Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations

Precedent and observation of performance are an essential part of the design and construction process in geotechnical engineering. For deep urban excavations designers rely on empirical data to estimate potential deformations and impact on surrounding structures. Numerical simulations are also employed to estimate induced ground deformations. Significant resources are dedicated to monitor construction activities and control induced ground deformations. While engineers are able to learn from observations, numerical simulations have been unable to fully benefit from information gained at a given site or prior excavation case histories in the same area. A novel analysis method, self-learning in engineering simulations (SelfSim), is introduced to integrate precedent into numerical simulations. SelfSim is an inverse analysis technique that combines finite element method, biologically inspired material models, and field measurements. SelfSim extracts relevant constitutive soil information from field measurements of excavation response such as lateral wall deformations and surface settlement. The resulting soil model, used in a numerical analysis, provides correct ground deformations and can be used in estimating deformations of similar excavations. The soil model can continuously evolve using additional field information. SelfSim is demonstrated using two excavation case histories in Boston and Chicago.

[1]  K. Terzaghi,et al.  Soil mechanics in engineering practice , 1948 .

[2]  H M Horn,et al.  SETTLEMENT OF BUILDINGS ON THE MIT CAMPUS , 1964 .

[3]  E. Barghoorn,et al.  Late Quaternary Sea-Level Change and Crustal Rise at Boston, Massachusetts, with Notes on the Autocompaction of Peat , 1964 .

[4]  Ralph B. Peck,et al.  Advantages and Limitations of the Observational Method in Applied Soil Mechanics , 1969 .

[5]  R. Peck.  Deep excavations and tunnelling in soft ground , 1969 .

[6]  Richard B. Bovee,et al.  Consolidated Undrained Plane Strain Shear Tests on Boston Blue Clay. , 1971 .

[7]  G. Wayne Clough,et al.  PREDICTION OF MOVEMENTS FOR BRACED CUTS IN CLAY , 1981 .

[8]  David M. Potts,et al.  The behaviour of a propped retaining wall: results of a numerical experiment , 1984 .

[9]  H. D. Hibbitt,et al.  ABAQUS-EPGEN: a general-purpose finite-element code. Volume 4. Systems manual , 1985 .

[10]  A. Fourie,et al.  Studies of the influence of non-linear stress-strain characteristics in soil-structure interaction , 1986 .

[11]  Giancarlo Gioda,et al.  Back analysis procedures for the interpretation of field measurements in geomechanics , 1987 .

[12]  G. Wayne Clough,et al.  Construction Induced Movements of Insitu Walls , 1990 .

[13]  Richard J. Finno,et al.  Finite Element Analyses of HDR‐4 Excavation , 1991 .

[14]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[15]  Youssef M A Hashash,et al.  Analysis of deep excavations in clay , 1992 .

[16]  Xiping Wu.  Neural network-based material modeling , 1992 .

[17]  John C. Small,et al.  APPLICATION OF A FULLY COUPLED METHOD TO THE ANALYSIS OF AN EXCAVATION , 1993 .

[18]  Andrew J. Whittle,et al.  Analysis of Deep Excavation in Boston , 1993 .

[19]  Andrew J. Whittle,et al.  Formulation of MIT-E3 constitutive model for overconsolidated clays , 1994 .

[20]  Chang-Yu Ou,et al.  Soil parameter determination for deep excavation analysis by optimization , 1994 .

[21]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[22]  Andrew J. Whittle,et al.  Ground Movement Prediction for Deep Excavations in Soft Clay , 1996 .

[23]  Anthony T. C. Goh,et al.  Pile Driving Records Reanalyzed Using Neural Networks , 1996 .

[24]  Mingfu Michael Zhang.  Neural Network Material Models Determined From Structural Tests , 1997 .

[25]  Jamshid Ghaboussi,et al.  New nested adaptive neural networks (NANN) for constitutive modeling , 1998 .

[26]  Jamshid Ghaboussi,et al.  Constitutive modeling of geomaterials from non-uniform material tests , 1998 .

[27]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[28]  M. A. A. Kiefa.  GENERAL REGRESSION NEURAL NETWORKS FOR DRIVEN PILES IN COHESIONLESS SOILS , 1998 .

[29]  Robert J. Marks,et al.  Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks , 1999 .

[30]  G. N. Pande,et al.  On self-learning finite element codes based on monitored response of structures , 2000 .

[31]  Matthew Blake Olsen,et al.  Measured performance of a large excavation on the MIT campus , 2001 .

[32]  Pierre-Yves Hicher,et al.  IDENTIFICATION OF SOIL PARAMETERS BY INVERSE ANALYSIS , 2001 .

[33]  Jamshid Ghaboussi,et al.  Biologically inspired soft computing methods in structural mechanics and engineering , 2001 .

[34]  Andrew J. Whittle,et al.  Mechanisms of Load Transfer and Arching for Braced Excavations in Clay , 2002 .

[35]  G Pande,et al.  Finite elements with artificial intelligence , 2002 .

[36]  Shih-Lin Hung,et al.  NEURAL NETWORK FORECAST MODEL IN DEEP EXCAVATION , 2002 .

[37]  Youssef M A Hashash,et al.  Systematic update of a deep excavation model using field performance data , 2003 .

[38]  Youssef M A Hashash,et al.  Numerical implementation of a neural network based material model in finite element analysis , 2004 .

[39]  Youssef M A Hashash,et al.  Constitutive model update using observed field behavior , 2004 .

[40]  Richard J. Finno,et al.  Selecting parameters to optimize in model calibration by inverse analysis , 2004 .

[41]  Richard J. Finno,et al.  Three-Dimensional Responses of a Tied-Back Excavation through Clay , 2005 .

[42]  Youssef M A Hashash,et al.  Neural network material model enhancement: Optimization through visualization and selective data removal , 2005 .

[43]  Camilo Marulanda,et al.  Integration of Numerical Modeling and Field Observations of Deep Excavations , 2005 .

[44]  Youssef M A Hashash,et al.  A novel framework for extracting dynamic soil behavior from downhole array data , 2006 .