Open AccessFibre-reinforced cementitious composite: parameter identification using Ohno shear beam test
Author(s) -
David Lehký,
Radomír Pukl,
Drahomı́r Novák,
Martin Lipowczan
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1205/1/012023
Subject(s) - structural engineering , composite number , artificial neural network , materials science , shear (geology) , beam (structure) , sensitivity (control systems) , composite material , test data , computer science , engineering , artificial intelligence , electronic engineering , programming language
Computational-experimental methodology based on artificial neural networks used to identify the material parameters of fibre-reinforced cementitious composite is presented and applied for Ohno shear beam test. The aim is to provide techniques for an 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. The data is used in inverse analysis based on artificial neural networks together with sensitivity analysis which plays an important role in the process. Developed software tool FRCID-S is also briefly described.