An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

Musa Adamu; A. Batur Çolak; Ibrahim K. Umar; Yasser E. Ibrahim; Mukhtar F. Hamza

doi:10.28991/CEJ-2023-09-09-04

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Musa Adamu
madamu@psu.edu.sa
Engineering Management Department, College of Engineering, Prince Sultan University, 11586 Riyadh,, Saudi Arabia
A. Batur Çolak Information Technologies Application and Research Center, Istanbul Ticaret University, Istanbul 34445,, Turkey
Ibrahim K. Umar Department of Civil Engineering, Kano State Polytechnics, Kano,, Nigeria
Yasser E. Ibrahim Engineering Management Department, College of Engineering, Prince Sultan University, 11586 Riyadh,, Saudi Arabia
Mukhtar F. Hamza Department of Mechanical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 16273,, Saudi Arabia

Vol. 9 No. 9 (2023): September

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Plastic waste (PW) is a major soild waste, which its generation continues to increase globally year in and year out. Proper management of the PW is still a challenge due to its non-biodegradable nature. One of the most convenient ways of managing plastic waste is by using it in concrete as a partial substitute for natural aggregate. However, the main shortcomings of adding plastic waste to concrete are a reduction in strength and durability. Hence, to reduce the undesirable impact of the PW in concrete, highly reactive additives are normally added. In this research, 240 experimental datasets were used to train an artificial neural network (ANN) model using Levenberg Marquadt algorithms for the prediction of the mechanical properties and durability of high-volume fly ash (HVFA) concrete containing fly ash and PW as partial substitutes for cement and coarse aggregate, respectively, and graphene nanoplatlets (GNP) as additives to cementitious materials. The optimized model structure has five input parameters, 17 hidden neurons, and one output layer for each of the physical parameters. The results were analyzed graphically and statistically. The obtained results revealed that the generated network model can forecast with deviations less than 0.48%. The efficiency of the ANN model in predicting concrete properties was compared with that of the SVR (support vector regression) and SWLR (stepwise regression) models. The ANN outperformed SVR and SWLR for all the models by up to 6% and 74% for SVR and SWLR, respectively, in the confirmation stage. The graphical analysis of the results further demonstrates the higher prediction ability of the ANN.

Doi: 10.28991/CEJ-2023-09-09-04

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Publication Start Year:	2015
JCR 2023 Impact Factor (IF):	4.3
JIF QUARTILE:	Q1
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An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

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