Özet:
This study presents soft computing and statistical models for the prediction of the compressive strength
of steel fiber-reinforced concrete. The model examines nine different parameters for the matrix
mixture, including: (C) cement content; (W) water content; (CA) coarse aggregate content; (FA) fine
aggregate content; (Dmax) maximum size of aggregate; (Vf) volume of steel fiber; (Lf) length of fiber;
(Df) diameter of fiber; and (SP) superplasticizer content. An artificial neural network (ANN), gene
expression programming (GEP), random forest method (RFM), LASSO regression, and multiple linear
regression (MLR) methods were used to predict the 28-day compressive strength. The data set was
formed by collecting 204 experimental data samples from available literature to cover different
variables. These models are compared and evaluated through statistical tests such as coefficient of
determination (R2
) and root mean squared error (RMSE). Statistical analysis and comparisons revealed
that ANN, GEP, and RFM models can be useful tools in predicting the compressive strength of steel
fiber-reinforced concrete. The results also indicate that the proposed formula for ANN, GEP, and RFM
provides a more reliable and accurate prediction ability than the LASSO and MLR methods.
