Evolutionary Polynomial Regression as an Alternative Way to Predict the Torsional Strength of Reinforced Concrete Beams

2011 ◽  
Author(s):  
A. Fiore ◽  
L. Berardi ◽  
J. Avakian ◽  
G.C. Marano
Keyword(s):  
Reinforced Concrete ◽  
Polynomial Regression ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
Torsional Strength ◽  
Evolutionary Polynomial Regression

Prediction Scheme of Torsional Strength of Reinforced Concrete Beam

2012 ◽  
Vol 214 ◽  
pp. 306-310
Author(s):  
Han Chen Huang
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Concrete Beam ◽  
Concrete Beams ◽  
Reinforced Concrete Beam ◽  
Reinforced Concrete Beams ◽  
Ann Model ◽  
Torsional Strength ◽  
Prediction Scheme ◽  
Optimal Network

This study proposes a artificial neural network with genetic algorithm (GA-ANN) for predicting the torsional strength of reinforced concrete beam. Genetic algorithm is used to the optimal network structure and parameters. A database of the torsional failure of reinforced concrete beams with a rectangular section subjected to pure torsion was obtained from existing literature for analysis. This study compare the predictions of the GA-ANN model with the ACI 318 code used for analyzing the torsional strength of reinforced concrete beam. The results show that the proposed model provides reasonable predictions of the ultimate torsional strength of reinforced concrete beams and offers superior torsion accuracy compared to that of the ACI 318-89 equation.

scholarly journals Experiment on Torsional Behaviour of Reinforced Concrete Beams

2019 ◽  
Vol 8 (6S4) ◽  
pp. 100-104
Keyword(s):  
Reinforced Concrete ◽  
Concrete Beams ◽  
Experimental Results ◽  
Reinforced Concrete Beams ◽  
Longitudinal Reinforcement ◽  
Torsional Moment ◽  
Torsional Strength

Structural beams in construction are subjected to significant torsional moment which affects the design of structures.Eight beams were produced with two distinct grade of concrete with two ratios of longitudinal as well as transverse reinforcements.An experiment for evaluation of torsional strength of reinforced concrete beams is presented in this paper.The main objective of this study is to access the role of varying percentage of transverse and longitudinal reinforcements on torsional strength of beams.Concrete grades of M 20 and M 30 beams were cast with 0.56% and 0.85% of longitudinal reinforcement as well as 50 mm and 75 mm spacing of stirrups.The experimental results are presented.The reported results include the behavioural curves and the values of torsional moment and angle of twist for entire 8 beams

Using a Hybrid Neural Network to Predict the Torsional Strength of Reinforced Concrete Beams

2012 ◽  
Vol 538-541 ◽  
pp. 2749-2753
Author(s):  
Han Chen Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
High Strength ◽  
Activation Function ◽  
Reinforced Concrete Beams ◽  
Multilayer Perceptrons ◽  
Optimum Number ◽  
Torsional Strength

This study proposes a multilayer perceptrons neural network with genetic algorithm (GA-MLP) for predicting the torsional strength of reinforced concrete beams. Genetic algorithm is used to determine the optimum number of inputs and hidden nodes of a feedforward neural network, the optimum slope of the activation function, and the optimum values of the learning rates and momentum coefficients. A database of the torsional failure of reinforced concrete (including normal-strength and high-strength concrete) beams with a rectangular section subjected to pure torsion was obtained from existing literature for analysis. We compare the predictions of the GA-MLP model with the ACI 318 code used for analyzing the torsional strength of reinforced concrete beams. We found that the proposed model provides reasonable predictions of the ultimate torsional strength of reinforced concrete beams and offers superior torsion accuracy compared to that of the ACI 318-02 equation considering both the correlation coefficient and absolute relative error.

Sign in / Sign up

Export Citation Format

Share Document