{"title":"采用人工神经网络对pet废料替代细骨料的混凝土抗拉强度进行了建模","authors":"W. Ajagbe, M. Tijani, O. Odukoya","doi":"10.29081/jesr.v28i4.003","DOIUrl":null,"url":null,"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.","PeriodicalId":15687,"journal":{"name":"Journal of Engineering Studies and Research","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MODELING THE TENSILE STRENGTH OF CONCRETE WITH POLYETHYLENE TEREPHTHALATE (PET) WASTE AS REPLACEMENT FOR FINE AGGREGATE USING ARTIFICIAL NEURAL NETWORK\",\"authors\":\"W. Ajagbe, M. Tijani, O. Odukoya\",\"doi\":\"10.29081/jesr.v28i4.003\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":15687,\"journal\":{\"name\":\"Journal of Engineering Studies and Research\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering Studies and Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29081/jesr.v28i4.003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Studies and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29081/jesr.v28i4.003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MODELING THE TENSILE STRENGTH OF CONCRETE WITH POLYETHYLENE TEREPHTHALATE (PET) WASTE AS REPLACEMENT FOR FINE AGGREGATE USING ARTIFICIAL NEURAL NETWORK
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.
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