Modeling The Tensile Strength Of Concrete With Polyethylene Terephthalate (Pet) Waste As Replacement For Fine Aggregate Using Artificial Neural Network
dc.contributor.author | AJAGBE W. | |
dc.contributor.author | Tijani M. | |
dc.contributor.author | Oluwafemi O. | |
dc.date.accessioned | 2025-05-13T10:40:23Z | |
dc.date.issued | 2022 | |
dc.description.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. | |
dc.identifier.uri | https://repository.ui.edu.ng/handle/123456789/10381 | |
dc.language.iso | en | |
dc.title | Modeling The Tensile Strength Of Concrete With Polyethylene Terephthalate (Pet) Waste As Replacement For Fine Aggregate Using Artificial Neural Network | |
dc.type | Article |