Intelligent feedback analysis on a deep excavation for the gravity anchorage foundation of a super suspension bridge
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摘要
In order to ensure the construction safety of the 38.5 m deep excavation for the gravity anchorage foundation of Fuma Yangtze River Bridge, an intelligent feedback analysis was applied to this excavation project. First, a three-dimensional numerical model that simulating the construction process of the excavation was built,and the deformations of the supporting structures were calculated by the finite difference program FLAC3 D. Then,the non-linear mapping relationship between the geomechanical parameters and the excavation-induced displacements was established by the back-propagation neural network(BPNN). Last,the geomechanical parameters were optimized intelligently by the genetic algorithm(GA) based on the developed BPNN model and the measured displacements,and the deformations during the subsequent excavation stages were predicted based on the back-calculated parameters. The research results showed that:the back-calculated values of E1,μ1,c1,and φ1 of the completely weathered stratum,and E2 of the heavily weathered stratum were greater than the initial values,while the inversion value of E3 of the moderately weathered stratum was smaller than the initial value. The magnitudes and the variation tendencies of the predicted displacements were in good accordance with the measured displacements. At the end of the excavation,the retaining piles and the top beams had a maximum displacement of 15–20 mm,exhibiting a quite small magnitude as comparing with other case histories. Local concentration of shear stress mainly occurred at the soil-pile interface and at the toe of the excavation slope,and the plastic zones mainly appeared in the completely weathered stratum. After the completion of the excavation,there were no yielding elements in the model,and the convergence of the numerical computation was achieved,indicating the excavation was in a stable state. This study lays the basis for the subsequent construction and operation of the bridge,and offers a significant reference for the feedback analysis of similar anchorage excavation projects.
In order to ensure the construction safety of the 38.5 m deep excavation for the gravity anchorage foundation of Fuma Yangtze River Bridge, an intelligent feedback analysis was applied to this excavation project. First, a three-dimensional numerical model that simulating the construction process of the excavation was built,and the deformations of the supporting structures were calculated by the finite difference program FLAC3 D. Then,the non-linear mapping relationship between the geomechanical parameters and the excavation-induced displacements was established by the back-propagation neural network(BPNN). Last,the geomechanical parameters were optimized intelligently by the genetic algorithm(GA) based on the developed BPNN model and the measured displacements,and the deformations during the subsequent excavation stages were predicted based on the back-calculated parameters. The research results showed that:the back-calculated values of E1,μ1,c1,and φ1 of the completely weathered stratum,and E2 of the heavily weathered stratum were greater than the initial values,while the inversion value of E3 of the moderately weathered stratum was smaller than the initial value. The magnitudes and the variation tendencies of the predicted displacements were in good accordance with the measured displacements. At the end of the excavation,the retaining piles and the top beams had a maximum displacement of 15–20 mm,exhibiting a quite small magnitude as comparing with other case histories. Local concentration of shear stress mainly occurred at the soil-pile interface and at the toe of the excavation slope,and the plastic zones mainly appeared in the completely weathered stratum. After the completion of the excavation,there were no yielding elements in the model,and the convergence of the numerical computation was achieved,indicating the excavation was in a stable state. This study lays the basis for the subsequent construction and operation of the bridge,and offers a significant reference for the feedback analysis of similar anchorage excavation projects.
