University of Oulu

Elijah Adesanya, Adeyemi Aladejare, Adeolu Adediran, Abiodun Lawal, Mirja Illikainen, Predicting shrinkage of alkali-activated blast furnace-fly ash mortars using artificial neural network (ANN), Cement and Concrete Composites, Volume 124, 2021, 104265, ISSN 0958-9465, https://doi.org/10.1016/j.cemconcomp.2021.104265

Predicting shrinkage of alkali-activated blast furnace-fly ash mortars using artificial neural network (ANN)

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Author: Adesanya, Elijah1; Aladejare, Adeyemi2; Adediran, Adeolu1;
Organizations: 1Faculty of Technology, Fibre and Particle Engineering Research Unit, University of Oulu, PO Box 4300, 90014, Finland
2Oulu Mining School, University of Oulu, Oulu, 90014, Finland
3Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021111254965
Language: English
Published: Elsevier, 2021
Publish Date: 2021-11-12
Description:

Abstract

Drying shrinkage of alkali-activated binders are recognized as one of the most important properties towards quality assurance of the binders. In this study, results of experimental studies and predictive models developed to determine the drying shrinkage of alkali - activated blast furnace-fly ash mortars are presented and discussed. Different parameters were altered in the experimental study such as the content of GGBFS, FA, activator modulus (Ms), and curing temperature. Their effects on the drying shrinkage of the mortars were then evaluated. Artificial neural network (ANN) and Multiple Linear Regression (MLR) models were built to predict the drying shrinkage at 28 days using the above-mentioned parameters as inputs. The experimental results and ANN model predictions showed strong correlations. The prediction of 28-days drying shrinkage for the alkali-activated GGBFS-FA was more accurate using ANN than MLR.

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Series: Cement & concrete composites
ISSN: 0958-9465
ISSN-E: 1873-393X
ISSN-L: 0958-9465
Volume: 124
Article number: 104265
DOI: 10.1016/j.cemconcomp.2021.104265
OADOI: https://oadoi.org/10.1016/j.cemconcomp.2021.104265
Type of Publication: A1 Journal article – refereed
Field of Science: 215 Chemical engineering
216 Materials engineering
Subjects:
Funding: This research was supported by the Fibre and Particle Engineering Research Unit. Adeolu Adediran has received funding from Ahti Pekkala Foundation toward his doctoral research.
Copyright information: © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/