Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Abstract

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators (R², R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R², R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and t_w, l_w and f ^′_c are the key parameters affecting the shear capacity of the flanged shear wall.