,2005
[90]谭文,王耀南,混沌系统的模糊神经网络控制理论与方法[M],北京:科学出版社,2008
[91]孙海蓉,模糊神经网络及其应用[D],华北电力大学博士学位论文,2006
[92]张元标,王文娟,模糊神经网络在可转换债券价格预测中的应用[J],西南民族大学学报,2004,Vol.30,No.3:372-376
[93]樊春玲等,卫星姿态测量系统中模糊神经网络的应用[J],中国惯性技术学报,2002,Vol.10,No.3:21-25
[94]宋晓睿等,基于模糊神经网络的火灾探测系统研究[J],工矿自动化,2008,No.1:15-17
[95]曾珞亚,模糊神经网络的应用与研究[D],广西师范大学硕士学位论文,2000
[96]王科俊等,基于混沌BP算法的T-S型模糊神经网络的应用研究第25届控制会议论文,2005:1172-1176
[97] Kim B,Lee HJ,Kim D. Neural Network Model of Plasma Charging Damage on MOSFET Device. Materials and Manufacturing Processes,2009,24(6):615-618.
[98] Ma, QL; Zheng, QL; Peng, H, et al. Chaotic time series prediction based on fuzzy boundary modular neural networks. ACTA PHYSICA SINICA,2009.58(3): 1410-1419.
[99] Shi L, Wang LJ, Wang ZZ.The Modeling and the Sensor Fault Diagnosis of a Continuous Stirred Tank Reactor with a Takagi-Sugeno Recurrent Fuzzy Neural Network. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2009,5(1): 37
[100] Kim JT, Choi W, Oh SK, et al. Partial Discharge Pattern Recognition Using Fuzzy-Neural Networks (FNNs) Algorithm. PROCEEDINGS OF THE 2008 IEEEINTERNATIONAL POWER MODULATORS AND HIGH VOLTAGE CONFERENCE,2008: 272-275
[101] Stepnicka M, Polakovic O. A Neural Network Approach to the Fuzzy Transform. FUZZY SETS AND SYSTEMS,2009,160(8): 1037-1047
[102] [6] Kim JT, Choi W, Oh SK, et al. Fuzzy-Neural Networks (FNNs) Algorithm for Partial Discharge Pattern Recognition. 2008 INTERNATIONAL CONFERENCE ON HIGH VOLTAGE ENGINEERING AND APPLICATION,2008: 621-625
[103] Chen CS, Chen HH. Robust adaptive neural-fuzzy-network control for the synchronization of uncertain chaotic systems. NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS,2009,10(3): 1466-1479
[104] Lin CJ, Lee CY. FPGA Implementation of a Recurrent Neural Fuzzy Network with On-Chip Learning for Prediction and Identification Applications. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,2009,25(2): 575-589
[105] Cobaner M, Unal B, Kisi O. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. JOURNAL OF HYDROLOGY,2009,367(1-2): 52-61
[106] Li JZ, Liu CL, Zuo ZP. Research on Application of Fuzzy Neural Networks for Logistics Forecasting. ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 7, PROCEEDINGS,2008: 255-259
[107] Zhu QD, Zhang Y, Zhang JQ. Design of Fuzzy Neural Network Controller for Marine Steam Turbine System. ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 4, PROCEEDINGS,2008: 353-356
[108]许宁,基于模糊神经网络的建筑工程造价估计方法[J],江汉石油学院学报,2000,Vol.22,No.1:87-88
