Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with li...Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.展开更多
In order to further improve the seismic performance of RC shear walls, a new composite shear wall with concrete filled steel tube （CFT） columns and concealed steel trusses is proposed. This new shear wall is a doubl...In order to further improve the seismic performance of RC shear walls, a new composite shear wall with concrete filled steel tube （CFT） columns and concealed steel trusses is proposed. This new shear wall is a double composite shear wall; the first composite being the use of three different force systems, CFT, steel truss and shear wall, and the second the use of two different materials, steel and concrete. Three 1/5 scaled experimental specimens： a traditional RC shear wall, a shear wall with CFT columns, and a shear wall with CFT columns and concealed steel trusses, were tested under cyclic loading and the seismic performance indices of the shear walls were comparatively analyzed. Based on the data from these experiments, a thorough elastic-plastic finite element analysis and parametric analysis of the new shear walls were carried out using ABAQUS software. The finite element results of deformation, stress distribution, and the evolution of cracks in each phase were compared with the experimental results and showed good agreement. A mechanical model was also established for calculating the load-carrying capacity of the new composite shear walls. The results show that this new type of shear wall has improved seismic performance over the other two types of shear wails tested.展开更多
基金Acknowledgements This work was sponsored by the National Natural Science Foundation of China （Grant No. 61272264）.
文摘Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.
基金Science and Technology Key Project of Beijing Under Grant No.D0905060370000National Natural Science Foundation of China Under Grant No.50878007+1 种基金Project High-level Personnel in Beijing Under Grant No.PHR20100502the Scientific and Technological Planning of Beijing Key Project Education Commission Under Grant No.KZ200910005008
文摘In order to further improve the seismic performance of RC shear walls, a new composite shear wall with concrete filled steel tube （CFT） columns and concealed steel trusses is proposed. This new shear wall is a double composite shear wall; the first composite being the use of three different force systems, CFT, steel truss and shear wall, and the second the use of two different materials, steel and concrete. Three 1/5 scaled experimental specimens： a traditional RC shear wall, a shear wall with CFT columns, and a shear wall with CFT columns and concealed steel trusses, were tested under cyclic loading and the seismic performance indices of the shear walls were comparatively analyzed. Based on the data from these experiments, a thorough elastic-plastic finite element analysis and parametric analysis of the new shear walls were carried out using ABAQUS software. The finite element results of deformation, stress distribution, and the evolution of cracks in each phase were compared with the experimental results and showed good agreement. A mechanical model was also established for calculating the load-carrying capacity of the new composite shear walls. The results show that this new type of shear wall has improved seismic performance over the other two types of shear wails tested.
