A Neural Network Model to Predict Ultimate Strength of Rectangular Concrete Filled Steel Tube Beam – Columns | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 30, Issue 19, November 2012, Pages 3328-3340 PDF (323.05 K) | ||
| DOI: 10.30684/etj.30.19.4 | ||
| Authors | ||
| 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 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. | ||
| Keywords | ||
| beam; columns; artificial neural networks; Concrete Filled Steel Tube | ||
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