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. SixMLmodels, including Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Categorical Boosting (CatB), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), are evaluated against performance metrics and validated by 1000 Monte Carlo simulations. The compressive strength prediction performance by the ML models is arranged in descending order as follows XGB>GB>CatB>RF >ANN>KNN. The two best models can predict the compressive strength of steel fiber concrete to be GB with a coefficient of determination (R2) of 0.9874 and a root mean square error (RMSE) of 2.5763 MPa and XGB with an R2 of 0.9926 and an RMSE of 1.9814 MPa for the testing dataset. The influence of the ten variables can be arranged in descending order as follows: Cement content > Water content > Silica fume content > Steel fiber content >Coarse aggregate content > Sand content > Superplasticizer content > Fiber diameter > Fiber length > Fly ash content. Among them, Cement content, Silica fume content, and Steel fiber content have a positive effect on improving the compressive strength of concrete. The steel fiber content used should be less than 1.5% of concrete volume to improve the efficiency of steel fiber in concrete. Meanwhile, Fiber diameter and Fiber length have a minimal influence on the compressive strength of steel fiber concrete.