Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 14 March 2019 / Approved: 15 March 2019 / Online: 15 March 2019 (09:54:22 CET)

How to cite: Awolusi, T.F.; Oke, O.L.; Akinkurolere, O.O.; Atoyebi, O. 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. Preprints 2019, 2019030161 (doi: 10.20944/preprints201903.0161.v1). Awolusi, T.F.; Oke, O.L.; Akinkurolere, O.O.; Atoyebi, O. 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. Preprints 2019, 2019030161 (doi: 10.20944/preprints201903.0161.v1).

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.

Subject Areas

Response Surface Methodology; Hybrid; Genetic Algorithm Artificial Neural Network; Concrete; Flexural Strength; Steel Fibre Reinforced Concrete; Civil Engineering

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