Prediction of Compressive and Splitting Tensile Strengths in Steel Fiber-Reinforced Recycled Aggregate Concrete Using Machine Learning and PSO Optimization

This study examines the use of GradientBoostingRegressor, StackingRegressor, and Gradient Boosting Regression withHistGradientBoosting in developing models that predict the compressive strength (fcu) and splitting tensile strength (fsp) of steelfiber-reinforced recycled aggregate concrete (SFR-RAC). The information comprises 465 compressive strength and 339 splittingtensile strength data of concrete mixes with varied ratios. Training and model testing were performed using 80/20 split with PSOfor the hyperparameter optimization. The performance of the model was measured with four statistical metrics: coefficient ofdetermination (R²), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE).Out of the models, Gradient Boosting Regression with HistGradientBoosting performed better in terms of prediction, withStackingRegressor taking the second rank. SHapley Additive exPlanations (SHAP) and feature importance were employed todetermine the influence of input parameters on model predictions. From the results obtained, it was evident that the watercontent, cement content, and fiber ratio influence considerably the strength of SFR-RAC. The models give good insightsregarding SFR-RAC mixture behavior, which is helpful in the production of environmentally friendly concrete with greaterenhanced strength. Future research can enhance the data and use other predictor variables to further support these models.