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. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Awolusi, Temitope F.; Oke, Oluwaseyi L.; Akinkurolere, Olufunke O.; Atoyebi, Olumoyewa D.
• Other Names: Bureerat Sujin reviewing-editor.
• Is Part Of: Cogent engineering. Volume 6:Issue 1(2019)
• Publication Date: 2019-01-01
• Subjects: Response Surface Methodology -- hybrid -- genetic algorithm artificial neural network -- concrete -- flexural strength -- steel fibre reinforced concrete -- civil engineering; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: Https://www.tandfonline.com/doi/10.1080/23311916.2019.1649852
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The study presents a comparative approach between Response Surface Methodology (RSM) and hybridized Genetic Algorithm of Artificial Neural Network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength, split tensile strength and slump for steel fibre reinforced concrete. The effects 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 was compared using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Model Predictive Error (MPE) and Absolute Average Deviation (AAD). The response surface methodology model was found more accurate in being able to predict compared to the hybridized genetic algorithm of the artificial neural network.