Issue 39

P. Král et alii, Frattura ed Integrità Strutturale, 39 (2017) 38-46; DOI: 10.3221/IGF-ESIS.39.05 39 models of concrete also require multiple material parameters, some of which carry only a mathematical meaning and therefore remain difficult to acquire due to the lack of a physical substance or can be acquired only via special testing. Such aspects then render the use of certain material models rather problematic; however, modern computing technology enables us to solve the above-mentioned drawbacks by means of inverse analysis, or, in other words, the inverse identification of material parameters. The inverse analysis combines the numerical and experimental approaches with optimization procedures, and it advantageously facilitates finding the optimum parameter values of the employed material model in such a manner that the computer simulation-based response of the structure is as close as possible to the structure’s real response obtained from the related experimental measurement. At present, the set of the most widely preferred techniques for the inverse identification of material parameters includes, among other tools, optimization methods based on the training of artificial neural networks [13]. Considering commercial computing systems, a very powerful instrument to perform inverse analysis currently appears to consist in the optiSLang program [14], which offers a robust algorithm comprising a broad spectrum of optimization procedures suitable for the inverse identification of material parameters [15, 16]. The aim of the research characterized in this paper is to execute the inverse identification of the material parameters of a nonlinear constitutive model, namely, the Karagozian & Case (K & C) Concrete model [17]. The relevant inverse analysis is carried out exploiting a load-displacement curve, one experimentally measured during the triaxial compression strength testing of concrete cylinders. Within the inverse analysis process, we utilize the interaction of nonlinear numerical simulations performed using LS-Dyna software (an explicit solver of the finite element method), with the optimization procedures implemented in the optiSLang program. E XPERIMENTAL ANALYSIS enerally, the entire inverse analysis process requires us to define the input data. In the inverse identification of the material parameters of nonlinear models, the basic input information consists in experimental data, which are most often formed by a loading curve defining the response of the real structure to static or dynamic loading. Triaxial compression strength testing of concrete cylinders Within this paper, the experimental data consisted in a load-displacement curve obtained from the multiple triaxial compression strength tests presented by the authors of reference [18]. Such testing of cylindrical concrete specimens is one of the methods to examine and verify the physical-mechanical properties of concrete. The dimensions of the concrete cylinders applied in each test were, invariably, 304.8 mm (height) and 152.4 mm (base diameter). The established ultimate uniaxial compressive strength of the concrete used to produce the test cylinders corresponded to 45.4 MPa. During the testing, each cylinder was compressed at a constant velocity, and the loading had a quasi-static character. For this paper, we chose the results for the temporally constant transverse pressure (confinement) of 7 MPa, from which the triaxial stress was induced. Figure 1 : The experimentally-measured load-displacement curve. G