Prediction method of ship impacting bridge based on least square support vector machines

Luo Wei-lin; Zou Zao-jian

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Luo Wei-lin, Zou Zao-jian. Prediction method of ship impacting bridge based on least square support vector machines[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 30-33.

Citation:

Luo Wei-lin, Zou Zao-jian. Prediction method of ship impacting bridge based on least square support vector machines[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 30-33.

Luo Wei-lin, Zou Zao-jian. Prediction method of ship impacting bridge based on least square support vector machines[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 30-33.

Citation:

Luo Wei-lin, Zou Zao-jian. Prediction method of ship impacting bridge based on least square support vector machines[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 30-33.

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Prediction method of ship impacting bridge based on least square support vector machines

Luo Wei-lin^1
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Zou Zao-jian^{1, 2
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1.
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200030, China
2.
State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200030, China

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Author Bio:
Luo Wei-lin (1973-), male, doctoral student of engineering, +86-21-62933645, wlluo@sjtu.edu.cn

Zou Zao-jian (1956-), male, EngD, professor, +86-21-62933645, zjzou@sjtu.edu.cn
Received Date: 2007-01-13
Publish Date: 2007-08-25

Abstract

Abstract

In order to improve the safety of bridge and ship, an intelligent prediction method of the probability on ship impacting bridge was proposed based on least square support vector machines.The transverse span of piers, current velocity, current direction relative to bridge and waterway curvature were taken as the inputs of the method, while the output is the accident probability of pervoyage, some data samples of 12 bridges over Yangtze River and Heilongjiang River during the periods of flood, normal and low water respectively were chosen, the method was validated by the data, and the prediction results of neural network method and the proposed method were compared.Comparison result shows that the method can accurately predict the probability of ship impacting bridge, its prediction probability is a globally optimal resolution, and its convergent velocity and learning efficiency are better than that of neural network method.
- ship impacting bridges,
- probability analysis,
- prediction of collision,
- support vector machines

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References(13)

References

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