Detail publikace

Fibre-reinforced cementitious composite: parameter identification using Ohno shear beam test

LEHKÝ, D. PUKL, R. NOVÁK, D. LIPOWCZAN, M.

Originální název

Fibre-reinforced cementitious composite: parameter identification using Ohno shear beam test

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Shear test, Ohno beam, nonlinear analysis, inverse analysis, artificial neural network

Autoři

LEHKÝ, D.; PUKL, R.; NOVÁK, D.; LIPOWCZAN, M.

Vydáno

23. 11. 2021

Nakladatel

IOP PUBLISHING LTD

Místo

BRISTOL

ISSN

1757-899X

Periodikum

IOP Conference Series: Materials Science and Engineering

Ročník

1205

Číslo

012023

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

8

Strany počet

8

URL

https://iopscience.iop.org/article/10.1088/1757-899X/1205/1/012023/pdf

BibTex

@inproceedings{BUT197208,
  author="David {Lehký} and Radomír {Pukl} and Drahomír {Novák} and Martin {Lipowczan}",
  title="Fibre-reinforced cementitious composite: parameter identification using Ohno shear beam test",
  booktitle="IOP Conference Series: Materials Science and Engineering",
  year="2021",
  journal="IOP Conference Series: Materials Science and Engineering",
  volume="1205",
  number="012023",
  pages="1--8",
  publisher="IOP PUBLISHING LTD",
  address="BRISTOL",
  doi="10.1088/1757-899X/1205/1/012023",
  issn="1757-899X",
  url="https://iopscience.iop.org/article/10.1088/1757-899X/1205/1/012023/pdf"
}