In order to ensure the construction safety of the 38.5 m deep excavation for the gravity anchorage foundation of Fuma Yangtze River Bridge, an intelligent feedback analysis was applied to this excavation project. First, a three-dimensional numerical model that simulating the construction process of the excavation was built,and the deformations of the supporting structures were calculated by the finite difference program FLAC3 D. Then,the non-linear mapping relationship between the geomechanical parameters and the excavation-induced displacements was established by the back-propagation neural network(BPNN). Last,the geomechanical parameters were optimized intelligently by the genetic algorithm(GA) based on the developed BPNN model and the measured displacements,and the deformations during the subsequent excavation stages were predicted based on the back-calculated parameters. The research results showed that:the back-calculated values of E1,μ1,c1,and φ1 of the completely weathered stratum,and E2 of the heavily weathered stratum were greater than the initial values,while the inversion value of E3 of the moderately weathered stratum was smaller than the initial value. The magnitudes and the variation tendencies of the predicted displacements were in good accordance with the measured displacements. At the end of the excavation,the retaining piles and the top beams had a maximum displacement of 15–20 mm,exhibiting a quite small magnitude as comparing with other case histories. Local concentration of shear stress mainly occurred at the soil-pile interface and at the toe of the excavation slope,and the plastic zones mainly appeared in the completely weathered stratum. After the completion of the excavation,there were no yielding elements in the model,and the convergence of the numerical computation was achieved,indicating the excavation was in a stable state. This study lays the basis for the subsequent construction and operation of the bridge,and offers a significant reference for the feedback analysis of similar anchorage excavation projects.
引文
[1]WANG H,LI A Q,LI J.Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements[J].Engineering Structures,2010,32(9):2 546-2 556.
[2]HOU Q,LI S M.Transport infrastructure development and changing spatial accessibility in the Greater Pearl River Delta,China,1990-2020[J].Journal of Transport Geography,2011,19(6):1 350-1 360.
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[11]SAKURAI S,TAKEUCHI K.Back analysis of measured displacements of tunnels[J].Rock Mechanics and Rock Engineering,1983,16(3):173-180.
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[14]ERING P,BABU G L S.Probabilistic back analysis of rainfall induced landslide-A case study of Malin landslide,India[J].Engineering Geology,2016,208:154-164.
[15]RECHEA C,LEVASSEUR S,FINNO R.Inverse analysis techniques for parameter identification in simulation of excavation support systems[J].Computers and Geotechnics,2008,35(3):331-345.
[16]TANG Y G,KUNG G T C.Application of nonlinear optimization technique to back analyses of deep excavation[J].Computers and Geotechnics,2009,36(1/2):276-290.
[17]MIRANDA T,DIAS D,ECLAIRCY-CAUDRON S,et al.Back analysis of geomechanical parameters by optimisation of a 3D model of an underground structure[J].Tunnelling and Underground Space Technology,2011,26(6):659-673.
[18]FENG X T,ZHAO H B,LI S J.A new displacement back analysis to identify mechanical geo-material parameters based on hybrid intelligent methodology[J].International Journal for Numerical and Analytical Methods in Geomechanics,2004,28(11):1 141-1 165.
[19]FENG X T,CHEN B R,YANG C,et al.Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm[J].International Journal of Rock Mechanics and Mining Sciences,2006,43(5):789-801.
[20]FENG X T,ZHANG Z Q,SHENG Q.Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method[J].International Journal of Rock Mechanics and Mining Sciences,2000,37(7):1 039-1 054.
[21]DENG J H,LEE C F.Displacement back analysis for a steep slope at the Three Gorges Project site[J].International Journal of Rock Mechanics and Mining Sciences,2001,38(2):259-268.
[22]SHANG Y J,CAI J G,HAO W D,et al.Intelligent back analysis of displacements using precedent type analysis for tunneling[J].Tunnelling and Underground Space Technology,2002,17(4):381-389.
[23]JIANG A N,WANG S Y,TANG S L.Feedback analysis of tunnel construction using a hybrid arithmetic based on Support Vector Machine and Particle Swarm Optimisation[J].Automation in Construction,2011,20(4):482-489.
[24]YU Y Z,ZHANG B Y,YUAN H N.An intelligent displacement back-analysis method for earth-rockfill dams[J].Computers and Geotechnics,2007,34(6):423-434.
[25]MANOUCHEHRIAN A,SHARIFZADEH M,MOGHADAM R H.Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics[J].International Journal of Mining Science and Technology,2012,22(2):229-236.
