The glass fiber reinforced polymer (GFRP) tube is an effective material that can increase the bearing capacity and ductility of concrete.To study the mechanical behavior of this composite structure,twenty-one concrete...The glass fiber reinforced polymer (GFRP) tube is an effective material that can increase the bearing capacity and ductility of concrete.To study the mechanical behavior of this composite structure,twenty-one concrete-filled GFRP tubular short columns were tested under an eccentric load.The principle influencing factors,such as the eccentricity ratio,concrete strength and ratio of longitudinal reinforcement were also studied.In addition,the course of deformation,failure mode,and failure mechanism were analyzed by observing the phenomena and summarizing the data.The test results indicated that the strength and deformation characteristics of core concrete increase as a result of the addition of the GFRP tube.However,the gain in strength due to the addition of the GFRP tube decreases as the ratio of e /d increases.An increase in the longitudinal steel ratio can improve the bearing capacity of the composite short column effectively.Furthermore,the study showed that the constraint effect of the GFRP tube on high-strength concrete is not as effective as that on common concrete.The reason is that the lateral deformation of the high-strength concrete is less than that of the common concrete when the concrete column was tested under the same axial compression ratio.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
文摘The glass fiber reinforced polymer (GFRP) tube is an effective material that can increase the bearing capacity and ductility of concrete.To study the mechanical behavior of this composite structure,twenty-one concrete-filled GFRP tubular short columns were tested under an eccentric load.The principle influencing factors,such as the eccentricity ratio,concrete strength and ratio of longitudinal reinforcement were also studied.In addition,the course of deformation,failure mode,and failure mechanism were analyzed by observing the phenomena and summarizing the data.The test results indicated that the strength and deformation characteristics of core concrete increase as a result of the addition of the GFRP tube.However,the gain in strength due to the addition of the GFRP tube decreases as the ratio of e /d increases.An increase in the longitudinal steel ratio can improve the bearing capacity of the composite short column effectively.Furthermore,the study showed that the constraint effect of the GFRP tube on high-strength concrete is not as effective as that on common concrete.The reason is that the lateral deformation of the high-strength concrete is less than that of the common concrete when the concrete column was tested under the same axial compression ratio.
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.
