地铁隧道施工地表沉降预测模型及实证研究
|
摘要
随着地铁项目在我国的迅速发展,地铁隧道开挖引起的地表沉降越来越影响人们的日常生活,但由于土体介质的复杂性,国内外对于地表沉降规律的研究尚处于不成熟阶段。随着计算机技术的普遍应用,将海量的监测数据整理、分类、汇总,并与计算机软件相结合,预测地表最大沉降和时序沉降,总结地表沉降规律,为地铁施工的安全预警给予技术支持和理论指导,对提高地铁施工安全性有重要意义。
本文在阅读大量国内外文献的基础上,并结合武汉地铁的建设背景,首先对地下工程特点、隧道施工方法给予介绍,分析总结了影响地表沉降的因素;其次介绍了BP神经网络模型的应用,并通过实证分析预测地铁隧道横断面地表最大沉降量,然后对影响因素进行敏感性分析。最后介绍了时间序列模型的建模方法,并通过实证分析预测横断面测点的时序沉降量。结合Matlab和Eviews软件的应用,分别从空间、时间角度对地表沉降进行预测,通过实证分析,两种预测模型取得了较好的拟合效果,可以应用于地铁施工安全预警系统。
本文的研究涉及到土木工程、计算机技术、计量经济学、工程管理等各个学科的交叉与融合,提出了两种预测模型并实证分析,将BP神经网络时间序列的相关理论应用到地铁项目的建设中,为推动地铁隧道施工地表沉降规律的研究具有重要的现实意义。
With the metro project in China's rapid development, surface settlement caused by subway tunnel excavation affect people's daily lives increasingly. But because of the complexity of the soil media, surface settlement is still in the immature stage. With the widespread application of computer technology, mass of monitoring data will be sorted, classified, summarized, and combined with computer software to predict the maximum settlement and the timing of surface settlement, to provide technical support and theoretical guidance for the safety warning of the metro construction. To improve the safety of subway construction is important.
In this thesis, on the basis of literature reading, combined with the background of Wuhan subway construction. Firstly, the characteristics of the underground tunnel and construction method are introduced. Factors of ground settlement are analyzed and summarized. Secondly, introduce the application of BP neural network model, through empirical analysis, to predict the maximum settlement and the factors sensitivity analysis. Finally, introduce time series modeling method, through empirical analysis, to predict the timing of settlement monitoring points. Combined with applications of the Matlab and the Eviews software, this thesis predicts surface settlement from space, time respectively. Through empirical analysis, the two made a good prediction model fitting results and can be applied to the safety warning system of subway construction.
This study relates to civil engineering, computer technology, econometrics, project management, etc. Two prediction models are proposed. BP neural network theory and time series theory are applied to the subway construction, to promote the research of surface settlement by subway tunnel has important practical significance.
本文在阅读大量国内外文献的基础上,并结合武汉地铁的建设背景,首先对地下工程特点、隧道施工方法给予介绍,分析总结了影响地表沉降的因素;其次介绍了BP神经网络模型的应用,并通过实证分析预测地铁隧道横断面地表最大沉降量,然后对影响因素进行敏感性分析。最后介绍了时间序列模型的建模方法,并通过实证分析预测横断面测点的时序沉降量。结合Matlab和Eviews软件的应用,分别从空间、时间角度对地表沉降进行预测,通过实证分析,两种预测模型取得了较好的拟合效果,可以应用于地铁施工安全预警系统。
本文的研究涉及到土木工程、计算机技术、计量经济学、工程管理等各个学科的交叉与融合,提出了两种预测模型并实证分析,将BP神经网络时间序列的相关理论应用到地铁项目的建设中,为推动地铁隧道施工地表沉降规律的研究具有重要的现实意义。
With the metro project in China's rapid development, surface settlement caused by subway tunnel excavation affect people's daily lives increasingly. But because of the complexity of the soil media, surface settlement is still in the immature stage. With the widespread application of computer technology, mass of monitoring data will be sorted, classified, summarized, and combined with computer software to predict the maximum settlement and the timing of surface settlement, to provide technical support and theoretical guidance for the safety warning of the metro construction. To improve the safety of subway construction is important.
