This study evaluates the performance of nine supervised machine learning (ML) techniques, namely, K-nearest neighbors (KNN), decision tree (DT), support vector regression (SVR), random forest (RF), gradient boosting (GB), AdaBoost (AB), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and categorical boosting (CatBoost)—on predicting the compressive and tensile strengths of steel fiber reinforced concrete (SFRC), thus providing a data-driven, non-destructive framework for assessing material performance. An extensive dataset is created by varying the fine-to-coarse aggregate ratio and steel fiber content in the range of 0%–2%. The models were evaluated using performance metrics such as coefficient of determination, R 2 , and mean squared error (MSE). The results indicate that XGBoost exhibited the best performance of all the compared ML models, as evident through its R 2 value of 0.9926 for flexural strength, 0.9965 for split tensile, and 0.7837 for compressive strength prediction. These results illustrate that the nonlinear behavior of SFRC can be captured more effectively by AI-ML models and provide better and more accurate strength predictions, thereby supporting advanced non-destructive testing strategies and reducing reliance on extensive destructive testing. Through this study, ML models can be positioned within a structural health monitoring (SHM) context where the predicted parameters of strength can be used for maintenance planning, condition assessment, and damage detection. The outcomes of this study contribute to the enhancement of data-driven approaches for material characterization, thus helping incorporate ML models into real-world structural assessment frameworks.
Shijin et al. (Tue,) studied this question.