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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| Published in: | International journal for numerical and analytical methods in geomechanics Vol. 36; no. 5; pp. 597 - 618 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Chichester, UK
John Wiley & Sons, Ltd
10.04.2012
Wiley |
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| ISSN: | 0363-9061, 1096-9853 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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. |
| Author | Hicher, P.-Y. Riou, Y. Papon, A. Dano, C. |
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| Keywords | 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 |
| Language | English |
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International Journal for Numerical and Analytical Methods in Geomechanics 2010; 34:471-491. 2010; 34 1996; 18 2006; 31 1990; 57 1991; 15 1996; 19 2005; 131 1984; 24 1993; 63 2009 1975 1997 1897 2006; 5 2008; 35 1995 1970; 96 2008; 32 1994 2005 1996; 122 2004 2001; 28 1992 2002 2009; 33 1967; 7 2004; 31 2002; 26 2001 2000; 225 1965; 7 1994; 120 2005; 5 1985 2003; 27 1994; 13 1989 1988 e_1_2_6_31_2 e_1_2_6_30_2 Biarez J (e_1_2_6_39_2) 1994 Dano C (e_1_2_6_37_2) 2002 Goldberg DE (e_1_2_6_29_2) 1989 Mestat P (e_1_2_6_5_2) 2000; 225 Holland JH (e_1_2_6_28_2) 1975 Gioda G (e_1_2_6_6_2) 1985 Renders JM (e_1_2_6_43_2) 1995 Briaud JL (e_1_2_6_12_2) 1994 e_1_2_6_18_2 e_1_2_6_19_2 e_1_2_6_35_2 e_1_2_6_13_2 e_1_2_6_34_2 e_1_2_6_10_2 e_1_2_6_11_2 e_1_2_6_16_2 e_1_2_6_17_2 e_1_2_6_38_2 e_1_2_6_36_2 Duncan JM (e_1_2_6_2_2) 1970; 96 e_1_2_6_42_2 e_1_2_6_20_2 e_1_2_6_41_2 e_1_2_6_40_2 Lade PV (e_1_2_6_3_2) 1988 Deb K (e_1_2_6_14_2) 2001 e_1_2_6_8_2 e_1_2_6_7_2 Poles S (e_1_2_6_32_2) 2004 e_1_2_6_9_2 e_1_2_6_4_2 e_1_2_6_24_2 Hicher PY (e_1_2_6_25_2) 1994; 13 e_1_2_6_23_2 e_1_2_6_22_2 e_1_2_6_21_2 Pareto V (e_1_2_6_15_2) 1897 e_1_2_6_27_2 e_1_2_6_26_2 Poloni C (e_1_2_6_33_2) 1997 |
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This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation... This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for... This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM-simulation for... |
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| SubjectTerms | 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 |
| Title | Single-and multi-objective genetic algorithm optimization for identifying soil parameters |
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