In this thesis, on the basis of literature reading, combined with the background of Wuhan subway construction. Firstly, the characteristics of the underground tunnel and construction method are introduced. Factors of ground settlement are analyzed and summarized. Secondly, introduce the application of BP neural network model, through empirical analysis, to predict the maximum settlement and the factors sensitivity analysis. Finally, introduce time series modeling method, through empirical analysis, to predict the timing of settlement monitoring points. Combined with applications of the Matlab and the Eviews software, this thesis predicts surface settlement from space, time respectively. Through empirical analysis, the two made a good prediction model fitting results and can be applied to the safety warning system of subway construction.
This study relates to civil engineering, computer technology, econometrics, project management, etc. Two prediction models are proposed. BP neural network theory and time series theory are applied to the subway construction, to promote the research of surface settlement by subway tunnel has important practical significance.
引文
[1]张庆贺,朱合华,庄荣.地铁与轻轨.北京.人民交通出版社, 2002
[2]沈静文.国务院批复22个城市地铁建设规划.人民日报海外版, 2010
[3] Peck R B. Deep excavations and tunnelling in soft ground. In: Proc.7th Int. Conf.SMFE. Mexico City: 1969. 225-290
[4] Schmidt B. Settlements and ground movements associated with tunnelling in soils. University of Illinois, 1969, 12(2): 62-70
[5] Attewell P B, Glossop N H, Farmer I W. Ground deformations caused by tunnelling insoi. Ground Engineering, 1978, 15(8): 32-41
[6] Rankin W J. Ground movement resulting from urban tunnelling:predictions and effects. In Engineering geology of underground movements. Nottingham University: 1988. 79-92
[7] Mair R J, Taylor R N, Bracegirdle. Subsurface settlement profiles above tunnels inclays. Geotechnique, 1993, 43(2): 315-320
[8] Celestino T B, Gomes R A M P. Errors in ground distortions due tosettlement trough adjustment. Tunnelling and underground space technology, 2000, 15(1): 97-100
[9]俞建霖,赵荣欣,龚晓南.软土地基基坑开挖地表沉降量的数值研究.浙江大学学报(自然科学版), 1998, 23(01): 14-15
[10]王善勇,唐春安,王述红等.地铁开挖对地基沉降影响的数值分析.东北大学学报, 2002, 35(09): 55-58
[11]芮勇勤,岳中琦,唐春安等.隧道开挖方式对建筑物桩基影响的数值模拟分析.岩石力学与工程学报, 2003, 16(05): 16-19
