Improving the Mechanical Performance of Biocomposite Plaster/ Washingtonia filifera: Optimization Comparison Between ANN and RSM Approaches

Abstract

The present research is an extension of a previous paper published by the authors. In the first part of the research, the flexural properties of Washingtonia filifera (WF) fiber-reinforced plaster composite treated with sodium bicarbonate were explored using response surface method statistics. In the current study, the data was analyzed using artificial neural network tool. The main objective of the current research is to model the flexural properties of an environmentally friendly gypsum biocomposite reinforced with treated and untreated WF fibers using response surface method and artificial neural networks. For this purpose, the study reports a comparative approach between models predicted by response surface methodology (RSM) and artificial neural networks (ANNs). The statistical results as root mean square error and coefficient of determination reveal that ANN and RSM are effective techniques for bending properties prediction of plaster/WF biocomposites. In addition, ANN and RSM models correlate highly with the experimental data. However, artificial neural network model displayed more accuracy.

Keywords

• washingtonia filifera
• plaster-gypsum
• tg/dsc
• prediction
• artificial neural networks
• response surface methodology