Efficacious application of data-driven machine learning models for predicting and optimizing the flexural tensile strength of fiber-reinforced concrete

Abstract

In the literature, a limited number of studies have utilized the random forest (RF) technique to model the flexural tensile strength (FTS) of steel fiber reinforced concrete (SFRC). This study established modeling of the FTS of SFRC containing mono-fibrous (exclusively crimped and hooked) systems. A comprehensive database of 193 records was used from 18 published studies (including 30 documents sampled by authors). Machine-learning numerical models were developed and evaluated. Partial dependence plots were constructed to visualize the relationship between the model parameters and rank the parameters based on their importance. It has been found that the RF model can be rationally used to predict and optimize the FTS of SFRC, as most of the predicted–tested results were close to the ± 80% accuracy range. The model showed of underestimation nature for SFRC with high FTS values. The Gini index-based importance analysis showed that the fiber’s content and tensile strength had the highest significance among the other variables. A drastic increase in the FTS by an increase in the fibrous dosage was achieved. However, no major SFRC’s tensile strength enhancement for dosages was more than 1.5%, while its optimum diameter was 0.5–0.7 mm. The results also illustrated that the effect of the fiber’s length is quite similar to that of the fiber content. However, no significant increase can be achieved by increasing the fiber’s length from 40 to 60 mm.