Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches

The application of waste materials in concrete is gaining more popularity for sustainable development. The adaptation of this approach not only reduces the environmental risks but also fulfills the requirement of concrete material. This study used the novel algorithms of machine learning (ML) to forecast the splitting tensile strength (STS) of concrete containing recycled aggregate (RA). The gene expression programming (GEP), artificial neural network (ANN), and bagging techniques were investigated for the selected database. Results reveal that the precision level of the bagging model is more accurate toward the prediction of STS of RA-based concrete as opposed to GEP and ANN models. The high value (0.95) of the coefficient of determination (R2) and lesser values of the errors (MAE, MSE, RMSE) were a clear indication of the accurate precision of the bagging model. Moreover, the statistical checks and k-fold cross-validation method were also incorporated to confirm the validity of the employed model. In addition, sensitivity analysis was also carried out to know the contribution level of each parameter toward the prediction of the outcome. The application of ML approaches for the anticipation of concrete’s mechanical properties will benefit the area of civil engineering by saving time, effort, and resources.