Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks

被引:152
|
作者
Asteris, Panagiotis G. [1 ]
Armaghani, Danial J. [2 ]
Hatzigeorgiou, George D. [3 ]
Karayannis, Chris G. [4 ]
Pilakoutas, Kypros [5 ]
机构
[1] Sch Pedag & Technol Educ, Computat Mech Lab, Athens, Greece
[2] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[3] Hellen Open Univ, Sch Sci & Technol, Parodos Aristotelous 18, GR-26335 Patras, Greece
[4] Democritus Univ Thrace, Dept Civil Engn, GR-67100 Xanthi, Greece
[5] Univ Sheffield, Dept Civil & Struct Engn, Sheffield, S Yorkshire, England
来源
COMPUTERS AND CONCRETE | 2019年 / 24卷 / 05期
关键词
artificial neural networks; heuristic algorithm; reinforced concrete beams; stirrups; soft computing; shear strength; FUZZY MODELING APPROACH; FRP-RC BEAMS; COMPRESSIVE STRENGTH; DESIGN PROCEDURE; SLENDER BEAMS; PART II; STIRRUPS; SURFACE; SIZE; ANN;
D O I
10.12989/cac.2019.24.5.469
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.
引用
收藏
页码:469 / 488
页数:20
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