所属单位：土木与建筑工程学院

教研室：力学教研室

发表刊物：Soft Computing

摘要：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.

合写作者：Gui-Qin Liang

第一作者：Chun-Song Jiang*

论文类型：期刊论文

论文编号：4a41df957a504670017a9dd590db6f5a

是否译文：否

发表时间：2021-07-12