[26]TIRYAKI S,AYDIN A.An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model[J].Construction and Building Materials,2014,62:102-108.
[27]SHARMA L K,VISHAL V,SINGH T N.Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties[J].Measurement,2017,102:158-169.
[28]HOLLAND J H.Adaptation in natural and artificial systems:an introductory analysis with applications to biology,control,and artificial intelligence[M].Ann Arbor,MI:University of Michigan Press,1992:1-211.
[29]HOLLAND J H,GOLDBERG D E.Genetic algorithms in search,optimization and machine learning[J].Reading,MA:AddisonWesley,1989.
[30]DAVIS L.Handbook of genetic algorithms[M].New York:Van Nostrand Reinhold,1991:1-385.
[31]LONG M.Database for retaining wall and ground movements due to deep excavations[J].Journal of Geotechnical and Geoenvironmental Engineering,2001,127(3):203-224.
[32]LEUNG E H Y,NG C W W.Wall and ground movements associated with deep excavations supported by cast in situ wall in mixed ground conditions[J].Journal of Geotechnical and Geoenvironmental Engineering,2007,133(2):129-143.
[33]LIU G B,JIANG R J,NG C W W,et al.Deformation characteristics of a 38 m deep excavation in soft clay[J].Canadian Geotechnical Journal,2011,48(12):1 817-1 828.
[34]TAN Y,LI M W.Measured performance of a 26 m deep top-down excavation in downtown Shanghai[J].Canadian Geotechnical Journal,2011,48(5):704-719.
[2]HOU Q,LI S M.Transport infrastructure development and changing spatial accessibility in the Greater Pearl River Delta,China,1990-2020[J].Journal of Transport Geography,2011,19(6):1 350-1 360.
[3]MENG L B,LI T B,JIANG Y,et al.Characteristics and mechanisms of large deformation in the Zhegu mountain tunnel on the SichuanTibet highway[J].Tunnelling and Underground Space Technology,2013,37:157-164.
[4]HU N,DAI G L,YAN B,et al.Recent development of design and construction of medium and long span high-speed railway bridges in China[J].Engineering Structures,2014,74:233-241.
[5]AL-HOMOUD A S,TUBEILEH T K.Analysis and remedies of landslides of cut slopes due to the presence of weak cohesive layers within stronger formations[J].Environmental Geology,1998,33(4):299-311.
[6]STARK T D,ARELLANO W D,HILLMAN R P,et al.Effect of toe excavation on a deep bedrock landslide[J].Journal of Performance of Constructed Facilities,2005,19(3):244-255.
[7]SINGH T N,GULATI A,DONTHA L,et al.Evaluating cut slope failure by numerical analysis-a case study[J].Natural Hazards,2008,47(2):263-279.
[8]CHEN S H,CHEN S F,SHAHROUR I,et al.The feedback analysis of excavated rock slope[J].Rock Mechanics and Rock Engineering,2001,34(1):39-56.
[9]SHAO Y,MACARI E J.Information feedback analysis in deep excavations[J].International Journal of Geomechanics,2008,8(1):91-103.
[10]SHARIFZADEH M,DARAEI R,BROOJERDI M S.Design of sequential excavation tunneling in weak rocks through findings obtained from displacements based back analysis[J].Tunnelling and Underground Space Technology,2012,28:10-17.
[11]SAKURAI S,TAKEUCHI K.Back analysis of measured displacements of tunnels[J].Rock Mechanics and Rock Engineering,1983,16(3):173-180.
[12]YANG Z F,LEE C F,WANG S J.Three-dimensional back-analysis of displacements in exploration adits-principles and application[J].International Journal of Rock Mechanics and Mining Sciences,2000,37(3):525-533.
[13]WANG L,HWANG J H,LUO Z,et al.Probabilistic back analysis of slope failure-A case study in Taiwan[J].Computers and Geotechnics,2013,51:12-23.
