Predicting the compressive strength of fiber-reinforced recycled aggregate concrete: A machine-learning modeling with SHAP analysis

Fiber-reinforced recycled aggregate concrete (FR-RAC) has recently gained more popularity because of its advantages, high strength, eco-friendliness, and cost-effectiveness. This study uses an advanced machine-learning technique for forecasting the compressive strength of FR-RAC. In this study, an experimental database that contained pertinent data from several previous research was evaluated to train and test using machine learning (ML) techniques and models. To accurately represent the subtle interactions within the dataset, the multivariate analysis identifies and includes essential factors that impact the complicated behavior of FR-RAC in the model. This study presents a hybrid ML model for predicting concrete’s compressive strength by combining several machine learning algorithms in a novel way. To predict the reliability of machine learning models, several algorithms, such as adaptive boosting regressor, support vector regressor, KNN regressor, gradient boosting, and random forest, were developed to help find the interrelated behaviors of parameters. Among all the models used in this study, the Light Gradient-Boosting Machine (GBM) outperforms (R 2 = 0.90) other models, each of which was fitted to a different portion of the training dataset. Additionally, the SHAP analysis revealed that recycled coarse aggregate has an inverse impact on the strength of FR-RAC. Overall, the outcomes of this study can significantly contribute to cost and material reduction by predicting the compressive strength of FR-RAC without the need for extensive laboratory testing and promoting more efficient use of resources.