Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

Oyebisi, S.O and Alomayri, Thamer (2023) Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks. Construction and Building Materials, 400.

PDF Download (6MB)

Abstract

The construction and building industry, one of the greatest emitters of greenhouse gases, is under tremendous pressure because of the growing concern about global climate change and its detrimental effects on societies. Given the environmental problems connected to cement production, geopolymer concrete has become a viable alternative. In addition, if the concrete strength results failed to meet the specified strength after being cast, modifications are impossible. Thus, it is particularly desirable to predict strength prior to casting concrete. This study presents the first effort in applying deep neural networks (DNN) of AI techniques to predict the mechanical strengths (GGBFS) of geopolymer concrete (GPC) produced from corncob ash and ground granulated blast furnace slag. The mixes were activated with 12–16 M of alkali solutions at ambiently cured conditions for 7–90 days. Following that, back propagation learning algorithms were created for forecasting the concrete strengths based on concrete mix proportions. The mechanical strengths estimated by the DNN were verified by laboratory testing results. Results revealed that GGBFS, mix grade, curing days, and alkali precursor are variables that govern the mechanical strengths of the GGBFS-CCA-GPC. Forecasting the mechanical properties of GPC produced using DNN shows that the relationship between the input and output arguments could be most accurately predicted by a 10–20–20–20-1 network topology, evident by approximately 99% correlation coefficient between the actual and predictive values for compressive and flexural strengths. However, the 10–17–17–17-1 network architecture showed the best DNN for predicting split tensile strength, with a 97% correlation coefficient between the actual and projected values. This study demonstrated that the DNN techniques are efficient in predicting the mechanical strengths of GPC based on the mix proportions. Application of these techniques will greatly advance concrete quality assurance.

Item Type:	Article
Uncontrolled Keywords:	AlkaliArtificial intelligenceAshCompressive strengthGeopolymer concreteSustainable production
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment
Depositing User:	AKINWUMI
Date Deposited:	24 Oct 2023 12:40
Last Modified:	24 Oct 2023 12:40
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/17432

Actions (login required)

View Item