scholarly journals Design Optimization of Reinforced Concrete Beams by Genetically Optimized Neural Network Technique

2020 ◽  
Vol 955 ◽  
pp. 012021
Author(s):  
Eshanya Tongper Nongsiej ◽  
Karthiga @ Shenbagam Natarajan ◽  
Saravanan Murugesan
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Design Optimization ◽  
Concrete Beams ◽  
Reinforced Concrete Beams

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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.

scholarly journals Predicting Shear Strength in FRP-Reinforced Concrete Beams Using Bat Algorithm-Based Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mohammad Nikoo ◽  
Babak Aminnejad ◽  
Alireza Lork
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Bat Algorithm ◽  
Pso Algorithm ◽  
Reinforced Concrete Beams ◽  
Feed Forward Network ◽  
Artificial Neural

In this article, 140 samples with different characteristics were collected from the literature. The Feed Forward network is used in this research. The parameters f’c (MPa), ρf (%), Ef (GPa), a/d, bw (mm), d (mm), and VMA are selected as inputs to determine the shear strength in FRP-reinforced concrete beams. The structure of the artificial neural network (ANN) is also optimized using the bat algorithm. ANN is also compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Finally, Nehdi et al.’s model, ACI-440, and BISE-99 equations were used to evaluate the models’ accuracy. The results confirm that the bat algorithm-optimized ANN is more capable, flexible, and provides superior precision than the other three models in determining the shear strength of the FRP-reinforced concrete beams.

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