[109]李学桥,神经网络工程应用[M],重庆:重庆大学出版社,1995:47~52
[110]高宁,基于BP神经网络的农作物虫情预测及其MATLAB实现[D],安徽农业大学,3003,5: 32~38
[111]崔玉理,基于神经网络的污水处理过程建模及仿真研究[D],山东:山东科技大学,2006,12: 23~29
[112]杨军,熊代余,岩石爆破机理[M],北京:冶金工业出版社,2004.8(1)
[113]段保福,费鸿禄,神经网络模型在台阶爆破块度预测中的应用[J],工程爆破,1999,12:7~32
[114]王祥厚,李程远,用神经网络建立台阶爆破块度的预测[J],贵州工业大学学报,2001,30(5): 8~12
[115]郭明,爆破块度影响因素的灰色关联分析[J] ,矿业研究与开发, 2000,20(6):41~43
[116]谢贤平,谢源,分形理论与岩石爆破块度的预报研究[J],工程爆破,1995,8(1):55~58
[117]张正宇,现代水利水电工程爆破[M],北京:中国水利水电出版社,2003:37~41
[118]姜绍飞,人工神经网络用于建筑工程领域的数据处理方法[J],哈尔滨建筑大学学报,1999, 10(5):62~66
[119]汪旭光,中国典型爆破工程与技术[M] ,北京:冶金工业出版社,2006,9:35~39
[120]段向军,基于神经网络的预测控制方法研究[D] ,大庆石油学院,2005,12:31~37
[121]钮强,熊代余,探索炸药岩石匹配的试验研究[J],爆破,1987,4:1-6
[122]李裕春,时党勇,赵远,ANSYS 10.0/LS-DYNA基础理论与工程实践[M],中国水利水电出版社,2006
[123]夏祥,石永强,李海波.岩体单孔及群孔齐发爆破爆炸荷载数值分析[J],岩石力学与工程学报,2007,26(s1):3390-3396
[124]顾宏伟,赵燕明,金文,柱状药包岩石中爆炸的数值模拟[J],工程爆破,2007,13(1):24-27
[125] [英] C、A布莱比亚若,工程师的边界元方法[M],北京:科学出版社,1986
[126]冯无桢,连续介质力原导论[M],北京:科学出版社,1986
[127] [美] A、C艾龙根等著,弹性动力学(第二卷)[M],北京:石油工业出版社,1975
[128]兰格弗斯,现代岩石爆破技术[M],北京:冶金工业出版社,1983
[2]刘殿中,工程爆破手册[M],北京:冶金工业出版社,2003
[3]李秦,国内工程爆破现状及发展前景的探讨[J],工程爆破,1999(3):82-85
[4]宋锦泉,汪旭光,段宝福,中国工程爆破发展现状与展望[J],铜业工程,2002(3):6-9
[5]王永青,汪旭光,乳化炸药能量密度与爆破效果的关系[J],有色金属,2003(3):7-11
[6]李夕兵,古德生,赖海辉,常规炸药与不同岩体匹配的可能途径[J],矿冶工程,1994(3):102-104
[7]宗琦,孟德君,炮孔不同装药结构对爆破能量影响的理论探讨[J],岩石力学与工程学报,2003(4):641-645
[8]杨年华,张志毅,王中黔等,硬岩隧道快速掘进的钻爆技术[J],工程爆破,2003(1):16-21
[9]史晓亮,段隆臣等,一种确定金刚石最优钻进规程的新方法[J],地质装备,2002(3):19-21
[10]周圣元,铜绿山铜铁矿深孔爆破技术[J],矿业研究与开发,1996(2):7-11
[11]杨小林,朱毅,煤矿采掘爆破中的块度问题[J],煤,1996(1):33-36
[12]孙建鼎,关于提高爆炸能量利用率的探讨[J],爆破,1996(2):20-24
[13]韩有林,米欣,重铵油炸药及其在石灰石矿的应用[J],山东冶金,1997(4):61-62
[14]赖应得,论炸药和岩石的能量匹配[J],工程爆破,1995(2):22-26
[15]逄焕东,张金泉,掏槽爆破爆炸能量与损伤岩体破坏的能量匹配[J],爆破,2003(增刊):58-60
[16]徐颖,郝飞,丁亚光,小直径中深孔爆破直眼分段掏槽机理及参数探讨[J],矿山压力与顶板管理,2003(1):99-101
[17]吴立,关于岩体爆破研究的几点思考[J],地质科技情报,1999(增刊):97-99
[18] C. Hendricks,J. Peck, M.J. Scoble, Machine performanence monitoring in surface mines,Mining Engineering, v44, n3, Mar,1992, p243-250
[19]叶海旺,土岩爆破智能化系统研究[D],武汉理工大学博士学位论文,2003
[20] M.J. Scoble , J. Peck, C. Hendricks,Correlation between rotary drill performanence parameters and borehole geophysical logging,Mining Science & Technology, v8, n3, May, 1989, p301-312
[21] Zhu R.G., Wang X.F., Theoretical Analysis and Computation on the Stress Field and Size Distribution of Fragmentation by Rock Blasting, Proc.of the Second World Conf. On Non-Metallic Minerals, Beijing, China, 1998
