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. One hundred and forty eight experimental samples collected with different geometric, material and physical factors from literature were utilized for SVR-GA with fivefold 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 SVR-GA model can achieve highest accuracy in shear strength prediction based on testing set with a coefficient of determination (R²) of 0.9642, root mean squared error of 1.4685 and mean absolute error of 1.0216 superior to that for traditional SVR model with 0.9379, 2.0375 and 1.4917, which both perform better than multiple linear regression and ACI-318. Furthermore, the sensitivity analysis reveals the most important variables affecting the result of shear strength prediction 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 are employed to reflect the nonlinear variation of the shear strength with the two coupling variables. All in all, the proposed SVR-GA model can achieve excellent accuracy in prediction the shear strength of medium- to ultra-high strength concrete beams with stirrups in comparison with results obtained by traditional SVR, MLP and ACI-318.

本文提出了一种数据驱动的支持向量回归 (SVR) 机器学习方法和遗传算法 (GA) 优化方法，称为 SVR-GA，用于预测具有纵向配筋和竖向的中至超高强度混凝土梁的抗剪强度能力。马镫。从文献中收集的具有不同几何、材料和物理因素的 148 个实验样品用于 SVR-GA，并进行五重交叉验证。箍筋间距、梁宽、剪跨深度比、梁的有效深度、混凝土抗压和抗拉强度、纵向配筋率、箍筋比与箍筋屈服强度的乘积等剪切影响因素被用作输入变量。[R ²) 为 0.9642，均方根误差为 1.4685，平均绝对误差为 1.0216，优于传统 SVR 模型的 0.9379、2.0375 和 1.4917，均优于多元线性回归和 ACI-318。此外，敏感性分析揭示了影响抗剪强度预测结果的最重要变量是剪跨深度比、混凝土抗压强度、配筋率以及箍筋比和箍筋屈服强度的乘积。采用三维输入/输出图来反映剪切强度随两个耦合变量的非线性变化。总而言之，与传统 SVR 获得的结果相比，所提出的 SVR-GA 模型在预测中到超高强度带箍筋的混凝土梁的抗剪强度方面可以达到出色的精度，