Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques

Awoyera, P. O. and Kirgiz, Mehmet S. and Viloria, A. and Ovallos-Gazabon, D. (2020) Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques. Journal of Material Research and Technology. pp. 9016-9028.

PDF Download (2MB)

Abstract

tThere has been a persistent drive for sustainable development in the concrete industry.While there are series of encouraging experimental research outputs, yet the research fieldrequires a standard framework for the material development. In this study, the strengthcharacteristics of geopolymer self-compacting concrete made by addition of mineral admix-tures, have been modelled with both genetic programming (GEP) and the artificial neuralnetworks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium sili-cate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to theconventional material (river sand), fly ash was partially replaced with silica fume and gran-ulated blast furnace slag. Various properties of the concrete, filler ability and passing abilityof fresh mixtures, and compressive, split-tensile and flexural strength of hardened concretewere determined. The model development involved using raw materials and fresh mix prop-erties as predictors, and strength properties as response. Results shows that the use of theadmixtures enhanced both the fresh and hardened properties of the concrete. Both GEP andANN methods exhibited good prediction of the experimental data, with minimal errors.However, GEP models can be preferred as simple equations are developed from the process,while ANN is only a predictor.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural networksGenetic programmingPredictorResponseSelf-Compacting concreteGeopolymersa
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	22 Jan 2021 13:09
Last Modified:	22 Jan 2021 13:09
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/13805

Actions (login required)

View Item