Probabilistic analysis of the inverse analysis of an excavation problem

This study presents the probabilistic analysis of the inverse analysis of an excavation problem. Two techniques are used during two successive stages. First, a genetic algorithm inverse analysis is conducted to identify soil parameters from in situ measurements (i.e. first stage of the construction project). For a given tolerable error between the measurement and the response of the numerical model the genetic algorithm is able to generate a statistical set of soil parameters, which may then serve as input data to a stochastic finite element method. The second analysis allows predicting a confidence interval for the final behaviour of the geotechnical structure (i.e. second stage of the project). The tools employed in this study have already been presented in previous papers, but the originality herein consists of coupling them. To illustrate this method, a synthetic excavation problem with a very simple geometry is used.

[1]  Brice Lecampion,et al.  Parameter identification for lined tunnels in a viscoplastic medium , 2002 .

[2]  H. Matthies,et al.  Uncertainties in probabilistic numerical analysis of structures and solids-Stochastic finite elements , 1997 .

[3]  Brice Lecampion,et al.  Sensitivity analysis for parameter identification in quasi‐static poroelasticity , 2005 .

[4]  D. Agarwal,et al.  Computer-aided calibration of a soil plasticity model , 1991 .

[5]  Cédric Chauvière,et al.  An efficient SFE method using Lagrange polynomials: Application to nonlinear mechanical problems with uncertain parameters , 2007 .

[6]  Etienne Flavigny,et al.  Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data , 2009 .

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

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

[9]  Karl Terzaghi,et al.  Mécanique des sols appliquée aux travaux publics et au bâtiment , 1957 .

[10]  Sukhamay Kundu,et al.  Calibration of a constitutive model using genetic algorithms , 1996 .

[11]  A. Tarantola.  Inverse problem theory : methods for data fitting and model parameter estimation , 1987 .

[12]  Richard J. Finno,et al.  Supported Excavations: Observational Method and Inverse Modeling , 2005 .

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

[14]  Antonio Gens,et al.  Estimation of parameters in geotechnical backanalysis — I. Maximum likelihood approach , 1996 .

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

[16]  Marc Boulon,et al.  Soil parameter identification using a genetic algorithm , 2008 .

[17]  Eduardo Alonso,et al.  Estimation of Parameters in Geotechnical Backanalysis - II. Application to a Tunnel Excavation Problem , 1996 .

[18]  D. I. Golenko,et al.  The Monte Carlo Method. , 1967 .

[19]  Julien Baroth,et al.  Probabilistic analysis of a pull-out test , 2010 .

[20]  Marc Boulon,et al.  Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests , 2010 .

[21]  Richard J. Finno,et al.  Inverse analysis techniques for parameter identification in simulation of excavation support systems , 2008 .

[22]  Ulf Wickström,et al.  Comments on calculation of temperature in fire-exposed bare steel structures in prEN 1993-1-2: Eurocode 3—design of steel structures—Part 1–2: general rules—structural fire design , 2005 .