In this study, nine simplified short composite columns consisting of core CFST (concrete filled steel tube) of different diameters and outer reinforced concrete were constructed to study their compressive performance ...In this study, nine simplified short composite columns consisting of core CFST (concrete filled steel tube) of different diameters and outer reinforced concrete were constructed to study their compressive performance under axial or eccentric compression. The failure mode is characterized by the crush of the outer concrete. The bearing capacity increases at first and then decreases with further increase of the position coefficient. It can be concluded that position coefficient is an important structural parameter that has considerable influences on the ultimate bearing capacity of the composite columns. The outer concrete, steel tubes and longitudinal reinforcement are found to work in a cooperative manner under axial or eccentric compression when the position coefficient is about 0.5. An improved bearing capacity algorithm that takes the position coefficient into account has been proposed based on the experimental and simulation results and current technical specification in China. It has been proven to be precise and safe.展开更多
The application of artificial neural network to predict the ultimate bearing capacity of CFST （ concrete-filled square steel tubes） short columns under axial loading is explored. Input parameters consiste of concret...The application of artificial neural network to predict the ultimate bearing capacity of CFST （ concrete-filled square steel tubes） short columns under axial loading is explored. Input parameters consiste of concrete compressive strength, yield strength of steel tube, confinement index, sectional dimension and width-to-thickness ratio. The ultimate bearing capacity is the only output parameter. A multilayer feedforward neural network is used to describe the nonlinear relationships between the input and output variables. Fifty-five experimental data of CFST short columns under axial loading are used to train and test the neural network. A comparison between the neural network model and three parameter models shows that the neural network model possesses good accuracy and could be a practical method for predicting the ultimate strength of axially loaded CFST short columns.展开更多
基金Funded by the National Natural Science Foundation of China(Grant No. 51178119)
文摘In this study, nine simplified short composite columns consisting of core CFST (concrete filled steel tube) of different diameters and outer reinforced concrete were constructed to study their compressive performance under axial or eccentric compression. The failure mode is characterized by the crush of the outer concrete. The bearing capacity increases at first and then decreases with further increase of the position coefficient. It can be concluded that position coefficient is an important structural parameter that has considerable influences on the ultimate bearing capacity of the composite columns. The outer concrete, steel tubes and longitudinal reinforcement are found to work in a cooperative manner under axial or eccentric compression when the position coefficient is about 0.5. An improved bearing capacity algorithm that takes the position coefficient into account has been proposed based on the experimental and simulation results and current technical specification in China. It has been proven to be precise and safe.
文摘The application of artificial neural network to predict the ultimate bearing capacity of CFST （ concrete-filled square steel tubes） short columns under axial loading is explored. Input parameters consiste of concrete compressive strength, yield strength of steel tube, confinement index, sectional dimension and width-to-thickness ratio. The ultimate bearing capacity is the only output parameter. A multilayer feedforward neural network is used to describe the nonlinear relationships between the input and output variables. Fifty-five experimental data of CFST short columns under axial loading are used to train and test the neural network. A comparison between the neural network model and three parameter models shows that the neural network model possesses good accuracy and could be a practical method for predicting the ultimate strength of axially loaded CFST short columns.
