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

ABSTRACT: 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 collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the

SUBMITTER: Khalaf AA 

PROVIDER: S-EPMC9002561 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature