基于MIV-BP模型和AIC准则的盾构掘进参数优化研究
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摘要
为解决水下隧道和隧洞工程盾构施工关键掘进参数在复合地层中难以控制的问题,以广东陆丰核电站1、2号机组排水隧洞工程盾构施工为背景,将平均影响值(Mean Impact Value,MIV)算法引入BP神经网络模型,筛选出对施工效果影响显著的关键掘进参数,在此基础上,基于AIC准则对其进行最优分布拟合,提出以50%和90%置信水平下的置信区间,分别作为掘进参数的控制区间和预警区间的掘进参数优化设计方案,并基于Python脚本语言自带的开源Scikit-Learn、SciPy模块库开发了相应的程序。分析结果表明,刀盘扭矩、总推力等参数对隧洞拱顶沉降起重要的控制作用,所开发程序具有良好的统计分析、快速指导施工的功能,可以为同类型盾构在相似复合地层下掘进参数的选取、优化和隧洞拱顶沉降量的控制提供参考。
Aiming at the problems that the key boring parameters in mixed ground are more difficult to control in submarine drainage tunnels and caverns, this paper proposes a specific solution. In this study, based on the engineering application example of submarine drainage tunnel project of No.1 and No.2 Unit in Lufeng Nuclear Power Station, the Mean Impact Value(MIV) algorithm is introduced to the BP neural network model and the key boring parameters that have significant impact on shield construction effect can be screened out. On this basis, with the optimum distribution fitting of boring parameters based on Akaike Information Criterion(AIC), this paper proposes an optimized scheme for boring parameters, which use the confidence intervals of 50% and 90% confidence level as the controlling intervals and warning intervals respectively. The corresponding program is developed based on the open source module libraries of Scikit-Learn and SciPy of Python. The analysis results indicate that boring parameters such as cutter head torque and total thrust, have produced significant influences on the control in vault settlement of the tunnel. The developed program has some practical function of statistic analysis and quick guidance of construction, which provide the reference of selection and optimization of boring parameters and the control of vault settlement for the same type of shield construction in similar mixed ground.
Aiming at the problems that the key boring parameters in mixed ground are more difficult to control in submarine drainage tunnels and caverns, this paper proposes a specific solution. In this study, based on the engineering application example of submarine drainage tunnel project of No.1 and No.2 Unit in Lufeng Nuclear Power Station, the Mean Impact Value(MIV) algorithm is introduced to the BP neural network model and the key boring parameters that have significant impact on shield construction effect can be screened out. On this basis, with the optimum distribution fitting of boring parameters based on Akaike Information Criterion(AIC), this paper proposes an optimized scheme for boring parameters, which use the confidence intervals of 50% and 90% confidence level as the controlling intervals and warning intervals respectively. The corresponding program is developed based on the open source module libraries of Scikit-Learn and SciPy of Python. The analysis results indicate that boring parameters such as cutter head torque and total thrust, have produced significant influences on the control in vault settlement of the tunnel. The developed program has some practical function of statistic analysis and quick guidance of construction, which provide the reference of selection and optimization of boring parameters and the control of vault settlement for the same type of shield construction in similar mixed ground.
引文
[1] 孙谋,谭忠盛.盾构法修建水下隧道的关键技术问题[J].中国工程科学,2009(7):18-23.
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[3] 赵博剑,周建军,谭忠盛,等.复合地层盾构掘进参数及其与地层相关性分析[J].土木工程学报,2017,50(S1):140-144.
[4] 朱宏海.上软下硬复合地层地铁盾构隧道设计及施工探析[J].隧道建设,2015,35(2):144-148.
[5] 魏纲,周洋,魏新江.土压平衡盾构掘进参数对地面隆起影响的研究[J].地下空间与工程学报,2012,8(S2):1703-1709.
[6] 李承辉,贺少辉,刘夏冰.粗粒径砂卵石地层中泥水平衡盾构下穿黄河掘进参数控制研究[J].土木工程学报,2017,50(S2):147-152.
[7] 杨旸,谭忠盛,彭斌,等.富水圆砾地层土压平衡盾构掘进参数优化研究[J].土木工程学报,2017,50(S1):94-98.
[8] 管会生,张瑀,杨延栋.新街台格庙矿区斜井隧道双模式盾构关键掘进参数配置研究[J].隧道建设(中英文),2015,35(4):377-381.
[9] 肖超,阳军生,李科,等.穿越湘江地层裂隙密集区域大直径泥水平衡盾构掘进参数控制[J].铁道科学与工程学报,2013,10(5):40-46.
[10] 张志奇,李彤,韩爱民,等.复杂地层盾构掘进速率和刀盘扭矩预测模型及其地层适应性研究[J].隧道建设,2016,36(12):1449-1455.
[11] 褚东升.长沙地铁下穿湘江土压平衡盾构隧道掘进参数研究[D].长沙:中南大学,2012.
[12] 徐新,王帆,齐保栋,等.软硬不均地层盾构近接下穿施工掘进参数优化分析[J].路基工程,2017(3):212-215.
[13] 陈秋鑫,李俊才,刘光臣,等.盾构施工安全穿越禄口机场复合地层段的数值模拟与监测分析[J].铁道标准设计,2014,58(12):94-98.
[14] 李超,李涛,李正,等.基于BP神经网络的复合地层盾构掘进参数预测与分析[J].土木工程学报,2017,50(S1):145-150.
[15] 朱北斗,龚国芳,周如林,等.基于盾构掘进参数的BP神经网络地层识别[J].浙江大学学报(工学版),2011,45(5):851-857.
[16] 孙谋,刘维宁.软土地层盾构近距穿越老式建筑区掘进参数分析[J].土木工程学报,2009,42(12):170-176.
[17] 邵成猛.基于盾构掘进参数的LVQ神经网络地层识别[J].石家庄铁道大学学报(自然科学版),2016,29(1):93-96,102.
[18] 任宏,周其明.神经网络在工程造价和主要工程量快速估算中的应用研究[J].土木工程学报,2005,38(8):135-138.
[19] Dombi G W,Nandi P,Saxe J M,et al.Prediction of rib fracture injury outcome by an artificial neural network[J].Journal of Trauma,1995,39(5):915.
[20] 周文波,胡珉.盾构法隧道施工主要参数控制方法研究[J].岩石力学与工程学报,2003(S1):2430-2433.
[21] 郭信君.超大直径越江隧道泥水盾构掘进模型试验研究[J].铁道标准设计,2008(10):71-75.
[22] 侯喜冬.盾构施工引起地表沉降的BP神经网络预测[J].隧道建设,2007(3):17-20.
[23] 颜波,杨国龙,林辉,等.盾构隧道施工参数优化与地表沉降控制研究[J].地下空间与工程学报,2011(S2):1683-1687.
[24] 胡珉,周文波.基于多级神经网络的盾构法隧道施工参数控制[J].计算机工程,2005(8):192-194.
[25] 熊静,喻钢.基于多级神经网络的盾构法隧道施工参数优化[J].计算机工程与科学,2006(5):133-135,140.
[2] 刘招伟.城陵矶穿越长江水下软硬不均地层隧道修建技术[J].中国铁道科学,2006(3):139-144.
[3] 赵博剑,周建军,谭忠盛,等.复合地层盾构掘进参数及其与地层相关性分析[J].土木工程学报,2017,50(S1):140-144.
[4] 朱宏海.上软下硬复合地层地铁盾构隧道设计及施工探析[J].隧道建设,2015,35(2):144-148.
[5] 魏纲,周洋,魏新江.土压平衡盾构掘进参数对地面隆起影响的研究[J].地下空间与工程学报,2012,8(S2):1703-1709.
[6] 李承辉,贺少辉,刘夏冰.粗粒径砂卵石地层中泥水平衡盾构下穿黄河掘进参数控制研究[J].土木工程学报,2017,50(S2):147-152.
[7] 杨旸,谭忠盛,彭斌,等.富水圆砾地层土压平衡盾构掘进参数优化研究[J].土木工程学报,2017,50(S1):94-98.
[8] 管会生,张瑀,杨延栋.新街台格庙矿区斜井隧道双模式盾构关键掘进参数配置研究[J].隧道建设(中英文),2015,35(4):377-381.
[9] 肖超,阳军生,李科,等.穿越湘江地层裂隙密集区域大直径泥水平衡盾构掘进参数控制[J].铁道科学与工程学报,2013,10(5):40-46.
[10] 张志奇,李彤,韩爱民,等.复杂地层盾构掘进速率和刀盘扭矩预测模型及其地层适应性研究[J].隧道建设,2016,36(12):1449-1455.
[11] 褚东升.长沙地铁下穿湘江土压平衡盾构隧道掘进参数研究[D].长沙:中南大学,2012.
[12] 徐新,王帆,齐保栋,等.软硬不均地层盾构近接下穿施工掘进参数优化分析[J].路基工程,2017(3):212-215.
[13] 陈秋鑫,李俊才,刘光臣,等.盾构施工安全穿越禄口机场复合地层段的数值模拟与监测分析[J].铁道标准设计,2014,58(12):94-98.
[14] 李超,李涛,李正,等.基于BP神经网络的复合地层盾构掘进参数预测与分析[J].土木工程学报,2017,50(S1):145-150.
[15] 朱北斗,龚国芳,周如林,等.基于盾构掘进参数的BP神经网络地层识别[J].浙江大学学报(工学版),2011,45(5):851-857.
[16] 孙谋,刘维宁.软土地层盾构近距穿越老式建筑区掘进参数分析[J].土木工程学报,2009,42(12):170-176.
[17] 邵成猛.基于盾构掘进参数的LVQ神经网络地层识别[J].石家庄铁道大学学报(自然科学版),2016,29(1):93-96,102.
[18] 任宏,周其明.神经网络在工程造价和主要工程量快速估算中的应用研究[J].土木工程学报,2005,38(8):135-138.
[19] Dombi G W,Nandi P,Saxe J M,et al.Prediction of rib fracture injury outcome by an artificial neural network[J].Journal of Trauma,1995,39(5):915.
[20] 周文波,胡珉.盾构法隧道施工主要参数控制方法研究[J].岩石力学与工程学报,2003(S1):2430-2433.
[21] 郭信君.超大直径越江隧道泥水盾构掘进模型试验研究[J].铁道标准设计,2008(10):71-75.
[22] 侯喜冬.盾构施工引起地表沉降的BP神经网络预测[J].隧道建设,2007(3):17-20.
[23] 颜波,杨国龙,林辉,等.盾构隧道施工参数优化与地表沉降控制研究[J].地下空间与工程学报,2011(S2):1683-1687.
[24] 胡珉,周文波.基于多级神经网络的盾构法隧道施工参数控制[J].计算机工程,2005(8):192-194.
[25] 熊静,喻钢.基于多级神经网络的盾构法隧道施工参数优化[J].计算机工程与科学,2006(5):133-135,140.