This paper presents a data-driven machine learning approach of support vector regression (SVR) with genetic algorithm (GA) optimization approach called SVR-GA for predicting the shear strength capacity of medium-to ultra-high strength concrete beams with longitudinal reinforcement and vertical stirrups. 148 experimental samples collected with different geometric, material and physical factors from literature were utilized for SVR-GA with 5-fold cross validation. Shear influence factors such as the stirrup spacing, the beam width, the shear span-to-depth ratio, the effective depth of the beam, the concrete compressive and tensile strength, the longitudinal reinforcement ratio, the product of stirrup ratio and stirrup yield strength were served as input variables. The simulation results show that the predicted shear strength of SVR-GA model can achieve high accuracy based on testing set with a coefficient of determination (R2) of 0.9642, root mean squared error (RMSE) of 1.4685 and mean absolute error (MAE) of 1.0216 superior to that for traditional SVR model with 0.9379, 2.0375 and 1.4917. The sensitivity analysis reveals that the most important variables affecting the prediction of the shear strength are shear span-to-depth ratio, concrete compressive strength, reinforcement ratio and the product of stirrup ratio and stirrup yield strength. Three-dimensional input/output maps can vividly reflect the nonlinear variation of the shear strength with the two coupling variables. All in all, the proposed SVR-GA model presents an effective and accurate artificial intelligence technology for modeling the shear strength of ultra-high strength concrete beams with stirrups.

No abstract

This paper presents a data-driven machine learning approach of support vector regression (SVR) with genetic algorithm (GA) optimization approach called SVR-GA for predicting the shear strength capacity of medium-to ultra-high strength concrete beams with longitudinal reinforcement and vertical stirrups. 148 experimental samples collected with different geometric, material and physical factors from literature were utilized for SVR-GA with 5-fold cross validation. Shear influence factors such as the stirrup spacing, the beam width, the shear span-to-depth ratio, the effective depth of the beam, the concrete compressive and tensile strength, the longitudinal reinforcement ratio, the product of stirrup ratio and stirrup yield strength were served as input variables. The simulation results show that the predicted shear strength of SVR-GA model can achieve high accuracy based on testing set with a coefficient of determination (R 2 ) of 0.9642, root mean squared error (RMSE) of 1.4685 and mean absolute error (MAE) of 1.0216 superior to that for traditional SVR model with 0.9379, 2.0375 and 1.4917. The sensitivity analysis reveals that the most important variables affecting the prediction of the shear strength are shear span-to-depth ratio, concrete compressive strength, reinforcement ratio and the product of stirrup ratio and stirrup yield strength. Three-dimensional input/output maps can vividly reflect the nonlinear variation of the shear strength with the two coupling variables. All in all, the proposed SVR-GA model presents an effective and accurate artificial intelligence technology for modeling the shear strength of ultra-high strength concrete beams with stirrups.