Prediction of Diagonal Crack Widths of High-Strength Reinforced Concrete Beam by Artificial Neural Network

2010 ◽  
Vol 163-167 ◽  
pp. 992-997 ◽  
Author(s):  
Ji Yao ◽  
Liang Cao ◽  
Jian Feng Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beam ◽  
High Strength ◽  
Reinforced Concrete Beam ◽  
Good Effect ◽  
Artificial Neural ◽  
Diagonal Crack ◽  
Crack Widths

Based on influencing factors of the diagonal crack widths of high-strength reinforced concrete beam, the paper presents a model for predict the diagonal crack width of high-strength reinforced concrete beam by artificial neural network. The model is verified using experimental data; it indicates that the neural network model has a good effect on the forecast of the diagonal crack widths. At the same time, artificial neural network provides a new way to calculate the diagonal crack widths.

scholarly journals Optimal Design Prediction of a Singly Reinforced Concrete Beam Steel Using Artificial Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 1-3
Author(s):  
SOA Olawale ◽  
◽  
OP Akintunde ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beam ◽  
Cost Optimization ◽  
Reinforced Concrete Beam ◽  
Structural Element ◽  
Design Variables ◽  
Target Values ◽  
Artificial Neural

This paper presents the prediction of a singly reinforced concrete beam tension reinforcement design requirements using Artificial Neural Networks (ANN). The method was adopted for cost optimization of the tension reinforcement in the structural element and compared with the requirements of Eurocode 2 design. The code provisions for the design of a singly reinforced beam can vary from place to place. The use of a system immune from the code variation is an excellent means of predicting the reinforcement’s need of a rectangular concrete beam. In this work, an artificial neural network (ANN) is employed to forecast the reinforcement of such a beam. Artificial neural network has the potential to simulate the data that are hard to produce in arithmetical analysis. The scheme was established using the MATLAB tool kit. The design variables were the depth of the beam, the width of the beam, and the moments. A forward pass supervised backward propagation training. The regression analysis of the results is one to one match. The predicted and target values are completely in accord.

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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