Interpretable machine learning model for evaluating mechanical properties of concrete made with recycled concrete aggregate

Abstract The main objective of this paper is to use the data‐driven approach to predict and evaluate the mechanical properties of concrete made with recycled concrete aggregate (RCA), including compressive strength and elastic modulus. Using 358 data samples, including 10 input variables, 10 popular machine learning (ML) algorithms are introduced to select the best ML performance model for predicting RCA concrete's compressive strength and elastic modulus. Gradient Boosting and Categorial Boosting have the best performance in predicting the compressive strength of RCA concrete, with R 2 = 0.9112, RMSE = 5.3464 MPa, MAE = 4.0845 MPa, and R 2 = 0.9175, RMSE = 5.1520 MPa, MAE = 3.7567 MPa, respectively. Light Gradient Boosting and Categorial Boosting have the best performance in predicting the elastic modulus of RCA concrete, with R 2 = 0.8775, RMSE = 2.3560 GPa, MAE = 1.8330 GPa, and R 2 = 0.9300, RMSE = 2.3560 MPa, MAE = 1.2589 MPa, respectively. Based on the Shapley Additive Explanation analysis, the influence of main factors on compressive strength and elastic modulus of RCA concrete values has been analyzed qualitatively and quantitatively. RCA replacement level and cement/sand ratio slightly affect compressive strength but have a dominant influence on the elastic modulus of RCA concrete.