基于差异进化算法的围岩参数识别
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摘要
引入了一种新型智能优化算法差异进化优化算法,建立采矿巷道围岩参数的识别方法.基于正交设计和均匀设计方案进行巷道开挖的数值试验,建立围岩力学参数与监测位移的映射关系回归模型.以计算位移和监测位移的误差为适应值,采用差异进化优化算法进行参数识别.在探讨差异进化算法原理和参数识别步骤基础上,将该方法应用于金山店铁矿进路巷道分析.分析结果表明该算法具有优良的全局快速收敛性,是具有较强适应性的新型巷道参数识别方法,具有良好应用前景.
As a new intelligent optimizing algorithm,the difference evolution algorithm was introduced into the recognition of mine workings' wall rock parameters. Based on the orthogonal design and homogeneous design,a numerical test of drift driving was carried out to develop a regression model to reflect the relation between mechanical parameters and monitored tiny displacement of wall rock,with the difference between computed and monitored displacements as fit value,thus introducing the difference evolution algorithm to recognize relevant parameters. Discussing the principle of difference evolution algorithm and parameter recognizing steps,the algorithm was used to analyze the entry roadway of Jinshandian Iron Mine and the results indicated that the algorithm has rapidly global convergence which provides a new way to recognize the parameters of mine workings with high adaptability and good prospect in applications.
As a new intelligent optimizing algorithm,the difference evolution algorithm was introduced into the recognition of mine workings' wall rock parameters. Based on the orthogonal design and homogeneous design,a numerical test of drift driving was carried out to develop a regression model to reflect the relation between mechanical parameters and monitored tiny displacement of wall rock,with the difference between computed and monitored displacements as fit value,thus introducing the difference evolution algorithm to recognize relevant parameters. Discussing the principle of difference evolution algorithm and parameter recognizing steps,the algorithm was used to analyze the entry roadway of Jinshandian Iron Mine and the results indicated that the algorithm has rapidly global convergence which provides a new way to recognize the parameters of mine workings with high adaptability and good prospect in applications.
引文
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[5]Tezuka M,Seoka T.Latest technology of under ground rock cavern excavation in Japan[J].Tunnelling and Underground Space Technology,2003,18(2/3):127-144.
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[7]Yang C X,ThamL G,Feng X T.Two-stepped evolutionary algorithmandits applicationto stability analysis of slopes[J].Journal of Computingin Civil Engineering,2004,18(2):145-153.
[8]杨成祥,冯夏庭,刘红亮.非线性位移时间序列分析模型的进化识别[J].东北大学学报:自然科学版,2004,25(5):494-497.(Yang Cheng-xiang,Feng Xia-ting,Liu Hong-liang.The evolutionary identifying of non-linear displacement ti me series analysis model[J].Journal of Northeastern University:Natural Science,2004,25(5):494-497.)
[9]Dervis K,Selcuk O.A si mple and global opti mization algorithmfor engineering problems[J].Turk J Elec Engin,2004,12(1):53-60.
[10]Ilonen J,Kamarainen J,Lampinen J.Differential evolution training algorithm for feed-forward neural networks[J].Neural Processing Letters,2003(1):93-105.
[2]Wittke W,Pierau B.Foundations for the design and construction of tunnel in swelling rock[C]∥Proceedings ofthe4th International Congress on Rock Mechanics.Montreux:AI ME,1979:216-219.
[3]Feng X T,Hudson J A.Integrated analysis approaches to rock mechanics problems[J].Chinese Journal of Rock Mechanics and Engineering,2002,21(11):1702-1707.
[4]Oreste P.Back analysis techniques for the i mprovement of the understanding of rock in underground constructions[J].Tunneling and Underground Space Technology,2005,20(1):7-21.
[5]Tezuka M,Seoka T.Latest technology of under ground rock cavern excavation in Japan[J].Tunnelling and Underground Space Technology,2003,18(2/3):127-144.
[6]杨志法,王思敬,冯紫良.岩土工程反分析原理及应用[M].北京:地震出版社,2002.(Yang Zhi-fa,Wang Si-jing,Feng Zi-liang.The back analysis principle and application of rock and soil engineering[M].Beijing:Seismic Press,2002.)
[7]Yang C X,ThamL G,Feng X T.Two-stepped evolutionary algorithmandits applicationto stability analysis of slopes[J].Journal of Computingin Civil Engineering,2004,18(2):145-153.
[8]杨成祥,冯夏庭,刘红亮.非线性位移时间序列分析模型的进化识别[J].东北大学学报:自然科学版,2004,25(5):494-497.(Yang Cheng-xiang,Feng Xia-ting,Liu Hong-liang.The evolutionary identifying of non-linear displacement ti me series analysis model[J].Journal of Northeastern University:Natural Science,2004,25(5):494-497.)
[9]Dervis K,Selcuk O.A si mple and global opti mization algorithmfor engineering problems[J].Turk J Elec Engin,2004,12(1):53-60.
[10]Ilonen J,Kamarainen J,Lampinen J.Differential evolution training algorithm for feed-forward neural networks[J].Neural Processing Letters,2003(1):93-105.
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