The seismic ductility of reinforced very-high-strength-concrete （VHSC） short columns was studied by combinatively applying axial load and low cyclic lateral load on specimens to simulate seismic impact. Twelve speci...The seismic ductility of reinforced very-high-strength-concrete （VHSC） short columns was studied by combinatively applying axial load and low cyclic lateral load on specimens to simulate seismic impact. Twelve specimens with concrete compressive strength ranging from 95.6 MPa to 118.6 MPa and a shear-span ratio of 2.0 were tested for shear failure pattern and fear force-displacement hysteretic responses. Combinative application of axial load and low cyclic lateral load to VHSC short columns incurs shear failure. The displacement ductility is much smaller when the axial load ratio is larger; whereas a larger stirrup ratio is accompanied with a better displacement ductility. The relationship of displacement ductility factor,μ△, with stirrup characteristic value, λv, and test axial load ratio, nt, is μ△=（1＋8λv）/（0.33＋nt）. By this relationship and relevant codes for aseismatic design, the axial load ratio limits for aseismatic design of reinforced VHSC （C95 to C100） short columns for frame construction are respectively 0.5, 0.6, and 0.7 for seismic classes Ⅰ, Ⅱ, and Ⅲ; corresponding minimum characteristic values of stirrups are calculated according to the required characteristic values of at least 1.273 times of experimental results. These data are very useful to aseismatic engineering.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the key project of the National Natural Science Foundation of China (No.50438010)
文摘The seismic ductility of reinforced very-high-strength-concrete （VHSC） short columns was studied by combinatively applying axial load and low cyclic lateral load on specimens to simulate seismic impact. Twelve specimens with concrete compressive strength ranging from 95.6 MPa to 118.6 MPa and a shear-span ratio of 2.0 were tested for shear failure pattern and fear force-displacement hysteretic responses. Combinative application of axial load and low cyclic lateral load to VHSC short columns incurs shear failure. The displacement ductility is much smaller when the axial load ratio is larger; whereas a larger stirrup ratio is accompanied with a better displacement ductility. The relationship of displacement ductility factor,μ△, with stirrup characteristic value, λv, and test axial load ratio, nt, is μ△=（1＋8λv）/（0.33＋nt）. By this relationship and relevant codes for aseismatic design, the axial load ratio limits for aseismatic design of reinforced VHSC （C95 to C100） short columns for frame construction are respectively 0.5, 0.6, and 0.7 for seismic classes Ⅰ, Ⅱ, and Ⅲ; corresponding minimum characteristic values of stirrups are calculated according to the required characteristic values of at least 1.273 times of experimental results. These data are very useful to aseismatic engineering.
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.
