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Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

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Abstract

The hybrid genetic algorithm is utilized to facilitate model parameter estimation. The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil. In order to save computing time during parameter inversion, a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method (FEM). The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm. The numerical and experimental results for conditioned soil are compared. The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones. The effectiveness and accuracy of proposed parameter estimation approach are validated.

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References

  1. CHAPARRO B M, THUILLIER S, MENEZES L F, MANACH P Y. Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms [J]. Computational Materials Science, 2008, 44(2): 339–346.

    Article  Google Scholar 

  2. RECHEA C, LEVASSEUR S, FINNO R. Inverse analysis techniques for parameter identification in simulation of excavation support systems [J]. Computers and Geotechnics, 2008, 35(3): 331–345.

    Article  Google Scholar 

  3. LIU G R, LEE J H, PATERA A T. Inverse identification of thermal parameters using reduced-basis method [J]. Comput Methods Appl Mech Engrg, 2005, 194(27/28/29): 3090–3107.

    Article  MATH  Google Scholar 

  4. HE R S, HWANG S F. Damage detection by an adaptive real-parameter simulated annealing genetic algorithm [J]. Computers and Structures, 2006, 84(31/32): 2231–2243.

    Article  Google Scholar 

  5. NYARKO E K, SCITOVSKI R. Solving the parameter identification problem of mathematical models using genetic algorithms [J]. Applied Mathematics and Computation, 2004, 153(3): 651–658.

    Article  MathSciNet  MATH  Google Scholar 

  6. WEI D C. An improved real-coded genetic algorithm for parameters estimation of nonlinear systems [J]. Mechanical Systems and Signal Processing, 2006, 20(1): 236–246.

    Article  Google Scholar 

  7. KHALIK M A, SHERIF M, SARAYA S. Parameter identification problem: Real-coded GA approach [J]. Applied Mathematics and Computation, 2007, 187(2): 1495–1501.

    Article  MATH  Google Scholar 

  8. GALDI V, IPPOLITO L, PICCOLO A, VACCARO A. Parameter identification of power transformers thermal model via genetic algorithms [J]. Electric Power Systems Research, 2001, 60(2): 107–113.

    Article  Google Scholar 

  9. TUTKUN N. Parameter estimation in mathematical models using the real coded genetic algorithms [J]. Expert Systems with Applications, 2009, 36(2): 3342–3345.

    Article  Google Scholar 

  10. LI S J, LIU Y X. Identification of vibration loads on hydro generator by using hybrid genetic algorithm [J]. Acta Mechanica Sinica, 2006, 22(6): 603–610.

    Article  MATH  Google Scholar 

  11. PERRY M J, KOH C G, CHOO Y S. Modified genetic algorithm strategy for structural identification [J]. Computers and Structures, 2006, 84(8/9): 529–540.

    Article  Google Scholar 

  12. HWANG S F, WU J C, HE R S. Identification of effective elastic constants of composite plates based on a hybrid genetic algorithm [J]. Composite Structures, 2009, 90(2): 217–224.

    Article  Google Scholar 

  13. HWANG S F, HE R S. Improving real-parameter genetic algorithm with simulated annealing for engineering problems [J]. Advances in Engineering Software, 2006, 37(6): 406–418.

    Article  Google Scholar 

  14. YEH W W. Review of parameter identification procedures in groundwater hydrology: The inverse problem [J]. Water Resour Res, 1986, 22(2): 95–108.

    Article  Google Scholar 

  15. FRANULOVIC M, BASAN R, PREBI I. Genetic algorithm in material model parameters identification for low-cycle fatigue [J]. Computational Materials Science, 2009, 45(2): 505–510.

    Article  Google Scholar 

  16. CHANG W D. Nonlinear system identification and control using a real-coded genetic algorithm [J]. Applied Mathematical Modelling, 2007, 31(3): 541–550.

    Article  MATH  Google Scholar 

  17. CHANG W D. Coefficient estimation of IIR filter by a multiple crossover genetic algorithm [J]. Computers and Mathematics with Applications, 2006, 51(9/10): 1437–1444.

    Article  MathSciNet  MATH  Google Scholar 

  18. XU T, ZUO W J, XU T S. An adaptive reanalysis method for genetic algorithm with application to fast truss optimization [J]. Acta Mechanica Sinica, 2010, 26(2): 225–234.

    Article  MathSciNet  Google Scholar 

  19. REN Z W, SAN Y, CHEN J F. Hybrid simplex-improved genetic algorithm for global numerical optimization [J]. Acta Mechanica Sinica, 2007, 23(1): 91–95.

    Article  MathSciNet  Google Scholar 

  20. LI S J, LIU Y X. Inverse determination of model parameters of nonlinear heat conduction problem using hybrid genetic algorithm [J]. Nonlinear Dynamics and Systems Theory, 2007, 7(1): 23–34.

    MathSciNet  MATH  Google Scholar 

  21. KONDNER R L. Hyperbolic stress-strain response: cohesive soil [J]. Journal of the Soil Mechanics and Foundations Division, 1963, 89(SM1): 115–143.

    Google Scholar 

  22. DUNCAN J M, CHANG C. Nonlinear analysis of stress and strain in soils [J]. Journal of the Soil Mechanics and Foundations Division, 1970, 96(SM5): 1629–1653.

    Google Scholar 

  23. SHANGGUAN Z C, LI S J, LUAN M T. Estimating model parameters of conditioned soils by using artificial network [J]. Journal of Software, 2010, 5(3): 296–302.

    Google Scholar 

  24. YU H, LI S J, DUAN H X, LIU Y X. A procedure of parameter inversion for a nonlinear constitutive model of soils with shield tunneling [J]. Computers and Mathematics with Application, 2011, 61(8): 2005–2009.

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China

    Shou-ju Li  (李守巨), Long-tan Shao  (邵龙潭) & Ying-xi Liu  (刘迎曦)

  2. The Second Affiliated Hospital of Dalian Medical University, Dalian, 116027, China

    Ji-zhe Wang  (王吉喆)

Authors
  1. Shou-ju Li  (李守巨)
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  2. Long-tan Shao  (邵龙潭)
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  3. Ji-zhe Wang  (王吉喆)
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  4. Ying-xi Liu  (刘迎曦)
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Corresponding author

Correspondence to Shou-ju Li  (李守巨).

Additional information

Foundation item: Project(2007CB714006) supported by the National Basic Research Program of China; Project(90815023) supported by the National Natural Science Foundation of China

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Li, Sj., Shao, Lt., Wang, Jz. et al. Inverse procedure for determining model parameter of soils using real-coded genetic algorithm. J. Cent. South Univ. Technol. 19, 1764–1770 (2012). https://doi.org/10.1007/s11771-012-1203-2

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  • DOI: https://doi.org/10.1007/s11771-012-1203-2

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