Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance

In this study, a comprehensive dataset containing 358 data points was collected from the literature, focusing on the compressive strength, split tensile strength, and modulus of elasticity of concrete made with recycled concrete aggregate (RCA). An Artificial Neural Network was used machine to predict mechanical properties of RCA concrete. Furthermore, K-fold cross validation was utilized to validate the model's reliability, and sensitivity analysis was performed to identify the most influential input parameters among the independent variables. The model demonstrated strong performance during training, achieving R2 values of 0.93 for compressive strength, 0.92 for split tensile strength, and 0.99 for modulus of elasticity with corresponding RMSE of 2.55, 3.85, and 0.37, respectively. The MAE and MAPE values during training were 0.68 and 0.03 for compressive strength, 0.71 and 0.03 for split tensile strength, and 0.08 and 0.01 for modulus of elasticity, respectively. Testing results revealed R2 values of 0.75 for compressive strength, 0.78 for split tensile strength, and 0.67 for modulus of elasticity, with RMSE values of 8.57, 5.03, and 3.83, respectively. Moreover, the sensitivity analysis indicated that the cement percentage and water-to-cement ratio were the main input parameters which significantly influence RCA concrete strength.