[22] J. Peck, C. Hendricks, Application of GPS-based navigation systems on mobile equipment in open-pit mines, CIM Bulletin, 1997, Vol 90:114-119
[23]叶海旺.基于模糊神经网络的炸药与岩石匹配优化系统研究[J].爆破器材,2005,34(3):5～7
[24]叶海旺,叶结旺,朱瑞庚.基于模糊推理的炸药与岩石智能匹配系统研究[J].爆破,2003,20(4):5～7
[25]叶海旺.基于改进BP网络和模糊综合评判的炸药与岩石匹配优化[J].有色金属,2004,8(3):94～97
[26]史忠植,高级人工智能[M],北京:科学出版社,1998
[27]李人厚,智能控制理论和方法[M],西安:西安电子科技大学出版社,1999
[28] Nils.J Nilsson, Artificial Intelligence, Beijing: China Machine Press,1999
[29]王玉杰,爆破工程,武汉理工大学出版社,2007
[30]郑孟菊,俞统昌,张银亮,炸药的性能及测试技术[M],北京:兵器工业出版社,1990
[31]欧育湘,炸药学[M],北京:北京理工大学出版社,2006
[32]翁春林,叶加冕,工程爆破,北京:冶金工业出版社,2008
[33]郭学彬,张继春,爆破工程,北京:人民交通出版社,2007
[34]吕春绪,刘祖亮,倪欧琪,工业炸药[M],北京:兵器工业出版社,1994
[35]张守中,爆炸基本原理[M],北京:国防工业出版社,1988
[36]陆明,工业炸药配方设计,北京:国防工业出版社,2004
[37] Kremev L Y, Gelatinized Emulsions XV1. Effect of Neutral Inorganic Salts on Emulsification, Kolloid Z, 1958(20):546
[38] Svatopluk Zeman, Petr Kohlicek, Andrzej Maranda , A study of chemical micromechanism governrning detonation initiation of condensed explosive mixtures by means of differential thermal analysis,Thermochimica Acta, 2003, (398): 185 -194
[39] Turcotte R, Lightfoot P D, Fouchard R, Jones D E C,Themal hazard assessment of AN and AN-based ex-plosives, Journal of Hazardous Materials. 2003, ( A101 ): l -27
[40] Kenji Murata, Katsuhiko Takahashi , Yukio Kato. Precise measurements of underwater explosion phenomena by pressure sensor using fluoropolymer, Journal of Materials Processing Technology.1999,85:39-42
[41] Kato Yukio, Torii Akio, Ishida Toshio, Hattori Katsuhide,Correlation between detonation properties and underwater explosion performances of emulsion explosives , International jahrestagung-Fraunhofer-Institut fuer Treib-und Exp1osivstoffe. Fraunhofer-Inst fuer Treib-und Explosivstoffe,1991, 80: 1- l1
[42] Heathcote N L, Kennedy M, Thompson F. Testing and trials of emu1sion explosives in UK coalmines. Mining Englneer.1990,149 (340): 263-268
[43] Kurokawa Koichi, Hashimoto Kenji, Tabuchi Masaharu,Experimental study on the effect of performances of explosives on rock fracture,Explosion and Explosives ( English translation of Kogyo Kayaku) 1992, 53 (5) :261-267
[44] Rao V Mohan, Ghosh P K, Vijayaraghavan V, Chandrakumar N,Void size measurement in emulsion explosives : a noninvasive approach using NMR imaging,Langmuir,2000,16 (6):2985 -2986
[45]中国力学学会工程爆破专业委员会编,爆破工程[M],北京:冶金工业出版社,1992
[46]陶颂霖,凿岩爆破[M],北京:冶金工业出版社,1986
[47]汪旭光,乳化炸药[M],北京:冶金工业出版社,2008
[48]王瑞龙等,铵油炸药混装车在黑岱沟露天煤矿的应用[J],露天采矿技术,2006(1):38-39
