Affiliations: School of Civil and Transportation Engineering, Henan University of Urban Construction, Pingdingshan, Henan, China
Correspondence:
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Corresponding author. School of Civil and Transportation Engineering, Henan University of Urban Construction, Pingdingshan 467036, Henan, China. E-mail: [email protected].
Abstract: Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty.
