| NEURAL NETWORK MODELING FOR ESTIMATION OF SCOUR DEPTH AROUND BRIDGE PIERS |
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| 作者姓名： | LEE T. L. JENG D. S. ZHANG G. H. HONG J. H. |
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| 作者单位： | Department of Construction Technology Leader University，Tainan 709，China，School of Civil Engineering The University of Sydney NSW 2006 Australia |
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| 摘 要： | It is essential to predict the scour depth around bridge piers for hydraulic engineers involved in the economical design of bridge pier foundation. Conventional investigations have long been of the opinion that empirical scour prediction equations based on laboratory data over predict scour depths. In this article, the Back-Propagation Neural Network (BPN) was applied to predict the scour depth in order to overcome the problem of exclusive and the nonlinear relationships. The observations obtained from thirteen states in USA was verified by the present model. From the comparison with conventional experimental methods, it can be found that the scour depth around bridge piers can be efficiently predicted using the BPN.
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| 关 键 词： | 桥墩 反向传播神经网络 冲刷深度 水力学 |
| 收稿时间： | 8 January 2007 |
| 修稿时间： | 2007-01-082007-01-24 |
NEURAL NETWORK MODELING FOR ESTIMATION OF SCOUR DEPTH AROUND BRIDGE PIERS |
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| LEE T. L. JENG D. S. ZHANG G. H. HONG J. H..NEURAL NETWORK MODELING FOR ESTIMATION OF SCOUR DEPTH AROUND BRIDGE PIERS[J].Journal of Hydrodynamics,2007,19(3):378-386. |
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| Authors: | LEE T L JENG D S ZHANG G H HONG J H |
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| Affiliation: | 1. Materials Science Centre, Indian Institute of Technology Kharagpur, Kharagpur 721302, WB, India;2. Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, WB, India |
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| Abstract: | It is essential to predict the scour depth around bridge piers for hydraulic engineers involved in the economical design of bridge pier foundation. Conventional investigations have long been of the opinion that empirical scour prediction equations based on laboratory data over predict scour depths. In this article, the Back-Propagation Neural Network (BPN) was applied to predict the scour depth in order to overcome the problem of exclusive and the nonlinear relationships. The observations obtained from thirteen states in USA was verified by the present model. From the comparison with conventional experimental methods, it can be found that the scour depth around bridge piers can be efficiently predicted using the BPN. |
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| Keywords: | Back-Propagation Neural Network (BPN) prediction bridge pier scour depth |
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