[12]彭畅,伋雨林,骆汉宾等.双线盾构施工对邻近建筑物影响的数值分析.岩石力学与工程学报, 2008, 27(2): 68-71
[13]方华,李国成,丁烈云等.隧道竖井施工对地下管线影响的数值模拟.铁道工程学报, 2008, 122(11): 62-65
[14]林志,朱合华,夏才初.双线盾构隧道施工过程相互影响的数值研究.公路隧道, 2009, 5(01): 85-89
[15]司翔宇.盾构推进引起周围土体变形的预测和控制研究[硕士学位论文].南京:南京林业大学, 2009
[16] Shi J, Ortigao J A R. Modular neural networks for predicting settlements during tunneling . Journal of geotechnical and geoenvironmental engineering, 1998, 10(5): 389
[17] Kim C Y, Bae G J, Hong S W, et al. Neural network based prediction of ground surface settlements due to tunnelling. Computers and Geotechnics, 2001, (28): 517-547
[18] Jan J C, Hung S, ASCE M. Neural Network Forecast Model in Deep Excavation. Journal of computing in civil engineering@American Society of Civil Engineers, 2002, 16(1): 59-65
[19] Leu S, Lo H. Neural-network-based regression model of ground surface settlement induced by deep excavation. 2004, 13(1) : 279-289
[20] Neaupane K M, Adhikari V R. Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunnelling and Underground Space Technology, 2006, 21(01): 151-169
[21] Jr. Santos O J, Celestino T B. Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunnelling and Underground Space Technology, 2008, 23(01): 481-491
[22] Cheng M, Tsai H, Ko C. Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering. Journal of computing in civil engineering@American Society of Civil Engineers, 2008, 22(4): 272-280
[23]袁金荣,赵福勇.基坑变形预测的时间序列分析.土木工程学报, 2001, 34(06): 55-59
[24]程楠.模糊神经网络在深基坑变形预报中的应用研究[硕士学位论文].郑州:郑州大学, 2002
[25]吴秀娟.人工神经网络在大坝监测数据分析中的应用研究[硕士学位论文].武汉:武汉大学, 2003
[26]李蔚.人工神经网络在高桩码头位移监测、预报中的应用研究[硕士学位论文].天津:天津大学, 2004
[27]周洪波.基于人工神经网络的大坝变形监测正反分析研究[硕士学位论文].武汉:武汉大学, 2004
[28]彭涛,杨岸英,梁杏等. BP神经网络-灰色系统联合模型预测软基沉降量.岩土力学, 2005, 26(11): 10-14
[29]曲永玲.城市地下工程地面变形预测神经网络模型研究[硕士学位论文].南京:南京工业大学, 2005
[30]张向东,于少春.改进BP神经网络算法在基坑沉降预测中的应用.山西建筑, 2007, 33 (01): 5-7
[31]齐干,朱瑞钧.基于BP网络的基坑周围地表沉降影响因素分析.地下空间与工程学报, 2007, 3(05): 863-867
[32]王万通.基于模糊神经网络的深基坑变形预测.重庆工学院学报(自然科学版), 2008, 22(06): 39-42
[33]叶国琳,邹律龙.基于BP网络的基坑位移监测和预报.建筑监督检测与造价, 2009, 12(03):21-25
[34]王翠,阎澍旺,刘润等.天津地区深基坑开挖引起的地表沉降分析.港工技术, 2005, 12(01): 35-38
[35]时亚昕,陶德敬,王明年.大断面浅埋暗挖隧道施工引起的地表移动及变形预测.岩土力学, 2008, 29(02): 465-474
[36]王丽琴,靳宝成,杨有海等.黄土路基工后沉降预测模型对比研究.铁道学报, 2008, 12(01): 45-49
[37]刘吉福,莫海鸿,李翔等.两种新的沉降推算方法.岩土力学, 2008, 29 (01): 140-144
