Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model

This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were c...

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Detalles Bibliográficos
Autores principales: Khalaf, Ali Abdulhasan, Kopecskó, Katalin, Merta, Ildiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002561/
https://www.ncbi.nlm.nih.gov/pubmed/35406295
http://dx.doi.org/10.3390/polym14071423

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002561/
https://www.ncbi.nlm.nih.gov/pubmed/35406295
http://dx.doi.org/10.3390/polym14071423

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