Identification of constitutive parameters of soil using an optimization strategy and statistical analysis
The paper at hand investigates a strategy to calibrate different constitutive models for soils via back analysis. The efficiency and reliability of the parameter identification for soil models is worked out. In order to demonstrate of how to utilise identification procedures, results from optimisation against conventional oedometer and drained triaxial compression tests on natural Pappadai clay are presented and discussed. The aim of geotechnical optimisation problems is to obtain a set of model parameter values that provide the best match between soil model simulations and appropriate measurements. For the parameter identification process, a constrained population-based algorithm is chosen, namely the Particle Swarm Optimiser. The identification is carried out in an initial step separately on each test and then simultaneously on oedometer and triaxial tests. The evaluation is performed employing three different constitutive models of varying complexity and number of constitutive parameters. A subsequent residual analysis and the computation of confidence intervals for the parameters provide valuable results to assess the quality of the identified parameters in correlation with the evaluated data. Therefore, criteria of the utility and reliability of the mathematical models for further prognosis computations can be estimated.
parameter identification clay constitutive soil models particle swarm optimization sensitivity analysis assessing model fit