Identification Of Soil Constitutive Model Parameters By Using Optimization Methods

The subject of this thesis is the identification of soil parameters and the selection of constitutive models using optimization methods.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 RCGA is employed to construct a formula for predicting the compressibility of remolded clays by using an evolutionary polynomial regression(EPR).Then,an efficient procedure for identifying the necessary parameters of soft structured clays is proposed by employing the enhanced RCGA coupled with an advanced anisotropic elasto-viscoplastic model.Finally,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 employing the 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.The concluding remarks of this study are summarized as follows.1)A comparative study was performed for identifying Mohr-Coulomb parameters from a synthetic PMT and excavation.The GA,PSO,SA,DE and ABC were selected to conduct the optimizations.All the comparisons demonstrate that the DE has the strongest search ability with the smallest objective error but on the other hand,it also has the slower convergence speed.2)A new efficient hybrid real-coded genetic algorithm(RCGA)has been developed by adopting two crossovers with outstanding ability.The performance of the proposed RCGA has been validated by optimising six mathematical functions and then further evaluated by identifying soil parameters based on both laboratory tests and field tests,for different soil models.All the comparisons demonstrate that the proposed RCGA has an excellent performance of inverse analysis for identifying soil parameters.3)The evolutionary polynomial regression(EPR)based modeling of clay compressibility using an enhanced hybrid real-coded genetic algorithm has been conducted.The results demonstrate that the EPR-based modeling of clay compressibility using the enhanced RCGA gives a more accurate and reliable correlation between the compression index and the physical properties of remolded clays.4)An efficient optimization method for identifying the parameters of advanced constitutive model for soft structured clays from only limited conventional triaxial tests is proposed.All comparisons demonstrate that a reliable solution can be obtained by the new RCGA optimization combined with an elasto-viscoplastic soil model,which is useful in practice with a reduction in testing costs.The selection of sand models and parameter identification by using the optimization method have been discussed.Four key points are discussed in turn:(1)which features are necessary to be accounted for in constitutive modeling of sand;(2)which type of tests(drained and/or undrained)should be selected for an optimal identification of parameters;(3)what is the minimum number of tests that should be selected for parameter identification;and(4)what is the suitable and the lower strain level of objective tests for obtaining reliable and reasonable parameters.