深基坑工程施工过程动态反演与变形预测
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摘要
城市中深基坑工程开挖造成的土体变形会对工程和周围环境产生不利的影响。因此确定出基坑开挖过程中土体的物性参数,对于准确预测深基坑工程的开挖变形情况,指导工程顺利进行是很有意义的。
本文采用了参数反分析的研究方法。应用ADINA有限元软件为正分析手段建立了模拟基坑开挖过程的模型;应用BP神经网络作为反分析工具建立了土性参数辨识系统,通过基坑支护结构实测位移数据反演修正土性参数的取值。用经过反演的土性参数作为有限元分析的输入数据,达到对深基坑的开挖过程进行较为准确模拟的目的。
以有限元为代表的数值解法在深基坑开挖过程模拟中有传统方法不可替代的优势。本文选用的ADINA有限元软件带有多种常见土体材料模型,在单元生死的处理上采用单元刚度逐渐消逝的算法。因此能够很好地动态模拟深基坑工程的分步开挖施工过程。本构模型的选择和土性参数取值的确定是有限元计算面临的问题。本文较详细的介绍了土的弹塑性模型,选用了比较适合工程实际情况且参数不多的D-P本构模型。对于难以准确测定的土体弹性模量E值,选用BP神经网络对其进行了反演分析,充分利用了神经网络突出的非线性映射功能。在网络结构的选择上采用了分模块处理的方法,使得网络收敛能够顺利的完成。最后通过天津某地铁车站基坑开挖工程的计算,得到了能够反映工程实际情况的支护结构变形值预测值。证明了利用神经网络进行土性参数辨识,可以很好的应用于深基坑开挖的变形预测中。
Digging the deep excavation in the city can cause earth transfiguration that would influence the works and the surroundings obviously. So it is important to determine the soil physical parameters in digging of the deep excavation to forecast the earth transfiguration levels and direct the works.
This study is researched by parameter back analysis method. In order to simulate the digging process of the deep excavation accurately ADINA finite element software is used to establish the simulate model of the digging process of the excavation as forward analysis method, BP Neural Networks is used to established the recognition system of the soil physical parameters as back analysis method and the measure data of displacements of shoring of trench in the real works is used to correct the soil physical parameters with method of inversion which is being input in finite element analysis with method of inversion.
Numerical solution that is represented by finite element analysis has the advantages obviously than conventional method in simulate the digging process of the deep excavation. ADINA finite element software, used in this study has kinds of usual earth material models and the element birth/death unit is calculated with unit rigidity fade away method, so it can simulate the digging process of the deep excavation step by step accurately. It is a problem for finite element analysis to choose the constitutive and determines the data of the soil physical parameters. In this study choose the D-P constitutive which is suitable for practical situations of the project and have few parameters and introduce the elastic-plastic constitutive in detail, BP Neural Networks is chose to invert and analyze the earth elastic ratio (E), which is hard to measured, can make a better use of the nonlinearity mapping function of the neural networks. Blocking method is chose in network structure to complete the network convergence smoothly. Conclusion, the model can reflect the displacement of the shoring of trench correctly and can be proved by a real excavation work of a underground metro station in Tianjin. The study indicates that Neural Networks can use in soil physical parameters recognizing, and can used to forecast the displacements in deep excavation.
