Use of Neural Networks to Predict Ultimate Strength of Circular Concrete Filled Steel Tube Beam-Columns

Abstract

Artificial neural networks (ANNs) are useful computing system which can be trained to
learn complex relationship between two or more variables. It learns from examples and
storage the knowledge for future use. In this study, a model for predicting the ultimate
strength of circular concrete filled steel tube (CCFST) beam-columns under eccentric axial
loads has been developed in ANN. The available experimental results for 181 specimens
obtained from previous studies were used to build the proposed model. The predicted
strengths obtained from the proposed ANN model were compared with the experimental
values and current design provision for CCFST beam-columns (AISC and Eurocode4).
Results showed that the predicted values by the proposed ANN model were very close to the
experimental values and were more accurate than the AISC and Eurocode4 values. As a
result, ANN provided an efficient alternative method in predicting the ultimate strength of
CCFST beam-columns