Abstract
The analysis of moment-curvature relationship of reinforced concrete sections is complex due to large number of variables as well as non-linear material behavior involved. Artificial Neural Networks (ANNs) are found to be a tool capable of solving such problems. This has led to increasing use of ANN for analyzing the behavior of reinforced concrete sections. This paper reports the details of a study conducted using ANN for predicting moment-curvature relationship of a reinforced concrete section. Using data generated based on the analytical solutions, the ANN model was trained. The trained model was tested for a different set of input parameters and the output values were compared with the values based on analytical results. The agreement was found to be good.
Highlights
The moment curvature for a cross-section envelope describes the changes in force capacity with deformation during a nonlinear analysis
A back-propagation neural network model was employed to predict the influence of various parameters on the behavior of reinforced concrete sections
A neural network model was applied to the data derived from the analytical solutions
Summary
The moment curvature for a cross-section envelope describes the changes in force capacity with deformation during a nonlinear analysis. To obtain the moment-curvature relationship of reinforced concrete section, various researchers have investigated using different models. Ersoy and Özcebe [2] presented a computer program to determine moment-curvature relationships of confined concrete sections. The generalized delta rule algorithm of artificial neural networks is employed to predict the flexural behavior of Steel Fibre Reinforced Concrete (SFRC) T-beams using a computer program developed using C++ by Patodi and Purani [3]. The behavior values of reinforced concrete sections subjected to flexure and axial load were obtained by using an analytical solution named the filament model, and the required data for the network training were prepared. To obtain the behavior of confined concrete, several data points were used in training a multi-layer, feed-forward and back propagation artificial neural network (ANN) algorithm. The reliability of the ANN solution was validated by comparing experimental values with modeled values
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