本文采用了参数反分析的研究方法。应用ADINA有限元软件为正分析手段建立了模拟基坑开挖过程的模型;应用BP神经网络作为反分析工具建立了土性参数辨识系统,通过基坑支护结构实测位移数据反演修正土性参数的取值。用经过反演的土性参数作为有限元分析的输入数据,达到对深基坑的开挖过程进行较为准确模拟的目的。
以有限元为代表的数值解法在深基坑开挖过程模拟中有传统方法不可替代的优势。本文选用的ADINA有限元软件带有多种常见土体材料模型,在单元生死的处理上采用单元刚度逐渐消逝的算法。因此能够很好地动态模拟深基坑工程的分步开挖施工过程。本构模型的选择和土性参数取值的确定是有限元计算面临的问题。本文较详细的介绍了土的弹塑性模型,选用了比较适合工程实际情况且参数不多的D-P本构模型。对于难以准确测定的土体弹性模量E值,选用BP神经网络对其进行了反演分析,充分利用了神经网络突出的非线性映射功能。在网络结构的选择上采用了分模块处理的方法,使得网络收敛能够顺利的完成。最后通过天津某地铁车站基坑开挖工程的计算,得到了能够反映工程实际情况的支护结构变形值预测值。证明了利用神经网络进行土性参数辨识,可以很好的应用于深基坑开挖的变形预测中。
Digging the deep excavation in the city can cause earth transfiguration that would influence the works and the surroundings obviously. So it is important to determine the soil physical parameters in digging of the deep excavation to forecast the earth transfiguration levels and direct the works.
This study is researched by parameter back analysis method. In order to simulate the digging process of the deep excavation accurately ADINA finite element software is used to establish the simulate model of the digging process of the excavation as forward analysis method, BP Neural Networks is used to established the recognition system of the soil physical parameters as back analysis method and the measure data of displacements of shoring of trench in the real works is used to correct the soil physical parameters with method of inversion which is being input in finite element analysis with method of inversion.
Numerical solution that is represented by finite element analysis has the advantages obviously than conventional method in simulate the digging process of the deep excavation. ADINA finite element software, used in this study has kinds of usual earth material models and the element birth/death unit is calculated with unit rigidity fade away method, so it can simulate the digging process of the deep excavation step by step accurately. It is a problem for finite element analysis to choose the constitutive and determines the data of the soil physical parameters. In this study choose the D-P constitutive which is suitable for practical situations of the project and have few parameters and introduce the elastic-plastic constitutive in detail, BP Neural Networks is chose to invert and analyze the earth elastic ratio (E), which is hard to measured, can make a better use of the nonlinearity mapping function of the neural networks. Blocking method is chose in network structure to complete the network convergence smoothly. Conclusion, the model can reflect the displacement of the shoring of trench correctly and can be proved by a real excavation work of a underground metro station in Tianjin. The study indicates that Neural Networks can use in soil physical parameters recognizing, and can used to forecast the displacements in deep excavation.
