| Prediction of Axial Capacity of Concrete-Filled Square Steel Tubes Using Neural Networks |
| |
| 作者姓名： | 朱美春 王清湘 冯秀峰 |
| |
| 作者单位： | [1]State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China [2]College of Civil Engneering, Ocean University of China, Qingdao 266071, China |
| |
| 基金项目： | The Natural Science Foundation of China （ No. 50078008）. |
| |
| 摘 要： | 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.
|
| 关 键 词： | 人工神经网络 CFST 钢管 神经元 短轴 |
| 文章编号： | 1005-2429（2005）02-0151-05 |
| 收稿时间： | 2004-12-21 |
Prediction of Axial Capacity of Concrete-Filled Square Steel Tubes Using Neural Networks |
| |
| Zhu Meichun,Wang Qingxiang,Feng Xiufeng.Prediction of Axial Capacity of Concrete-Filled Square Steel Tubes Using Neural Networks[J].Journal of Southwest Jiaotong University,2005,13(2):151-155. |
| |
| Authors: | Zhu Meichun Wang Qingxiang Feng Xiufeng |
| |
| Abstract: | 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. |
| |
| Keywords: | Concrete-filled square steel tubes Neural networks Axial capacity Short columns |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
