Single-and multi-objective genetic algorithm optimization for identifying soil parameters

SUMMARY This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for which two constitutive models—a linear elastic perfectly plastic Mohr–Coulomb model and a strain‐hardening elasto‐plastic model—are succ...

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Bibliographic Details
Published in:International journal for numerical and analytical methods in geomechanics Vol. 36; no. 5; pp. 597 - 618
Main Authors: Papon, A., Riou, Y., Dano, C., Hicher, P.-Y.
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 10.04.2012
Wiley
Subjects:
Applied sciences
Buildings. Public works
Civil Engineering
Computation methods. Tables. Charts
Engineering Sciences
Exact sciences and technology
genetic algorithms
Geotechnics
Géotechnique
inverse analysis
multi-objective algorithm
Soil investigations. Testing
soil parameter identification
Structural analysis. Stresses
Footing
Multiobjective programming
Property of soil
Identification
genetic algorithms
Forecast model
Modeling
Optimization
Inverse problem
Experimental result
Genetic algorithm
Soil test
Formulation
Parameter
inverse analysis
multi-objective algorithm
Comparative study
soil parameter identification
ISSN:0363-9061, 1096-9853
Online Access:Get full text
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Summary:SUMMARY This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for which two constitutive models—a linear elastic perfectly plastic Mohr–Coulomb model and a strain‐hardening elasto‐plastic model—are successively considered. Two kinds of optimization algorithms have been used: a deterministic simplex method and a stochastic genetic method. The soil data come from the results of two pressuremeter tests, complemented by triaxial and resonant column testing. First, the inverse analysis has been performed separately on each pressuremeter test. The genetic method presents the advantage of providing a collection of satisfactory solutions, among which a geotechnical engineer has to choose the optimal one based on his scientific background and/or additional analyses based on further experimental test results. This advantage is enhanced when all the constitutive parameters sensitive to the considered problem have to be identified without restrictions in the search space. Second, the experimental values of the two pressuremeter tests have been processed simultaneously, so that the inverse analysis becomes a multi‐objective optimization problem. The genetic method allows the user to choose the most suitable parameter set according to the Pareto frontier and to guarantee the coherence between the tests. The sets of optimized parameters obtained from inverse analyses are then used to calculate the response of a spread footing, which is part of a predictive benchmark. The numerical results with respect to both the constitutive models and the inverse analysis procedure are discussed. Copyright © 2011 John Wiley & Sons, Ltd.
Bibliography:ark:/67375/WNG-3M3J2LSW-X
istex:87F5E371A63AD0483536E2E93B3D960D97F58E4E
ArticleID:NAG1019
ISSN:0363-9061
1096-9853
DOI:10.1002/nag.1019