引文
[1]龚晓南,高有潮.深基坑工程设计施工手册[M].北京:中国建筑工业出版社.1998
[2]刘二栓.深基坑工程特点及存在的问题.有色金属设计[J].2004(1):45-47
[3]从蔼森编著.地下连续墙的设计施工与应用[M].北京:中国水利水电出版社. 2001
[4]齐干,陈学军,朱瑞钧.BP神经网络在深基坑工程中的应用[J].建筑技术开发.2004,31(5):22-26
[5]刘兴远.神经网络理论在土木工程应用中的几点认识[J].岩土工程学报.2003,25(4):514-516
[6] Pavol Oblozinsky, Keizo Ugai, Masaaki Katagiri, Kunio Saitoh, Takeshi Ishii, Toru Masuda,Kiyoshi Kuwabara. A design method for slurry trench wall stability in sandy ground based on the elasto-plastic FEM. Computers and Geotechnics[J].2001 (28):145-159
[7]杨林德.岩土工程问题的反演理论与工程实践[M].北京:科学出版社,1996
[8]吉林等.岩土工程中反分析的研究进展[J].水利水运工程学报2002(4):57-63
[9]袁金荣,赵福勇.基坑变形预测的时间序列分析[J].土木工程学报.2001,34(6):55-59
[10]李庆来,谢康和,曾国熙.基坑开挖变形预测与信息施工技术[J].水利学报,2000(1):42-48
[11]J. C. Jan, Shih-Lin Hung, M.ASCE, S. Y. Chi, and J. C. Chern. Neural Network Forecast Model in Deep Excavation. JOURNAL OF COMPUTING IN CIVIL NGINEERING[J].2002:59-65
[12]潘别桐,等编著.工程地质数值法[M].北京:地质出版社,1994. 95-124
[13]齐欢.反分析的模型、方法与工程应用[M].北京:科学出版社.2003.6,35-140
[14]焦李成,神经网络的应用与实现[M],西安:西安电子科技大学出版社,1990.54-78
[15]张乃尧,阎平凡.神经网络与模糊控制[M].北京:清华大学出版社,1998.1-56
[16]张际先,苾霞.神经网络及其在工程中的应用[M].北京:机械工业出版社,1996.1-74
[17]史佩栋.深基坑工程特殊技术问题[M].人民交通出版社,2004
[18]飞思科技产品研发中心编著. MATLAB 6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003
[19]丛爽编著.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科学技术大学出版社,1998
[20]袁金荣.地下工程施工变形的智能预测与控制.博士学位论文.同济大学.2001
[21]韩红霞,窦远明,王喜燕,崔武文.基坑支护中水土压力合算与分算的综合分析.河北工业大学学报,2002,31(6):62-65
[22]Donald J. Berger, and Susan O. Tryon. APPROACH TO DESIGNING STRUCTURAL SLURRY WALLS. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING,1999:1011-1022
[23]彭林欣.基坑开挖地下连续墙有限元分析系统.硕士学位论文.中山大学.2002
[24]蔡美峰.岩石力学与工程.北京:科学出版社,2002.284-301
[25]Mohamed Emam Abd El-Razek1.NEW METHOD FOR CONSTRUCTION OF DIAPHRAGM WALLS. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT,1999 : 233-241
[26]杨光华.深基坑支护结构的实用计算方法及其应用.岩石力学.2004(12):1885-1896
[27]Charles W.W.ng,member, ASCE, Ryan W.M.Yan. Stress Transfer and Deformation Mechanisms around A Diaphragm Wall Panel. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT,1998 : 638-648
[28]S.Rajasekaran, M.F.Febin and J.V.Ramasamy. Artificial Fussy Networks in Civil Engineering. COMPUTERS $ STUCTURES,1996,61(2):291-302
[29]IN-Mo lee, Jeong-Hark Lee. Prediction of Pile Bearing Capacity using Artificial Neural Networks. COMPUTERS $ GEOTECHNICS,1996,18(3):291-302
[30]M.Y.Rafig, G.Bugmann, D.J. Easterbrook. Neural network Design for Engineering Applications. COMPUTERS $ STUCTURES,2001,61:1541-1552
[31]C.Y. Kim, G.J. Bae, S.W. Hong, C.H. Park,H.K. Moon, H.S. Shin. Neural network based prediction of ground surface settlements due to tunneling. COMPUTERS $ STUCTURES,2001 (28) :517–547
