Publication detail

Identification of Mechanical Fracture Parameters of Alkali-activated Slag Based Composites During Specimens Ageing

ŠIMONOVÁ, H. KUCHARCZYKOVÁ, B. LIPOWCZAN, M. LEHKÝ, D. BÍLEK, V. KOCÁB, D.

Original Title

Identification of Mechanical Fracture Parameters of Alkali-activated Slag Based Composites During Specimens Ageing

English Title

Identification of Mechanical Fracture Parameters of Alkali-activated Slag Based Composites During Specimens Ageing

Type

journal article - other

Language

en

Original Abstract

The aim of the paper is to present the results of the experiment focused on the development of the mechanical fracture characteristics of alkali-activated slag (AAS) based composites within the time interval from 3 days to 2 years of ageing. Two AAS composites, which differed only in the presence of shrinkage reducing admixture (SRA), were prepared for the purpose of experiments. The composites were prepared using ground granulated blast furnace slag activated by water-glass with silicate modulus of 2.0, standardized quartzite sand with the particle grain size distribution of 0−2 mm, and water. Commercially produced SRA was added into the second mixture in an amount of 2 % by weight of slag. The test specimens were not protected from drying during the whole time interval and were stored in the laboratory at an ambient temperature of 21 ± 2 °C and relative humidity of 60 ± 10 %. The prism specimens made of above-mentioned composites with nominal dimensions of 40 × 40 × 160 mm with the initial central edge notch were subjected to the fracture tests in three-point bending configuration. The load F and displacement d (deflection in the middle of the span length) were continuously recorded during the fracture tests. The obtained F−d diagrams and specimen dimensions were used as input data for identification of parameters via the inverse analysis based on the artificial neural network, which aim is to transfer the fracture test response data to the desired material parameters. In this paper, the modulus of elasticity, tensile strength, and fracture energy values were identified and subsequently compared with values obtained based on the fracture test evaluation using the effective crack model and work-of-fracture method.

English abstract

The aim of the paper is to present the results of the experiment focused on the development of the mechanical fracture characteristics of alkali-activated slag (AAS) based composites within the time interval from 3 days to 2 years of ageing. Two AAS composites, which differed only in the presence of shrinkage reducing admixture (SRA), were prepared for the purpose of experiments. The composites were prepared using ground granulated blast furnace slag activated by water-glass with silicate modulus of 2.0, standardized quartzite sand with the particle grain size distribution of 0−2 mm, and water. Commercially produced SRA was added into the second mixture in an amount of 2 % by weight of slag. The test specimens were not protected from drying during the whole time interval and were stored in the laboratory at an ambient temperature of 21 ± 2 °C and relative humidity of 60 ± 10 %. The prism specimens made of above-mentioned composites with nominal dimensions of 40 × 40 × 160 mm with the initial central edge notch were subjected to the fracture tests in three-point bending configuration. The load F and displacement d (deflection in the middle of the span length) were continuously recorded during the fracture tests. The obtained F−d diagrams and specimen dimensions were used as input data for identification of parameters via the inverse analysis based on the artificial neural network, which aim is to transfer the fracture test response data to the desired material parameters. In this paper, the modulus of elasticity, tensile strength, and fracture energy values were identified and subsequently compared with values obtained based on the fracture test evaluation using the effective crack model and work-of-fracture method.

Keywords

Fracture test, Inverse analysis, Artificial neural network, Effective crack model, Work-of-fracture method, Slag, Alkali activation

Released

31.12.2019

Publisher

VSB - Technical University of Ostrava, Faculty of Civil Engineering

Location

Ostrava

ISBN

1213-1962

Periodical

Sborník vědeckých prací Vysoké školy báňské – Technické univerzity Ostrava

Year of study

19

Number

2

State

CZ

Pages from

59

Pages to

64

Pages count

6

URL

Documents

BibTex


@article{BUT160635,
  author="Hana {Šimonová} and Barbara {Kucharczyková} and Martin {Lipowczan} and David {Lehký} and Vlastimil {Bílek} and Dalibor {Kocáb}",
  title="Identification of Mechanical Fracture Parameters of Alkali-activated Slag Based Composites During Specimens Ageing",
  annote="The aim of the paper is to present the results of the experiment focused on the development of the mechanical fracture characteristics of alkali-activated slag (AAS) based composites within the time interval from 3 days to 2 years of ageing. Two AAS composites, which differed only in the presence of shrinkage reducing admixture (SRA), were prepared for the purpose of experiments. The composites were prepared using ground granulated blast furnace slag activated by water-glass with silicate modulus of 2.0, standardized quartzite sand with the particle grain size distribution of 0−2 mm, and water. Commercially produced SRA was added into the second mixture in an amount of 2 % by weight of slag. The test specimens were not protected from drying during the whole time interval and were stored in the laboratory at an ambient temperature of 21 ± 2 °C and relative humidity of 60 ± 10 %. The prism specimens made of above-mentioned composites with nominal dimensions of 40 × 40 × 160 mm with the initial central edge notch were subjected to the fracture tests in three-point bending configuration. The load F and displacement d (deflection in the middle of the span length) were continuously recorded during the fracture tests. The obtained F−d diagrams and specimen dimensions were used as input data for identification of parameters via the inverse analysis based on the artificial neural network, which aim is to transfer the fracture test response data to the desired material parameters. In this paper, the modulus of elasticity, tensile strength, and fracture energy values were identified and subsequently compared with values obtained based on the fracture test evaluation using the effective crack model and work-of-fracture method.",
  address="VSB - Technical University of Ostrava, Faculty of Civil Engineering",
  chapter="160635",
  doi="10.35181/tces-2019-0021",
  institution="VSB - Technical University of Ostrava, Faculty of Civil Engineering",
  number="2",
  volume="19",
  year="2019",
  month="december",
  pages="59--64",
  publisher="VSB - Technical University of Ostrava, Faculty of Civil Engineering",
  type="journal article - other"
}