[38]郭家伟,邵传青,王洁,等.时间序列模型和马尔柯夫模型在地面沉降预测中的集成应用.城市环境与城市生态, 2008, 21(01): 44-46
[39]宋建新.沧州地面沉降预测预报研究[硕士学位论文].北京:中国地质大学(北京),2009
[40]罗文柯,施式亮,谢东海.金竹山土朱煤矿开采地表沉降规律与灰色预测模型研究.中国安全科学学报, 2009, 19(10): 52-58
[41]徐永福.盾构推进引起地面变形的分析.地下工程与隧道, 2000, 1(01): 21-25
[42]孙钧,袁金荣.盾构施工扰动与地层移动及其智能神经网络预测.岩土工程学报, 2001, 23(03): 261-267
[43]张印涛.盾构施工引起的地表沉降研究[硕士学位论文].北京:北京工业大学, 2004
[44]吴波.复杂条件下城市地铁隧道施工地表沉降研究[博士学位论文].成都:西南交通大学, 2003
[45]杜建华,王玉林,沈仁强.浅谈盾构隧道施工引起的地表沉降.山西建筑, 2006, 32(06): 115-117
[46]赵运臣.城市地下工程施工引起的地表沉降规律研究及数据库开发[硕士学位论文].上海:同济大学, 2007
[47]荣永刚.浅埋暗挖隧道施工引起的地表沉降分析与对策.山西建筑, 2007, 32(01): 67-72
[48]吴贤国,陈跃庆,丁烈云,等.长江隧道盾构施工对建筑物的影响及其保护研究.铁道工程学报, 2008, 118(07): 57-60
[49]王绍君,王伟,刘少军.浅埋暗挖法地铁施工地表沉降的分析与研究.华中科技大学学报(城市科学版), 2008,25 (03): 65-68
[50]李莲欣.深基坑周围地表沉降规律的统计分析.山西建筑, 2008, 34(36): 94-95
[51]李国成,丁烈云.武汉长江隧道盾构施工引起的地表沉降预测.铁道工程学报, 2008, 116(05): 59-62
[52]刘联伟.复合地层中盾构法建设地铁地表沉降规律研究[博士学位论文].徐州:中国矿业大学, 2009
[53]王显星,包旭范,高强.上海铁路南站大型基坑工程施工对周边环境影响.岩石力学与工程学报, 2005, 24(2): 54-58
[54]俞凯.城市地铁隧道施工沉降对邻近建筑的影响研究[硕士学位论文].成都:西南交通大学, 2008
[55]周沈华,杨有海,王随新.深基坑开挖对周边地表沉降影响因素分析.土工基础, 2008, 22(02): 38-41
[56]钱七虎.迎接我国城市地下空间开发高潮.岩土工程学报, 1998, 20(01): 112-113
[57]北京城乡建设委员会.地下铁道工程施工及验收规范(GB50299-2002).北京.中国计划出版社, 1999
[58]中国住房与城乡建设部.盾构施工及验收规范(GB50446-2008).北京.中国建筑工业出版社, 2008
[59]建设综合勘查研究设计院.建筑变形测量规范(JBJ8-2007).北京.中国建筑工业出版社, 2007
[60]周文波.盾构法隧道施工技术及应用.北京.中国建筑工业出版社, 2004
[61]李世平.岩土力学简明教程.徐州.中国矿业大学出版社, 1996
[62]田景文,高美娟.人工神经网络算法研究及应用.北京.北京理工大学出版社, 2006
[63]于浩洋,初红霞,王希凤.控制系统仿真与应用—MATLAB实用教程.北京.化学工业出版社, 2009
[64] Suwansawat S, Einstein. Artifical neural network for prediction the maximum surface settlement caused by EPB shield tunneling. Space technol, 2006, 21(2): 133-150
[65]孙敬水.计量经济学.北京.清华大学出版社, 2005
[66]贺锏.计量经济学.北京.中国统计出版社, 2002
[67]张晓峒. Eviews使用指南与案例.北京.机械工业出版社, 2009
[2]沈静文.国务院批复22个城市地铁建设规划.人民日报海外版, 2010
[3] Peck R B. Deep excavations and tunnelling in soft ground. In: Proc.7th Int. Conf.SMFE. Mexico City: 1969. 225-290
[4] Schmidt B. Settlements and ground movements associated with tunnelling in soils. University of Illinois, 1969, 12(2): 62-70
[5] Attewell P B, Glossop N H, Farmer I W. Ground deformations caused by tunnelling insoi. Ground Engineering, 1978, 15(8): 32-41
[6] Rankin W J. Ground movement resulting from urban tunnelling:predictions and effects. In Engineering geology of underground movements. Nottingham University: 1988. 79-92
