Using mathematical models and artificial neural networks for predicting the compressive strength of concrete with steel fibers exposed to high temperatures

  • Hassiba Belaribi Laboratoire de recherché en Génie Civil, Université de Biskra, B.P. 145 R.P. 07000, Biskra, Algeria
  • Mekki Mellas Laboratoire de recherché en Génie Civil, Université de Biskra, B.P. 145 R.P. 07000, Biskra, Algeria

Abstract

In this study mathematical methods and artificial neural network (ANN) model are used to predict the compressive strength of concrete with steel fibers exposed at high temperatures. The data used in the models construction were obtained from laboratory experiments. The compressive strength was experimentally determined for specimens containing three volume fractions of steel fibers 0.19%,0.25%, 0.5% were used and two different water/cement ratios (w/c of 0.35 and 0.45). Specimens were subjected to various heating-cooling cycles from the room temperature to 200, 400, and 600°C. The inputs models of ANN were temperature, w/c, percentage of porosity, ultrasonic pulse velocity; percentage of steel fibers and percentage superplasticizer, the output was the compressive strength of concrete. Four mathematical models were development to predict the compressive strength. Three mathematical models including a number of functions to express the strength-porosity relationship, and other model used to establish the relationships between strength and ultrasonic pulse velocity. Mathematical methods, artificial neural network and their results were evaluated and compared. The results show that ANN has good potential to be used as a tool for predicting the compressive strength of concrete with steel fibers exposed to high temperatures.

Published
2018-05-26
How to Cite
BELARIBI, Hassiba; MELLAS, Mekki. Using mathematical models and artificial neural networks for predicting the compressive strength of concrete with steel fibers exposed to high temperatures. Journal of Applied Engineering Science & Technology, [S.l.], v. 4, n. 1, p. 61-67, may 2018. ISSN 2352-9873. Available at: <http://revues.univ-biskra.dz/index.php/jaest/article/view/3876>. Date accessed: 08 dec. 2021.
Section
Section C: Geotechnical and Civil Engineering

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.