[49]王清华,宋领,乳化炸药混装车水孔爆破的优越性[J],爆破器材,2002,Vol.31,No.5:10-12
[50]熊代余等,系列乳化炸药现场混装车的研制与应用[J],爆破器材,2006(4):12-16
[51]李志江,乳化炸药混装车爆破技术开采面板堆石坝料的研究[D],武汉理工大学硕士学位论文,2003
[52]赵江农,刘明德,BC型铵油炸药混装车及其装药量计量误差[J],矿山机械,2001(2):11-13
[53]杨疾风,BCRH-15型乳化炸药混装车故障浅析[J],海南矿冶,2000,Vol.10,No.2:19-20
[54]苑璞,张来库,乳化炸药混装车在齐大山铁矿的应用[J],中国矿业,2000(49):153-154
[55]万红彬等,运用混装车降低爆破大块率的措施研究[J],工程爆破,2004,Vol.21,No.2:37-38,42
[56]王清华,宋领,乳化炸药混装车在三峡工程爆破中的应用[J],水利水电施工,2002(4):77-79
[57]谢和平,陈忠辉,岩石力学[M],北京:科学出版社,2004
[58]张永兴,岩石力学[M],北京:中国建筑工业出版社,2004
[59]黄醒春,岩石力学[M],北京:高等教育出版社,2005
[60]张志呈,肖正学等,裂隙岩体爆破技术[M],成都:四川科学技术出版社,1999
[61]宁恩渐,采掘机械[M],北京:冶金工业出版社,1980
[62]王荣祥等,矿山工程设备技术[M],北京:冶金工业出版社,2005
[63]阳生权,阳军生,岩体力学[M],北京:机械工业出版社,2008
[64]凌贤长等,岩体力学[M],哈尔滨:哈尔滨工业大学出版社,2002
[65]高金川,岩土工程勘察与评价[M],武汉:中国地质大学出版社,2005
[66]阎世信等,山地地球物理勘探技术[M],北京:石油工业出版社,2000
[67]赵国隆,勘探工程技术[M],上海:上海科学技术出版社,2003
[68]管志宁,地磁场与磁力勘探,北京:地质出版社,2005
[69]郭守忠,水利水电工程勘探与岩土工程施工技术[M],北京:中国水利水电出版社,2002
[70]单娜琳,工程地震勘探[M],北京:冶金工业出版社,2006
[71]徐九华等,地质学[M],北京:冶金工业出版社,2007
[72]采矿设计手册编写委员会,采矿设计手册(矿产地质卷)[M],北京:中国建筑工业出版社,1989
[73]钮强,岩石爆破机理[M],沈阳:东北工学院出版社,1990
[74]刘宗平,冲击凿岩工具及其理论基础[M],北京:地质出版社,1987
[75]全国公路工程地质科技情报网,公路工程地质勘探使用技术[M],北京:人民交通出版社,2004
[76]赖海辉,钻孔工程问答[M],北京:冶金工业出版社,1985
[77]采矿设计手册编写委员会,采矿设计手册(矿产开采卷)[M],北京:中国建筑工业出版社,1987
[78]郭子庭,吴从师,炸药与岩石的全过程匹配[J],矿冶工程,1993,Vol.13,No.3:11-15
[79]朱剑英,智能系统非经典数学方法[M],武汉:华中科技大学出版社,2001
[80]胡守仁,神经网络导论[M],长沙:国防科技大学出版社,1993
[81]赵振宇,徐用懋,模糊理论和神经网络的基础和应用[M],北京:清华大学出版社,1997
[82]伍世虔,徐军编,动态模糊神经网络:设计与应用[M],北京:清华大学出版社,2008
[83]杨纶标,高英仪,模糊数学原理及应用[M],广州:华南理工大学出版社,2001
[84]李柏年,模糊数学及其应用[M],合肥:合肥工业大学出版社,2007
[85]梁保松,曹殿立,模糊数学及其应用[M],北京:科学出版社,2007
[86]宋晓秋,模糊数学原理与方法[M],徐州:中国矿业大学出版社,2004
[87]叶海旺,朱瑞赓,基于模糊综合评判的岩石分级系统研究[J],武汉理工大学学报,2003(05),P49-51
[88]李安贵,模糊数学及其应用,北京:冶金工业出版社,2005
[89]李鸿吉,模糊数学基础及实用算法,北京:科学出版社,2005
[90]谭文,王耀南,混沌系统的模糊神经网络控制理论与方法[M],北京:科学出版社,2008
[91]孙海蓉,模糊神经网络及其应用[D],华北电力大学博士学位论文,2006
[92]张元标,王文娟,模糊神经网络在可转换债券价格预测中的应用[J],西南民族大学学报,2004,Vol.30,No.3:372-376
[93]樊春玲等,卫星姿态测量系统中模糊神经网络的应用[J],中国惯性技术学报,2002,Vol.10,No.3:21-25
[94]宋晓睿等,基于模糊神经网络的火灾探测系统研究[J],工矿自动化,2008,No.1:15-17
[95]曾珞亚,模糊神经网络的应用与研究[D],广西师范大学硕士学位论文,2000
[96]王科俊等,基于混沌BP算法的T-S型模糊神经网络的应用研究第25届控制会议论文,2005:1172-1176
[97] Kim B,Lee HJ,Kim D. Neural Network Model of Plasma Charging Damage on MOSFET Device. Materials and Manufacturing Processes,2009,24(6):615-618.
[98] Ma, QL; Zheng, QL; Peng, H, et al. Chaotic time series prediction based on fuzzy boundary modular neural networks. ACTA PHYSICA SINICA,2009.58(3): 1410-1419.
