Anticipation of shear strength of recycled aggregate reinforced concrete beams: a novel hybrid RF-TGC model and realistic implementation

This study proposes the hybrid machine learning model combining random forest and Taguchi optimization (RF-TGC) to predict the shear strength of recycled reinforced concrete beams (RARC). For this objective, a total of 128 experimental results of shear strength of RARC beams from published papers were used to develop the proposed RF-TGC model. The performance of the hybrid RF-TGC model was compared with the pure RF model, the k-nearest neighbour (k-NN) model, and the multiple linear regression (MLR) model based on the four indicators of the error metric: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). As a result, the hybrid RF-TGC model showed the best accuracy in predicting the shear strength of the RARC beam compared to the pure RF, k-NN and MLR models with an R² value of over 0.9 in training and a value of 0.89 in testing. In addition, the sensitivity analyses of the input parameters for the shear strength of the RARC beam were also investigated. It was found that the percentage of transverse steels is the most important parameter for predicting the shear strength of RARC beams. Finally, a free web application was developed to quickly predict the shear strength of the RARC beam in practical implementation.