RAC is a kind of concrete made from Recycled Concrete Aggregates instead of natural aggregates. The use of RAC has been popular in recent years due to the environmental benefits of reducing waste and preserving natural resources. However, one of the RAC-using challenges is accurately predicting its compressive strength. This is a crucial factor in determining its suitability for various structural applications. In this research, eight ML algorithms were trained using a dataset of RAC samples to predict their compressive strength. They were Random Forest, support vector regression (SVR), K nearest neighbors (KNN), light gradient boosting machine (Light GBM), adaptive boosting (Adaboost), gradient boosting, extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP). The best hyperparameters for each algorithm obtained using different hyperparameter tuning methods include Grid Search, Random Search, Successive Halving, Bayesian Optimization with Gaussian Processes (BOGP), Bayesian Optimization Random Forest (BORF), and Bayesian optimization gradient boosted trees (BOGB). The study’s findings indicated that Gradient Boosting has the highest performance in predicting the compressive strength of RAC after applying the hyperparameter tuning methods, with R2 and RMSE equal to 0.86 and 5.46 MPa, respectively. In addition, a sensitivity analysis was performed to determine the effect of various input parameters on the compressive strength of RAC. This indicated that the Effective Water-Cement ratio and the RAC Nominal maximum size had the most significant effect. The results show the potential of machine learning algorithms to predict the compressive strength of RAC, which can contribute to the development of more sustainable building materials.

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