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In this thesis a new evolutionary polynomial regression (EPR) based approach is presented for constitutive modelling of soils. EPR is an evolutionary computing method that generates a transparent and structured representation of the data provided. EPR can operate on large quantities of data in order to capture the complex interaction between the variables of the system. Furthermore it allows the user to gain additional insight into the constitutive behaviour of the material by providing a structured representation of the data. Capabilities of the EPR methodology in constitutive modelling of soils are illustrated by application to modelling of the behaviour of soils under drained and undrained loading conditions. In addition, an algorithm, so called feed-forward algorithm, has been developed to show that the proposed EPR based constitutive model is capable of reproducing the behaviour of soil over an entire stress path.
Moreover, a new approach is presented, for the first time, for constitutive modelling of materials in finite element analysis, with potential applications in different engineering disciplines. The proposed approach provides a unified framework for modelling of complex materials, using evolutionary polynomial regression based constitutive model (EPRCM), integrated in finite element analysis. The main advantage of EPRCM over conventional constitutive models is that it provides the optimum structure for the material constitutive model representation as well as its parameters, directly from raw experimental (or field) data. The proposed algorithm provides a transparent relationship for the constitutive material model that can easily be incorporated in a finite element model. The application of the EPRCM for material modelling in finite element analysis will be illustrated through a number of simple and complex examples.",
Effective Researcher Award for Excellence 2008",
Genetic Programming entries for Mohammad Rezania