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.

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.

Using a Multilayer Perceptrons Neural Network and Genetic Algorithm to Predict the NTD/USD Exchange Rate

2012 ◽  
Vol 542-543 ◽  
pp. 1347-1352
Author(s):  
Han Chen Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Exchange Rate ◽  
Debt Crisis ◽  
Activation Function ◽  
Multilayer Perceptrons ◽  
Optimum Number ◽  
Absolute Relative Error ◽  
Operational Risks ◽  
Exchange Rate Fluctuations

In recent years, the global community has experienced economic difficulties, such as the 2008 financial crisis and the ongoing European debt crisis. Consequently, currency values have fluctuated significantly over short periods of time, which increases the difficulty of survival and fear of businesses that rely on import and export trade. Failure to properly and appropriately address the operational risks of exchange rate fluctuations can reduce corporate profit and even lead to operational losses. However, financial markets provide numerous methods for corporations to hedge the risks of exchange rate fluctuations. Nevertheless, a model for predicting exchange rate fluctuations can enable business owners to make more appropriate judgments. This study employs a multilayer perceptions (MLP) neural network with genetic algorithm (GA) to predict the New Taiwan dollar (NTD)/U.S. dollar (USD) exchange rate. The GA is used to determine the optimum number of input and hidden nodes for a feedforward neural network, the optimum slope of the activation function, and the optimum learning rates and momentum coefficients. The empirical results show that the ability of the proposed model to predict the NTD/USD exchange rate is excellent. The absolute relative error between the predicted value and the actual value was 0.2948%, and the correlation coefficient was 0.994802.

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.

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