Single-and multi-objective genetic algorithm optimization for identifying soil parameters
| Autor: | Aurélie Papon, Pierre-Yves Hicher, Christophe Dano, Yvon Riou |
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| Rok vydání: | 2011 |
| Předmět: |
Mathematical optimization
Engineering Optimization problem business.industry 0211 other engineering and technologies Computational Mechanics Pareto principle Inverse 02 engineering and technology 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Pressuremeter test Set (abstract data type) Simplex algorithm Mechanics of Materials Benchmark (computing) Coherence (signal processing) General Materials Science business 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | International Journal for Numerical and Analytical Methods in Geomechanics. 36:597-618 |
| ISSN: | 0363-9061 |
| DOI: | 10.1002/nag.1019 |
| Popis: | 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 modelsa linear elastic perfectly plastic MohrCoulomb model and a strain-hardening elasto-plastic modelare 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. |
| Databáze: | OpenAIRE |
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