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

dc.contributor.authorAJAGBE W.
dc.contributor.authorTijani M.
dc.contributor.authorOluwafemi O.
dc.date.accessioned2025-05-13T10:40:23Z
dc.date.issued2022
dc.description.abstractTensile 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.urihttps://repository.ui.edu.ng/handle/123456789/10381
dc.language.isoen
dc.titleModeling The Tensile Strength Of Concrete With Polyethylene Terephthalate (Pet) Waste As Replacement For Fine Aggregate Using Artificial Neural Network
dc.typeArticle

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