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| Neural network modeling of bearing capacity of axially loaded concrete-filled square steel tubular short columns |
| Revised:April 12, 2004 |
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| DOI：10.7511/jslx20063065 |
| KeyWord:concrete-filled square steel tubes,neural networks,bearing capacity,short columns |
| ZHU Mei-chun WANG Qing-xiang FENG Xiu-feng |
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| Abstract: |
| Due to the complexity of the confinement mechanism in concrete-filled square steel tubes(CFST),there is still no unified method for calculating the bearing capacity of CFST columns.The application of artificial neural network to predict the ultimate bearing capacity of CFST short columns under axial loading is explored.Input parameters consisted of concrete compressive strength,yield strength of steel tube,confinement index,sectional dimension and width-tothickness ratio.The ultimate bearing capacity was the only output parameter.A multi-layer feed-forward neural network was used to describe the nonlinear relationships between the input and output variables.Fifty-five experimental data of CFST short columns under axial loading were used to train and test the neural network.A comparison study between the neural network model and three analytical models was also carried out.The study shows that neural network model possesses good accuracy and it can be a new method for predicting the ultimate strength of axially loaded CFST short columns. |
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