1Faculty of Civil Engineering, Institute of Structural Mechanics, Brno University of Technology, Veveří 95, Brno, 60200, Czech Republic
2Červenka Consulting s.r.o., Na Hřebenkách 55, Praha 5, 15000, Czech Republic
Adv. Mater. Lett., 2020, 11 (3), 20031488 (1-5)
Publication Date (Web): Feb 26, 2020
Copyright © IAAM-VBRI Press
Methods and software tools used to identify the material parameters of high-performance cementitious composites are presented. The aim is to provide techniques for the advanced assessment of the mechanical fracture properties of these materials, and the subsequent numerical simulation of components/structures made from them. The paper describes the development of computational and material models utilized for efficient material parameter determination with regards to a studied composite. This determination is performed with the help of experimental data from four-point bending tests. The data is used in inverse analysis based on artificial neural networks. Sensitivity analysis plays an important role in the process. It is a part of a complex methodology for the statistical and reliability analysis of structures made of high-performance cementitious composites. The procedure also utilizes statistical simulation of the Monte Carlo type for the preparation of a training set for the artificial neural network utilized in the material parameter identification process. In the case of fiber-reinforced concrete, the simulation mainly includes tensile strength, modulus of elasticity and the parameters of the tensile softening model.
Fiber-reinforced cementitious composite, concrete, nonlinear modeling, sensitivity analysis