Publication detail

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

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

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

23. 11. 2021

Publisher

IOP PUBLISHING LTD

Location

BRISTOL

ISBN

1757-899X

Periodical

IOP Conference Series: Materials Science and Engineering

Year of study

1205

Number

012023

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

8

Pages count

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"
}