[7] Mair R J, Taylor R N, Bracegirdle. Subsurface settlement profiles above tunnels inclays. Geotechnique, 1993, 43(2): 315-320
[8] Celestino T B, Gomes R A M P. Errors in ground distortions due tosettlement trough adjustment. Tunnelling and underground space technology, 2000, 15(1): 97-100
[9]俞建霖,赵荣欣,龚晓南.软土地基基坑开挖地表沉降量的数值研究.浙江大学学报(自然科学版), 1998, 23(01): 14-15
[10]王善勇,唐春安,王述红等.地铁开挖对地基沉降影响的数值分析.东北大学学报, 2002, 35(09): 55-58
[11]芮勇勤,岳中琦,唐春安等.隧道开挖方式对建筑物桩基影响的数值模拟分析.岩石力学与工程学报, 2003, 16(05): 16-19
[12]彭畅,伋雨林,骆汉宾等.双线盾构施工对邻近建筑物影响的数值分析.岩石力学与工程学报, 2008, 27(2): 68-71
[13]方华,李国成,丁烈云等.隧道竖井施工对地下管线影响的数值模拟.铁道工程学报, 2008, 122(11): 62-65
[14]林志,朱合华,夏才初.双线盾构隧道施工过程相互影响的数值研究.公路隧道, 2009, 5(01): 85-89
[15]司翔宇.盾构推进引起周围土体变形的预测和控制研究[硕士学位论文].南京:南京林业大学, 2009
[16] Shi J, Ortigao J A R. Modular neural networks for predicting settlements during tunneling . Journal of geotechnical and geoenvironmental engineering, 1998, 10(5): 389
[17] Kim C Y, Bae G J, Hong S W, et al. Neural network based prediction of ground surface settlements due to tunnelling. Computers and Geotechnics, 2001, (28): 517-547
[18] Jan J C, Hung S, ASCE M. Neural Network Forecast Model in Deep Excavation. Journal of computing in civil engineering@American Society of Civil Engineers, 2002, 16(1): 59-65
[19] Leu S, Lo H. Neural-network-based regression model of ground surface settlement induced by deep excavation. 2004, 13(1) : 279-289
[20] Neaupane K M, Adhikari V R. Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunnelling and Underground Space Technology, 2006, 21(01): 151-169
[21] Jr. Santos O J, Celestino T B. Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunnelling and Underground Space Technology, 2008, 23(01): 481-491
[22] Cheng M, Tsai H, Ko C. Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering. Journal of computing in civil engineering@American Society of Civil Engineers, 2008, 22(4): 272-280
[23]袁金荣,赵福勇.基坑变形预测的时间序列分析.土木工程学报, 2001, 34(06): 55-59
[24]程楠.模糊神经网络在深基坑变形预报中的应用研究[硕士学位论文].郑州:郑州大学, 2002
[25]吴秀娟.人工神经网络在大坝监测数据分析中的应用研究[硕士学位论文].武汉:武汉大学, 2003
[26]李蔚.人工神经网络在高桩码头位移监测、预报中的应用研究[硕士学位论文].天津:天津大学, 2004
[27]周洪波.基于人工神经网络的大坝变形监测正反分析研究[硕士学位论文].武汉:武汉大学, 2004
[28]彭涛,杨岸英,梁杏等. BP神经网络-灰色系统联合模型预测软基沉降量.岩土力学, 2005, 26(11): 10-14