[99] Shi L, Wang LJ, Wang ZZ.The Modeling and the Sensor Fault Diagnosis of a Continuous Stirred Tank Reactor with a Takagi-Sugeno Recurrent Fuzzy Neural Network. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2009,5(1): 37
[100] Kim JT, Choi W, Oh SK, et al. Partial Discharge Pattern Recognition Using Fuzzy-Neural Networks (FNNs) Algorithm. PROCEEDINGS OF THE 2008 IEEEINTERNATIONAL POWER MODULATORS AND HIGH VOLTAGE CONFERENCE,2008: 272-275
[101] Stepnicka M, Polakovic O. A Neural Network Approach to the Fuzzy Transform. FUZZY SETS AND SYSTEMS,2009,160(8): 1037-1047
[102] [6] Kim JT, Choi W, Oh SK, et al. Fuzzy-Neural Networks (FNNs) Algorithm for Partial Discharge Pattern Recognition. 2008 INTERNATIONAL CONFERENCE ON HIGH VOLTAGE ENGINEERING AND APPLICATION,2008: 621-625
[103] Chen CS, Chen HH. Robust adaptive neural-fuzzy-network control for the synchronization of uncertain chaotic systems. NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS,2009,10(3): 1466-1479
[104] Lin CJ, Lee CY. FPGA Implementation of a Recurrent Neural Fuzzy Network with On-Chip Learning for Prediction and Identification Applications. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,2009,25(2): 575-589
[105] Cobaner M, Unal B, Kisi O. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. JOURNAL OF HYDROLOGY,2009,367(1-2): 52-61
[106] Li JZ, Liu CL, Zuo ZP. Research on Application of Fuzzy Neural Networks for Logistics Forecasting. ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 7, PROCEEDINGS,2008: 255-259
[107] Zhu QD, Zhang Y, Zhang JQ. Design of Fuzzy Neural Network Controller for Marine Steam Turbine System. ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 4, PROCEEDINGS,2008: 353-356
[108]许宁,基于模糊神经网络的建筑工程造价估计方法[J],江汉石油学院学报,2000,Vol.22,No.1:87-88
[109]李学桥,神经网络工程应用[M],重庆:重庆大学出版社,1995:47~52
[110]高宁,基于BP神经网络的农作物虫情预测及其MATLAB实现[D],安徽农业大学,3003,5: 32~38
[111]崔玉理,基于神经网络的污水处理过程建模及仿真研究[D],山东:山东科技大学,2006,12: 23~29
[112]杨军,熊代余,岩石爆破机理[M],北京:冶金工业出版社,2004.8(1)
[113]段保福,费鸿禄,神经网络模型在台阶爆破块度预测中的应用[J],工程爆破,1999,12:7~32
[114]王祥厚,李程远,用神经网络建立台阶爆破块度的预测[J],贵州工业大学学报,2001,30(5): 8~12
[115]郭明,爆破块度影响因素的灰色关联分析[J] ,矿业研究与开发, 2000,20(6):41~43
[116]谢贤平,谢源,分形理论与岩石爆破块度的预报研究[J],工程爆破,1995,8(1):55~58
[117]张正宇,现代水利水电工程爆破[M],北京:中国水利水电出版社,2003:37~41
[118]姜绍飞,人工神经网络用于建筑工程领域的数据处理方法[J],哈尔滨建筑大学学报,1999, 10(5):62~66
[119]汪旭光,中国典型爆破工程与技术[M] ,北京:冶金工业出版社,2006,9:35~39
[120]段向军,基于神经网络的预测控制方法研究[D] ,大庆石油学院,2005,12:31~37
[121]钮强,熊代余,探索炸药岩石匹配的试验研究[J],爆破,1987,4:1-6
[122]李裕春,时党勇,赵远,ANSYS 10.0/LS-DYNA基础理论与工程实践[M],中国水利水电出版社,2006
[123]夏祥,石永强,李海波.岩体单孔及群孔齐发爆破爆炸荷载数值分析[J],岩石力学与工程学报,2007,26(s1):3390-3396
[124]顾宏伟,赵燕明,金文,柱状药包岩石中爆炸的数值模拟[J],工程爆破,2007,13(1):24-27
[125] [英] C、A布莱比亚若,工程师的边界元方法[M],北京:科学出版社,1986
[126]冯无桢,连续介质力原导论[M],北京:科学出版社,1986
[127] [美] A、C艾龙根等著,弹性动力学(第二卷)[M],北京:石油工业出版社,1975
[128]兰格弗斯,现代岩石爆破技术[M],北京:冶金工业出版社,1983