Self-compacting concrete strength prediction using surrogate models

Abstract

Despite the extensive use of self-compacting concrete in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength based on its mix components. Τhis limitation is due to the highly nonlinear relation between the self-compacting concrete’s compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the mechanical characteristics of self-compacting concrete has been investigated. Specifically, surrogate models (such as artificial neural network models and a new proposed normalization method) have been used for predicting the 28-day compressive strength of admixture-based self-compacting concrete (based on experimental data available in the literature). The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of self-compacting concrete in a reliable and robust manner. Furthermore, the proposed formula for the normalization of data has been proven effective and robust compared to available ones.