Experimental results of new type joints between the column and the. steel beam of concrete-filled rectangular steel tubular （CFRT） under reversed cyclic loads are presented. The earthquake resistant capacity of the ...Experimental results of new type joints between the column and the. steel beam of concrete-filled rectangular steel tubular （CFRT） under reversed cyclic loads are presented. The earthquake resistant capacity of the joint is influenced by infilled concrete, stiffener length and relative dimensions of column and beam. It is found that the hysteresis curves obtained in the experiment are full and the joints have a good energy dissipation capacity. The nonlinear finite element models are also used to analyze the hysteresis behavior of the joints under reversed cyclic loads using ANSYS 8.0. The influences of the stiffener length and the infilled concrete are analyzed. Analytical results show that the stiffener length and the infilled concrete are critical for the joints. Furthermore, the skeleton curves of the finite element models are in good agreement with those of experiments.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Supprorted by the Science and Technology Foundation of Jiangsu Construction Committee(JS200214)the Science Research Foundation of Nanjing Institute of Technology(KXJ08122)~~
文摘Experimental results of new type joints between the column and the. steel beam of concrete-filled rectangular steel tubular （CFRT） under reversed cyclic loads are presented. The earthquake resistant capacity of the joint is influenced by infilled concrete, stiffener length and relative dimensions of column and beam. It is found that the hysteresis curves obtained in the experiment are full and the joints have a good energy dissipation capacity. The nonlinear finite element models are also used to analyze the hysteresis behavior of the joints under reversed cyclic loads using ANSYS 8.0. The influences of the stiffener length and the infilled concrete are analyzed. Analytical results show that the stiffener length and the infilled concrete are critical for the joints. Furthermore, the skeleton curves of the finite element models are in good agreement with those of experiments.
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.