[32]娄奕红,俞三溥,王秉勇.基坑支护结构内力及变形动态分析.岩石力学与工程学报,22(3):462-466
[33]高浪,谢康和.人工神经网络在岩土工程中的应用.土木工程学报,2002,35(4):77-81
[34]陈昌彦,王思敬,沈小克.边坡岩体稳定性的人工神经网络预测模型.岩土工程学报,2001,23(2):157-161
[35]李庆来.人工神经网络与概率方法在深基坑开挖信息施工中的应用.土木工程学报,2001,34(2):96-100
[36]周瑞忠,邱高翔.基于BP网络的深基坑支护位移反分析.土木工程学报,2001,34(6):60-62
[37]高沛峻,邓安福,李键.应用BP网络预测内撑式人工挖孔桩支护结构的水平变形.地下空间,2000,20(3):161-165
[38]陈灿寿,张尚根,余有山.深基坑支护结构变形计算.岩石力学与工程学报,2004,23(12):2065-2068
[39]刘巍,刘兴远,陆新.人工神经网络在强夯工程中的运用分析讨论.地下空间,2004,24(2):244-249
[40]郑永保,陈建功,董占文.应用径向基神经网络预测单桩极限承载力.低下空间,2004,24(4):519-521
[41]姜志强,孙树林,李磊.基坑开挖中土压力计算模型探讨.河海大学学报(自然科学版),2003,31(3):303-306
[42]王占生,张克绪.土一结构相互作用的修正刚度法及其在基坑工程中的应用.岩土工程学报. 2002, 24(5):652-655
[43]葛树高.信息化施工是岩土地下工程进步的关键.岩土工程界,1998(4):31~32
[44] Donald J. Berger, P.E., and Susan O. Tryon, P.E. APPROACH TO DESIGNING STRUCTURAL SLURRY WALLS. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING. 1999.9:1011-1022
[45]袁曾任,人工神经网络及其应用,北京:清华大学出版社,1999.66-274
[46]黄宗益,李兴华.信息化施工概述.建筑机械,2005:64-66
[47]高广运.黄诚.孙雨明.遗传BP神经网络在深基坑开挖监测中的应用.地下空间. 2002(4):288~293
[48] T. Kerh, Y.C. Yee. Analysis of a deformed three-dimensional culvert structure using neural networks. Advances in Engineering Software , 2000 (31) : 367~375
[49]蔡兴国,安天瑜,周苏荃. Grey series time—delay predicting model in state estimation for power distribution networks. Journal of Harbin Institute of Technology(哈尔滨工业大学学报:英文版).2003,10(2):120-123
[2]刘二栓.深基坑工程特点及存在的问题.有色金属设计[J].2004(1):45-47
[3]从蔼森编著.地下连续墙的设计施工与应用[M].北京:中国水利水电出版社. 2001
[4]齐干,陈学军,朱瑞钧.BP神经网络在深基坑工程中的应用[J].建筑技术开发.2004,31(5):22-26
[5]刘兴远.神经网络理论在土木工程应用中的几点认识[J].岩土工程学报.2003,25(4):514-516
[6] Pavol Oblozinsky, Keizo Ugai, Masaaki Katagiri, Kunio Saitoh, Takeshi Ishii, Toru Masuda,Kiyoshi Kuwabara. A design method for slurry trench wall stability in sandy ground based on the elasto-plastic FEM. Computers and Geotechnics[J].2001 (28):145-159
[7]杨林德.岩土工程问题的反演理论与工程实践[M].北京:科学出版社,1996
[8]吉林等.岩土工程中反分析的研究进展[J].水利水运工程学报2002(4):57-63
[9]袁金荣,赵福勇.基坑变形预测的时间序列分析[J].土木工程学报.2001,34(6):55-59
[10]李庆来,谢康和,曾国熙.基坑开挖变形预测与信息施工技术[J].水利学报,2000(1):42-48
[11]J. C. Jan, Shih-Lin Hung, M.ASCE, S. Y. Chi, and J. C. Chern. Neural Network Forecast Model in Deep Excavation. JOURNAL OF COMPUTING IN CIVIL NGINEERING[J].2002:59-65
[12]潘别桐,等编著.工程地质数值法[M].北京:地质出版社,1994. 95-124
[13]齐欢.反分析的模型、方法与工程应用[M].北京:科学出版社.2003.6,35-140
[14]焦李成,神经网络的应用与实现[M],西安:西安电子科技大学出版社,1990.54-78
[15]张乃尧,阎平凡.神经网络与模糊控制[M].北京:清华大学出版社,1998.1-56
[16]张际先,苾霞.神经网络及其在工程中的应用[M].北京:机械工业出版社,1996.1-74
[17]史佩栋.深基坑工程特殊技术问题[M].人民交通出版社,2004
[18]飞思科技产品研发中心编著. MATLAB 6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003
[19]丛爽编著.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科学技术大学出版社,1998
[20]袁金荣.地下工程施工变形的智能预测与控制.博士学位论文.同济大学.2001
