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Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires
Temitope F. Awolusi, Oluwaseyi L. Oke, Olufunke O. Akinkurolere and Olumoyewa D. Atoyebi. (2019). Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres. Cogent Engineering. Volume 6, 1649852. https://doi.org/10.1080/23311916.2019.1649852
Temitope F. Awolusi, Oluwaseyi L. Oke, Olufunke O. Akinkurolere and Olumoyewa D. Atoyebi. (2019). Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres. Cogent Engineering. Volume 6, 1649852. https://doi.org/10.1080/23311916.2019.1649852
Temitope F. Awolusi, Oluwaseyi L. Oke, Olufunke O. Akinkurolere and Olumoyewa D. Atoyebi. (2019). Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres. Cogent Engineering. Volume 6, 1649852. https://doi.org/10.1080/23311916.2019.1649852
Temitope F. Awolusi, Oluwaseyi L. Oke, Olufunke O. Akinkurolere and Olumoyewa D. Atoyebi. (2019). Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres. Cogent Engineering. Volume 6, 1649852. https://doi.org/10.1080/23311916.2019.1649852
Abstract
The study presents a comparative approach between response surface methodology (RSM) and hybridized, genetic algorithm artificial neural network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength split tensile strength and slump for steel fiber reinforced concrete. The effect of process variables such as aspect ratio, water cement ratio and cement content were investigated using the central composite design of response surface methodology. This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies were compared using the root mean sqaured error (RMSE), mean absolute error (MAE), model predictive error (MPE) and absolute average deviation (AAD). The RSM model was found more accurate in prediction compared to hybrid GA-ANN.
Copyright:
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