Abstract
The aim of this paper is to perform the inverse identification of the material parameters of a nonlinear constitutive model intended for the modeling of concrete which is known as the Karagozian & Case Concrete model. At present, inverse analysis is frequently used because it allows us to find the optimum parameter values of nonlinear material models. When applying such parameters, the resulting response of the structure obtained from a computer simulation is very similar to the real response of the structure based on the related experimental measurement. This condition then undoubtedly constitutes one of the progressive steps to refine the current numerical approaches. For the purposes of the inverse analysis performed in this paper the experimental data was obtained from the triaxial compression strength tests carried out on the concrete cylinders.
Highlights
Using nonlinear material models of concrete to examine the behavior of concrete structures exposed to static or dynamic loading currently constitutes a major move within the effort to approach the real character of and processes in the material during computer simulations
This paper was based on performing an inverse analysis to identify the material parameters of the Karagozian & Case Concrete constitutive model
The inverse analysis was carried out utilizing the load-displacement curve experimentally measured during the triaxial compression strength testing of concrete cylinders
Summary
Using nonlinear material models of concrete to examine the behavior of concrete structures exposed to static or dynamic loading currently constitutes a major move within the effort to approach the real character of and processes in the material during computer simulations. Current modern computing systems based on the finite element method, such as ANSYS [1], LS-Dyna [2], and Atena [3], offer a comparatively wide variety of nonlinear material models to simulate different conditions across the entire spectrum of materials. According to their respective properties, these models are applicable in both static [4,5,6,7] and dynamic [8,9,10,11,12] numerical simulations.
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