Design shear strength formula for high strength concrete beams

This paper concerns the incorporation of steel fibres in singly reinforced high strength concrete beams without stirrups failing under the combined effect of flexure and shear. A new equation for predicting the shear strength of reinforced high strength concrete beams is developed. This equation shows a good correlation with own test data of 16 reinforced high strength concrete beams with and without steel fibres and numerous published experimental data for beams with concrete compressive strength up to 140 MPa. The flexural capacity of reinforced high strength concrete beams is also investigated. The existing ACI method for predicting the flexural strength of steel fibre concrete composites is slightly modified to be applicable for high strength concrete. Based on the proposed equations, an analytical model is developed for predicting the relative flexural capacity, i.e., the ratio of the moment with shear interaction to the pure flexural moment. Key words: high strength concrete, steel fibre, shear strength, flexural strength, stirrups, web reinforcement.

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Prediction of shear strength in high-strength concrete beams considering size effect

Magazine of Concrete Research ◽

10.1680/macr.2006.58.4.193 ◽

2006 ◽

Vol 58 (4) ◽

pp. 193-200 ◽

Cited By ~ 3

Author(s):

Y.-H. Bae ◽

J.-H. Lee ◽

Y.-S. Yoon

Keyword(s):

Shear Strength ◽

Size Effect ◽

High Strength Concrete ◽

Concrete Beams ◽

High Strength ◽

Strength Concrete

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Modeling Shear Strength of Medium- to Ultra-High-Strength Concrete Beams with Stirrups using SVR and Genetic Algorithm

10.21203/rs.3.rs-338840/v1 ◽

2021 ◽

Author(s):

Chun-song Jiang ◽

Gui-Qin Liang

Keyword(s):

Genetic Algorithm ◽

Shear Strength ◽

Yield Strength ◽

High Strength Concrete ◽

Concrete Beams ◽

High Strength ◽

Reinforcement Ratio ◽

Longitudinal Reinforcement ◽

Strength Concrete ◽

Shear Span

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 (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.

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