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Identifying parameters of soils and selection of constitutive models using genetic algorithms

Abstract : The subject of this thesis is the identification of soil parameters and the selection of constitutive models using genetic algorithms. First, various optimization methods for identifying soil parameters are studied. Then, a real - coded genetic algorithm (RCGA) has been developed to improve the performance of genetic algorithms (GA) for identifying soil parameters. Subsequently, the RCG A is employed to construct a formula for predicting the compressibility of remolded clays by using an evolutionary polynomial regression ( EPR ) based on the initial void ratio e 0 , the liquid limit w L and the plastic index I P . Then, an efficient procedure fo r identifying the necessary parameters of soft structured clay s is propose d by employing the enhanced RCGA coupled with an advanced anisotropic elasto - viscoplastic model. This approach is then validated and several applications are developed to demonstrate that the procedure can be used with a reduction of the testing cost . F inally , an appropriate model of sand with the necessary features based on conventional tests and with an easy way of identifying parameters for geotechnical applications by employ ing th e RCGA and different sand models is selected. A discussion on nonlinear plastic stress - strain hardening , the incorporation of the critical state concept with interlocking effect , test types and numbers , and necessary strain level for the selection and use of sand models concludes the thesis.
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Submitted on : Thursday, November 5, 2020 - 5:59:10 PM
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  • HAL Id : tel-02990969, version 1



Yinfu Jin. Identifying parameters of soils and selection of constitutive models using genetic algorithms. Géotechnique. École centrale de Nantes, 2016. English. ⟨NNT : 2016ECDN0017⟩. ⟨tel-02990969⟩



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