[29]曲永玲.城市地下工程地面变形预测神经网络模型研究[硕士学位论文].南京:南京工业大学, 2005
[30]张向东,于少春.改进BP神经网络算法在基坑沉降预测中的应用.山西建筑, 2007, 33 (01): 5-7
[31]齐干,朱瑞钧.基于BP网络的基坑周围地表沉降影响因素分析.地下空间与工程学报, 2007, 3(05): 863-867
[32]王万通.基于模糊神经网络的深基坑变形预测.重庆工学院学报(自然科学版), 2008, 22(06): 39-42
[33]叶国琳,邹律龙.基于BP网络的基坑位移监测和预报.建筑监督检测与造价, 2009, 12(03):21-25
[34]王翠,阎澍旺,刘润等.天津地区深基坑开挖引起的地表沉降分析.港工技术, 2005, 12(01): 35-38
[35]时亚昕,陶德敬,王明年.大断面浅埋暗挖隧道施工引起的地表移动及变形预测.岩土力学, 2008, 29(02): 465-474
[36]王丽琴,靳宝成,杨有海等.黄土路基工后沉降预测模型对比研究.铁道学报, 2008, 12(01): 45-49
[37]刘吉福,莫海鸿,李翔等.两种新的沉降推算方法.岩土力学, 2008, 29 (01): 140-144
[38]郭家伟,邵传青,王洁,等.时间序列模型和马尔柯夫模型在地面沉降预测中的集成应用.城市环境与城市生态, 2008, 21(01): 44-46
[39]宋建新.沧州地面沉降预测预报研究[硕士学位论文].北京:中国地质大学(北京),2009
[40]罗文柯,施式亮,谢东海.金竹山土朱煤矿开采地表沉降规律与灰色预测模型研究.中国安全科学学报, 2009, 19(10): 52-58
[41]徐永福.盾构推进引起地面变形的分析.地下工程与隧道, 2000, 1(01): 21-25
[42]孙钧,袁金荣.盾构施工扰动与地层移动及其智能神经网络预测.岩土工程学报, 2001, 23(03): 261-267
[43]张印涛.盾构施工引起的地表沉降研究[硕士学位论文].北京:北京工业大学, 2004
[44]吴波.复杂条件下城市地铁隧道施工地表沉降研究[博士学位论文].成都:西南交通大学, 2003
[45]杜建华,王玉林,沈仁强.浅谈盾构隧道施工引起的地表沉降.山西建筑, 2006, 32(06): 115-117
[46]赵运臣.城市地下工程施工引起的地表沉降规律研究及数据库开发[硕士学位论文].上海:同济大学, 2007
[47]荣永刚.浅埋暗挖隧道施工引起的地表沉降分析与对策.山西建筑, 2007, 32(01): 67-72
[48]吴贤国,陈跃庆,丁烈云,等.长江隧道盾构施工对建筑物的影响及其保护研究.铁道工程学报, 2008, 118(07): 57-60
[49]王绍君,王伟,刘少军.浅埋暗挖法地铁施工地表沉降的分析与研究.华中科技大学学报(城市科学版), 2008,25 (03): 65-68
[50]李莲欣.深基坑周围地表沉降规律的统计分析.山西建筑, 2008, 34(36): 94-95
[51]李国成,丁烈云.武汉长江隧道盾构施工引起的地表沉降预测.铁道工程学报, 2008, 116(05): 59-62
[52]刘联伟.复合地层中盾构法建设地铁地表沉降规律研究[博士学位论文].徐州:中国矿业大学, 2009
[53]王显星,包旭范,高强.上海铁路南站大型基坑工程施工对周边环境影响.岩石力学与工程学报, 2005, 24(2): 54-58
[54]俞凯.城市地铁隧道施工沉降对邻近建筑的影响研究[硕士学位论文].成都:西南交通大学, 2008
[55]周沈华,杨有海,王随新.深基坑开挖对周边地表沉降影响因素分析.土工基础, 2008, 22(02): 38-41
[56]钱七虎.迎接我国城市地下空间开发高潮.岩土工程学报, 1998, 20(01): 112-113
[57]北京城乡建设委员会.地下铁道工程施工及验收规范(GB50299-2002).北京.中国计划出版社, 1999
[58]中国住房与城乡建设部.盾构施工及验收规范(GB50446-2008).北京.中国建筑工业出版社, 2008
[59]建设综合勘查研究设计院.建筑变形测量规范(JBJ8-2007).北京.中国建筑工业出版社, 2007
[60]周文波.盾构法隧道施工技术及应用.北京.中国建筑工业出版社, 2004
[61]李世平.岩土力学简明教程.徐州.中国矿业大学出版社, 1996
[62]田景文,高美娟.人工神经网络算法研究及应用.北京.北京理工大学出版社, 2006
[63]于浩洋,初红霞,王希凤.控制系统仿真与应用—MATLAB实用教程.北京.化学工业出版社, 2009
[64] Suwansawat S, Einstein. Artifical neural network for prediction the maximum surface settlement caused by EPB shield tunneling. Space technol, 2006, 21(2): 133-150
[65]孙敬水.计量经济学.北京.清华大学出版社, 2005
[66]贺锏.计量经济学.北京.中国统计出版社, 2002
[67]张晓峒. Eviews使用指南与案例.北京.机械工业出版社, 2009