Modelling the strength properties of concrete containing construction demolition waste using Response Surface Methodology and Artificial Neural Network - Oluyemi Ayibiowu, Bamitale Dorcas; Okanlawon, Rufus Aderenle; Falola, Kayode Emmanuel (European Journal of Applied Sciences, 2022)
The study presents a comparative approach between Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in estimating the compressive strength and flexural strength of concrete incorporating Construction Demolition waste. The effects of factor variables such as %RCA (Recycle Concrete Aggregate as replacement for granite), water-cement ratio, % RFA (Recycled Fine Aggregate as replacement for sand) and Recycled Concrete Aggregate size were investigated using the central composite design of response surface methodology. This same experimental design results were used in training the artificial neural network. The predicting ability of both methodologies was compared using the Root Mean Square Error (RMSE) and the Absolute Average Deviation (AAD). Response Surface Methodology Model had a RMSE and AAD compressive strength score of 9.74 and 1.6 while the ANN had score of 14.76 and 2.6 for compressive strength. When compared to Artificial Neural Network, Response Surface Methodology showed lower predictive error functions for both tensile and compressive strength predictions, and was found to be more accurate in its ability to predict the strength properties of CDW concrete.