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

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 concret...

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Bibliographic Details
Published in:Cogent engineering 2019, Vol.6 (1)
Main Authors: Awolusi, Temitope F., Oke, Oluwaseyi L., Akinkurolere, Olufunke O., Atoyebi, Olumoyewa D.
Format: Article
Language:English
Subjects:
Artificial neural networks
Aspect ratio
civil engineering
Compressive strength
concrete
Design of experiments
Flexural strength
genetic algorithm artificial neural network
Genetic algorithms
hybrid
Neural networks
Process variables
Reinforced concrete
Reinforcing steels
Response Surface Methodology
Root-mean-square errors
Steel fiber reinforced concretes
Steel fibers
steel fibre reinforced concrete
Tensile strength
Training
Water absorption
Water-cement ratio
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Summary: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.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2019.1649852