Modeling The Tensile Strength Of Concrete With Polyethylene Terephthalate (Pet) Waste As Replacement For Fine Aggregate Using Artificial Neural Network

Abstract

Tensile strength of concrete made with polyethylene terephthalate (PET) waste as replacement for fine aggregate was modelled using artificial neural network. A multilayer feedforward neural network (MLFFNN) and radial basis function (RBF) methodology were compared to see which was more accurate. The MLFFNN modelling results showed a predictive accuracy of 95.364% and a root mean square error value of 4.4409 × 10-16 while RBF neural network modeling results showed a higher predictive accuracy (99.509%) with a lower root mean square error value (1.6653 × 10-16). It is concluded that ANN models accurately predicted the tensile strength of PET concrete.