[21]韩红霞,窦远明,王喜燕,崔武文.基坑支护中水土压力合算与分算的综合分析.河北工业大学学报,2002,31(6):62-65
[22]Donald J. Berger, and Susan O. Tryon. APPROACH TO DESIGNING STRUCTURAL SLURRY WALLS. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING,1999:1011-1022
[23]彭林欣.基坑开挖地下连续墙有限元分析系统.硕士学位论文.中山大学.2002
[24]蔡美峰.岩石力学与工程.北京:科学出版社,2002.284-301
[25]Mohamed Emam Abd El-Razek1.NEW METHOD FOR CONSTRUCTION OF DIAPHRAGM WALLS. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT,1999 : 233-241
[26]杨光华.深基坑支护结构的实用计算方法及其应用.岩石力学.2004(12):1885-1896
[27]Charles W.W.ng,member, ASCE, Ryan W.M.Yan. Stress Transfer and Deformation Mechanisms around A Diaphragm Wall Panel. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT,1998 : 638-648
[28]S.Rajasekaran, M.F.Febin and J.V.Ramasamy. Artificial Fussy Networks in Civil Engineering. COMPUTERS $ STUCTURES,1996,61(2):291-302
[29]IN-Mo lee, Jeong-Hark Lee. Prediction of Pile Bearing Capacity using Artificial Neural Networks. COMPUTERS $ GEOTECHNICS,1996,18(3):291-302
[30]M.Y.Rafig, G.Bugmann, D.J. Easterbrook. Neural network Design for Engineering Applications. COMPUTERS $ STUCTURES,2001,61:1541-1552
[31]C.Y. Kim, G.J. Bae, S.W. Hong, C.H. Park,H.K. Moon, H.S. Shin. Neural network based prediction of ground surface settlements due to tunneling. COMPUTERS $ STUCTURES,2001 (28) :517–547
[32]娄奕红,俞三溥,王秉勇.基坑支护结构内力及变形动态分析.岩石力学与工程学报,22(3):462-466
[33]高浪,谢康和.人工神经网络在岩土工程中的应用.土木工程学报,2002,35(4):77-81
[34]陈昌彦,王思敬,沈小克.边坡岩体稳定性的人工神经网络预测模型.岩土工程学报,2001,23(2):157-161
[35]李庆来.人工神经网络与概率方法在深基坑开挖信息施工中的应用.土木工程学报,2001,34(2):96-100
[36]周瑞忠,邱高翔.基于BP网络的深基坑支护位移反分析.土木工程学报,2001,34(6):60-62
[37]高沛峻,邓安福,李键.应用BP网络预测内撑式人工挖孔桩支护结构的水平变形.地下空间,2000,20(3):161-165
[38]陈灿寿,张尚根,余有山.深基坑支护结构变形计算.岩石力学与工程学报,2004,23(12):2065-2068
[39]刘巍,刘兴远,陆新.人工神经网络在强夯工程中的运用分析讨论.地下空间,2004,24(2):244-249
[40]郑永保,陈建功,董占文.应用径向基神经网络预测单桩极限承载力.低下空间,2004,24(4):519-521
[41]姜志强,孙树林,李磊.基坑开挖中土压力计算模型探讨.河海大学学报(自然科学版),2003,31(3):303-306
[42]王占生,张克绪.土一结构相互作用的修正刚度法及其在基坑工程中的应用.岩土工程学报. 2002, 24(5):652-655
[43]葛树高.信息化施工是岩土地下工程进步的关键.岩土工程界,1998(4):31~32
[44] Donald J. Berger, P.E., and Susan O. Tryon, P.E. APPROACH TO DESIGNING STRUCTURAL SLURRY WALLS. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING. 1999.9:1011-1022
[45]袁曾任,人工神经网络及其应用,北京:清华大学出版社,1999.66-274
[46]黄宗益,李兴华.信息化施工概述.建筑机械,2005:64-66
[47]高广运.黄诚.孙雨明.遗传BP神经网络在深基坑开挖监测中的应用.地下空间. 2002(4):288~293
[48] T. Kerh, Y.C. Yee. Analysis of a deformed three-dimensional culvert structure using neural networks. Advances in Engineering Software , 2000 (31) : 367~375
[49]蔡兴国,安天瑜,周苏荃. Grey series time—delay predicting model in state estimation for power distribution networks. Journal of Harbin Institute of Technology(哈尔滨工业大学学报:英文版).2003,10(2):120-123
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