Prediction of the Mechanical Properties of Fibre-Reinforced Quarry Dust Concrete Using Response Surface and Artificial Neural Network Techniques

Sridhar, Jayaprakash and Balaji, Shanmugam and Jegatheeswaran, Dhanapal and Awoyera, P. O. (2023) Prediction of the Mechanical Properties of Fibre-Reinforced Quarry Dust Concrete Using Response Surface and Artificial Neural Network Techniques. Advances in Civil Engineering, 7.

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Abstract

Te focus of this study is to forecast the 28-day compressive strength and split tensile strength of concrete with various percentages of jute and coconut fbres mixed with quarry dust. Te response surface methodology (RSM) and the artifcial neural networks (ANN) methods were adopted for 3 variable process modelling (coconut fbres of 0% to 2.5%, jute fbres of 0% to 2.5%, and quarry dust of 0% to 25% by weight of cement). Te RSM Box−Behnken design (BBD) method was adopted to design the experiments. Test results showed that compressive strength of 34.6 N/mm 2 was obtained for concrete with 0% jute, 0% coir, and 12.5% quarry dust. Similarly, the maximum split tensile strength of 3.8 N/mm 2 was obtained for concrete with 1.25% jute fbres, 1.25% coconut fbres, and 12.5% quarry dust. ANOVA and Pareto charts were used to assess regression models for response data. Each progression variable’s statistical signifcance was assessed, and the resulting models were expressed as second-order polynomial equations. Levenberg−Marquardt (LM) algorithm with feed-forward back propagation neural network was used for assessing the compressive strength and split tensile strength of concrete. Te statistical data, root mean square error (RMSE), mean absolute error (MAE), mean absolute and percentage error (MAPE), and determination coefcient (R2 ) show that both techniques, ANN and RSM, are efective tools for predicting compressive strength and split tensile strength. Furthermore, RSM and ANN models have a high correlation with experimental data. However, the response surface methodology model is more accurate.

Item Type:	Article
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:	27 Mar 2023 10:58
Last Modified:	27 Mar 2023 10:58
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/16735

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