基于T-S模型的模糊神经网络在地下硐室超挖预测中的应用
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摘要
以山东某隧道为例,运用基于T-S模型的模糊神经网络,结合相关影响因素对地下硐室超挖进行了预测。预测模型根据工程实际情况选用了199组数据,其中179组数据作为训练样本训练网络,20组数据作为测试样本验证模型的预测结果。通过计算,基于T-S模型模糊神经网络超挖预测的相关系数为0.962 8,均方差为0.449,平均相对误差为6.33%。与BP神经网络和回归模型的预测结果进行了比较分析,结果表明基于T-S模型的模糊神经网络预测效果最好,能精确预测地下硐室爆破超挖量,对控制超挖量具有重要意义。
With a tunnel in Shandong Province as an example,fuzzy neural network based on T-S model was adopted together with relevant influencing factors to forecast over-excavation of underground openings. Based on the practical engineering situation,199 groups of data were chosen for this forecast model,among which 179 were used as training samples for training network and 20 as testing samples for validating the forecasting results of the model. It is found based on the calculation that the correlation coefficient,root mean square error and average relative error of the fuzzy neural network of over-excavation forecast based on T-S model were 0.962 8,0.449 and 6. 33%,respectively,showing more precise than the forecasting results obtained by BP neural network and regression model,indicating its significance in controlling over-excavation.
With a tunnel in Shandong Province as an example,fuzzy neural network based on T-S model was adopted together with relevant influencing factors to forecast over-excavation of underground openings. Based on the practical engineering situation,199 groups of data were chosen for this forecast model,among which 179 were used as training samples for training network and 20 as testing samples for validating the forecasting results of the model. It is found based on the calculation that the correlation coefficient,root mean square error and average relative error of the fuzzy neural network of over-excavation forecast based on T-S model were 0.962 8,0.449 and 6. 33%,respectively,showing more precise than the forecasting results obtained by BP neural network and regression model,indicating its significance in controlling over-excavation.
引文
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[2]Mohammadi Mohammad,Hossaini Mohammad Farouq,Mirzapour Bahman,et al.Use of fuzzy set theory for minimizing overbreak in underground blasting operations——A case study of Alborz Tunnel,Iran[J].International Journal of Mining Science and Technology,2015,25(3):439-445.
[3]Maerz N H,Ibarra J A,Franklin J A.Overbreak and underbreak in underground openings part1:Measurement using the light section method and digital image processing[J].Geotechnical and Geological Engineering,1996,14(4):307-323.
[4]Maerz N H,Ibarra J A,Franklin J A.Overbreak and underbreak in underground openings part 2:Causes and implications[J].Geotechnical and Geological Engineering,1996,14(4):325-340.
[5]Revey G F.Effects and control of overbreak in underground mining[J].Mining Engineering,1998,50(8):63-67.
[6]苏永华,孙小明,赵明华.隧道围岩超挖的分形特征研究[J].中国矿业大学学报,2006,35(1):89-93.
[7]Dey K,Murthy V.Prediction of blast-induced overbreak from uncontrolled burn-cut blasting in tunnels driven through medium rock class[J].Tunnelling and Underground Space Technology,2012,28:49-56.
[8]Gong F Q,Li X B,Zhang W.Over-excavation forecast of underground opening by using Bayes discriminant analysis method[J].Journal of Central South University of Technology,2008,15(4):498-502.
[9]孙少锐,吴继敏,魏继红.基于地质统计模型的小波神经网络在地下硐室超挖预测中的应用[J].岩石力学与工程学报,2003,22(8):1344-1350.
[10]冯伟,朱江,宋远卓.极弱胶结地层中大断面硐室开挖及支护技术研究[J].矿冶工程,2015(3):9-13.
[11]魏继红,吴继敏,陈显春,等.块体理论在金丽温高速公路连拱隧道超挖预测中的应用[J].水文地质工程地质,2005,32(5):60-63.
[12]邓宇.地下洞室超挖预测的费歇判别分析方法[J].水利与建筑工程学报,2015,13(3):209-212.
[13]王海霞.模糊神经网络在水质评价中的应用[D].重庆:重庆大学城市建设与环境工程学院,2002.
[14]史秀志,林大能,陈寿如.基于粗糙集模糊神经网络的爆破振动危害预测[J].爆炸与冲击,2009,29(4):401-407.
[15]杨文光,高艳辉,王清.模糊前向神经网络在瓦斯涌出量预测中的应用[J].安徽大学学报,2013,37(6):30-35.
[16]王小川,史峰,郁磊,等.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.
[17]周忠寿.基于T-S模型的模糊神经网络在水质评价中的应用[D].南京:河海大学计算机及信息工程学院,2007.
[18]孙增圻,徐红兵.基于T-S模型的模糊神经网络[J].清华大学学报,1997,37(3):76-80.