Ahmed Sagban Saadoon; Kadhim Zuboon Nasser; Ihsan Qasim Mohamed
Abstract
In this study, a model for predicting the ultimate strength of rectangular concrete filled steel tube (RCFST) beam-columns under eccentric axial loads has been developed using artificial ...
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In this study, a model for predicting the ultimate strength of rectangular concrete filled steel tube (RCFST) beam-columns under eccentric axial loads has been developed using artificial neural networks (ANN). The available experimental results for (111) specimens obtained from open literature were used to build the proposed model. The predicted strengths obtained from the proposed ANN model were compared with the experimental values and with unfactored design strengths predicted using the design procedure specified in the AISC and Eurocode 4 for RCFST beam-columns. 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 Eurocode 4 values. As a result, ANN provided an efficient alternative method in predicting the ultimate strength of RCFST beam-columns.
