Investigating compressive strength of concrete containing steel fiber by data-driven approach

The main objective of this paper is to use the data-driven approach to predict and study the factors affecting the compressive strength of steel fiber concrete. Therefore, six machine learning (ML) models were evaluated against a database of 166 samples and ten input variables, including Cement content, Water content, Silica fume content, Steel fiber content, Coarse aggregate content, Sand content, Superplasticizer content, Fiber diameter, Fiber length, Fly ash content.