[14]ERING P,BABU G L S.Probabilistic back analysis of rainfall induced landslide-A case study of Malin landslide,India[J].Engineering Geology,2016,208:154-164.
[15]RECHEA C,LEVASSEUR S,FINNO R.Inverse analysis techniques for parameter identification in simulation of excavation support systems[J].Computers and Geotechnics,2008,35(3):331-345.
[16]TANG Y G,KUNG G T C.Application of nonlinear optimization technique to back analyses of deep excavation[J].Computers and Geotechnics,2009,36(1/2):276-290.
[17]MIRANDA T,DIAS D,ECLAIRCY-CAUDRON S,et al.Back analysis of geomechanical parameters by optimisation of a 3D model of an underground structure[J].Tunnelling and Underground Space Technology,2011,26(6):659-673.
[18]FENG X T,ZHAO H B,LI S J.A new displacement back analysis to identify mechanical geo-material parameters based on hybrid intelligent methodology[J].International Journal for Numerical and Analytical Methods in Geomechanics,2004,28(11):1 141-1 165.
[19]FENG X T,CHEN B R,YANG C,et al.Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm[J].International Journal of Rock Mechanics and Mining Sciences,2006,43(5):789-801.
[20]FENG X T,ZHANG Z Q,SHENG Q.Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method[J].International Journal of Rock Mechanics and Mining Sciences,2000,37(7):1 039-1 054.
[21]DENG J H,LEE C F.Displacement back analysis for a steep slope at the Three Gorges Project site[J].International Journal of Rock Mechanics and Mining Sciences,2001,38(2):259-268.
[22]SHANG Y J,CAI J G,HAO W D,et al.Intelligent back analysis of displacements using precedent type analysis for tunneling[J].Tunnelling and Underground Space Technology,2002,17(4):381-389.
[23]JIANG A N,WANG S Y,TANG S L.Feedback analysis of tunnel construction using a hybrid arithmetic based on Support Vector Machine and Particle Swarm Optimisation[J].Automation in Construction,2011,20(4):482-489.
[24]YU Y Z,ZHANG B Y,YUAN H N.An intelligent displacement back-analysis method for earth-rockfill dams[J].Computers and Geotechnics,2007,34(6):423-434.
[25]MANOUCHEHRIAN A,SHARIFZADEH M,MOGHADAM R H.Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics[J].International Journal of Mining Science and Technology,2012,22(2):229-236.
[26]TIRYAKI S,AYDIN A.An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model[J].Construction and Building Materials,2014,62:102-108.
[27]SHARMA L K,VISHAL V,SINGH T N.Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties[J].Measurement,2017,102:158-169.
[28]HOLLAND J H.Adaptation in natural and artificial systems:an introductory analysis with applications to biology,control,and artificial intelligence[M].Ann Arbor,MI:University of Michigan Press,1992:1-211.
[29]HOLLAND J H,GOLDBERG D E.Genetic algorithms in search,optimization and machine learning[J].Reading,MA:AddisonWesley,1989.
[30]DAVIS L.Handbook of genetic algorithms[M].New York:Van Nostrand Reinhold,1991:1-385.
[31]LONG M.Database for retaining wall and ground movements due to deep excavations[J].Journal of Geotechnical and Geoenvironmental Engineering,2001,127(3):203-224.
[32]LEUNG E H Y,NG C W W.Wall and ground movements associated with deep excavations supported by cast in situ wall in mixed ground conditions[J].Journal of Geotechnical and Geoenvironmental Engineering,2007,133(2):129-143.
[33]LIU G B,JIANG R J,NG C W W,et al.Deformation characteristics of a 38 m deep excavation in soft clay[J].Canadian Geotechnical Journal,2011,48(12):1 817-1 828.
[34]TAN Y,LI M W.Measured performance of a 26 m deep top-down excavation in downtown Shanghai[J].Canadian Geotechnical Journal,2011,48(5):704-719.