基于神经网络与灰色理论的工程岩体分级
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摘要
建立岩体分级结果与岩石强度、岩体完整度、地下水分布等影响因素间的非线性映射关系,对大型水利水电工程的岩体质量分级工作具有重要意义。以前坪水库坝址区工程岩体为例,采用灰色理论对影响因素及对应结果进行聚类划分,构建灰色理论岩体质量分级体系;以类似工程岩体数据作为输入样本对BP神经网络进行训练,拟合各影响因素与分级结果之间的函数关系,并构造特定网络,最后将研究区岩体数据作为检验样本进行分级。与比传统工程岩体质量分级方法比较表明:新的模型能最大限度利用勘察数据库,且分级结果与传统方法基本一致,少数岩组偏向于经济性。
Establishing the relationshipamong the results and factors such as rock strength,rock mass integrity and groundwater distribution is of great significance to rock mass classification in large-scale Hydropower projects. The engineering rock mass of Qianping Reservoir was taken as an example,the grey models was used to cluster the influence factors and the corresponding results and the rock mass quality classification system was established. The BP neural network was trained by using similar engineering rock mass data as input samples,fitting the functional relationship between the influence factors and the results and constructing a specific network. Finally,the rock mass data in the study area were taken as test samples for classification. Compared with the traditional engineering rock mass classification method,the results show that the new model can make the maximum use of survey database and the results of classification are basically the same as those of traditional methods. A few groups are biased towards the more economical. It can be a reference for quality classification of engineering rock mass.
Establishing the relationshipamong the results and factors such as rock strength,rock mass integrity and groundwater distribution is of great significance to rock mass classification in large-scale Hydropower projects. The engineering rock mass of Qianping Reservoir was taken as an example,the grey models was used to cluster the influence factors and the corresponding results and the rock mass quality classification system was established. The BP neural network was trained by using similar engineering rock mass data as input samples,fitting the functional relationship between the influence factors and the results and constructing a specific network. Finally,the rock mass data in the study area were taken as test samples for classification. Compared with the traditional engineering rock mass classification method,the results show that the new model can make the maximum use of survey database and the results of classification are basically the same as those of traditional methods. A few groups are biased towards the more economical. It can be a reference for quality classification of engineering rock mass.
引文
[1]陈昌彦,王贵荣.各类岩体质量评价方法的相关性探讨[J].岩石力学与工程学报,2002,21(12):1894-1900.
[2]尹红梅,张宜虎,周火明,等.工程岩体分级研究综述[J].长江科学院院报,2011,28(8):59-65.
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[4] DENG J L.Control Problems of Grey Systems[J].Systems&Control Letters,1982,1(5):288-294.
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[6]刘远征,刘欣.“三峡YZP法”在某水库坝区工程岩体质量分级中的应用[J].煤炭技术,2008,27(9):110-112.
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[9]童新安.基于灰色系统与神经网络的组合预测方法及研究应用[D].西安:西安电子科技大学,2012:21-26.
[10]马壮壮,束龙仓,季叶飞,等.基于遗传算法的BP神经网络计算岩溶水安全开采量[J].水文地质工程地质,2016,43(1):22-27.
[11]金长宇,马震岳,张运良,等.神经网络在岩体力学参数和地应力场反演中的应用[J].岩土力学,2006,27(8):1263-1271.
[12]郭彬,薛希龙,徐敏.改进层次聚类法在矿山岩体分级中的应用[J].金属矿山,2011(11):13-19.
[13]刘玉成,刘延保.灰色关联理论在矿山岩体质量评价中的应用[J].矿业工程,2006,4(6):16-18.
[14]杨永斌.基于BP神经网络的边坡岩体变形模量反分析[D].长沙:中南大学,2011:51-59.
[2]尹红梅,张宜虎,周火明,等.工程岩体分级研究综述[J].长江科学院院报,2011,28(8):59-65.
[3] BIENIAWSKI ZT.Geomechanics Classification of Rock Masses and Its Application in Tunneling[C]//Procee-dings of the Third International Congress on Rock Mechanics.Denver:ISRM,1974:27-32.
[4] DENG J L.Control Problems of Grey Systems[J].Systems&Control Letters,1982,1(5):288-294.
[5]张毅,杨建国.基于灰色理论预处理的神经网络机床热误差建模[J].机械工程学报,2011,47(7):134-139.
[6]刘远征,刘欣.“三峡YZP法”在某水库坝区工程岩体质量分级中的应用[J].煤炭技术,2008,27(9):110-112.
[7]张伟,游艇,李双艳,等.主成分-BP组合模型在坝顶位移监控中的应用[J].人民黄河,2012,34(2):115-117.
[8]韩凤山.节理化岩体强度与力学参数估计的地质强度指标GSI法[J].大连大学学报,2007(6):46-51.
[9]童新安.基于灰色系统与神经网络的组合预测方法及研究应用[D].西安:西安电子科技大学,2012:21-26.
[10]马壮壮,束龙仓,季叶飞,等.基于遗传算法的BP神经网络计算岩溶水安全开采量[J].水文地质工程地质,2016,43(1):22-27.
[11]金长宇,马震岳,张运良,等.神经网络在岩体力学参数和地应力场反演中的应用[J].岩土力学,2006,27(8):1263-1271.
[12]郭彬,薛希龙,徐敏.改进层次聚类法在矿山岩体分级中的应用[J].金属矿山,2011(11):13-19.
[13]刘玉成,刘延保.灰色关联理论在矿山岩体质量评价中的应用[J].矿业工程,2006,4(6):16-18.
[14]杨永斌.基于BP神经网络的边坡岩体变形模量反分析[D].长沙:中南大学,2011:51-59.