Issue 39

P. Král et alii, Frattura ed Integrità Strutturale, 39 (2017) 38-46; DOI: 10.3221/IGF-ESIS.39.05 45 of the inverse analysis conducted by the authors of this paper showed that the K & C Concrete model is a suitable tool to describe the nonlinear behavior of concrete in triaxial compression, and they also indicated that, in any case where the parameters of the model are conveniently selected and entered, we can obtain a very good approximation of the experimental data. Importantly, this claim is proved by the outcome of the numerical simulation performed using the resulting identified parameter values of the material model. The discussed results then approximated the applied experimental data with a high degree of accuracy. Advantageously, the product of the procedure can be exploited for further research concerning the nonlinear numerical response of concrete structures. Although the experimental data approximation yielded very good accuracy, it has to be pointed out that there still is the possibility of reaching an even better performance rate via operations such as local optimization, the application of an optimization procedure different from that characterized in this paper, increasing the number of the design vector generations, or using a non-reduced design vector within the optimization process. These aspects, which could further improve the accuracy of experimental data approximation and thus lead us towards obtaining more refined values of identified material parameters, will be examined within future research planned by the authors of this paper. A CKNOWLEDGMENT he research presented within this paper was supported from project GA14-25320S "Aspects of the use of complex nonlinear material models" provided by Czech Science Foundation. Financial assistance was also received via project FAST-J-16-3744 "Optimization of the parameters of nonlinear concrete material models for explicit dynamics"; this instrument was structured and guaranteed by Brno University of Technology to support the Specific University Research. R EFERENCES [1] ANSYS, ANSYS Mechanical Theory Reference Release 15.0, (2014). [2] LS-DYNA, Theory Manual, Livemore Software Technology Corporation, Livemore, California, (2016). [3] ATENA Program Documentation, Cervenka consulting Ltd., (2013). [4] Kala, J., Hradil, P., Bajer, M., Reinforced concrete wall under shear load – Experimental and nonlinear simulation, International Journal of Mechanics, 9 (2015) 206-212. [5] Hradil, P., Kala, J., Analysis of the shear failure of a reinforced concrete wall, Applied Mechanics and Materials, 621 (2014) 124-129. [6] Hokeš, F., Selected Aspects of Modelling of Non-Linear Behaviour of Concrete During Tensile Test Using Multiplas Library, Transactions of the VŠB - Technical University of Ostrava, Civil Engineering Series, 2(15) (2015). [7] Sucharda, O., Brozovsky, J., Numerical modelling of reinforced concrete beams with fracture-plastic material, Frattura ed Integrita Strutturale, 30 (2014) 375-382. DOI: 10.3221/IGF-ESIS.30.4 [8] Kala, J., Hušek, M., Improved Element Erosion Function for Concrete-Like Materials with the SPH Method, Shock and Vibration, 2016 (2016) 1-13. [9] Wu, M., Chen, Z., Zhang, C., Determining the impact behavior of concrete beams through experimental testing and meso-scale simulation: I. Drop-weight tests, Engineering Fracture Mechanics, 135 (2015) 94-112. [10] Král, P., Kala, J., Hradil, P., Verification of the Elasto-Plastic Behavior of Nonlinear Concrete Material Models, International Journal of Mechanics, 10 (2016) 175-181. [11] Cichocki, K., Domski, J., Katzer, J., Ruchwa, M., Static and Dynamic Characteristics of Waste Ceramic Aggregate Fibre Reinforced Concrete, Transactions of the VŠB - Technical University of Ostrava, Civil Engineering Series, 2(15) (2015). [12] Králik, J., Králik Jr., J., Seismic analysis of reinforced concrete frame-wall systems considering ductility effects in accordance to Eurocode, Engineering Structures, 31(12) (2009) 2865-2872. DOI: 10.1016/j.engstruct.2009.07.029. [13] Lehký, D., Novák, D., Inverse reliability problem solved by artificial neural networks, In: Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures, New York, USA, (2013) 5303-5310. [14] optiSLang, Methods for multi-disciplinary optimization and robustness analysis, Dynardo GmbH, Weimar, Germany, (2014). [15] Most, T., Identification of the parameters of complex constitutive models: Least squares minimization vs. Bayesian updating, Reliability Conference in München, (2010). T