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CLC number: U44; U66

On-line Access: 2008-04-15

Received: 2007-10-23

Revision Accepted: 2008-01-17

Crosschecked: 0000-00-00

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.5 P.614~623

10.1631/jzus.A071556


Calculation method of ship collision force on bridge using artificial neural network


Author(s):  Wei FAN, Wan-cheng YUAN, Qi-wu FAN

Affiliation(s):  State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China; more

Corresponding email(s):   fanwei85@163.com

Key Words:  Ship-bridge collision force, Finite element method (FEM), Artificial neural network (ANN), Radial basis function neural network (RBFNN)


Wei FAN, Wan-cheng YUAN, Qi-wu FAN. Calculation method of ship collision force on bridge using artificial neural network[J]. Journal of Zhejiang University Science A, 2008, 9(5): 614~623.

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author="Wei FAN, Wan-cheng YUAN, Qi-wu FAN",
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%T Calculation method of ship collision force on bridge using artificial neural network
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%A Wan-cheng YUAN
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071556

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T1 - Calculation method of ship collision force on bridge using artificial neural network
A1 - Wei FAN
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071556


Abstract: 
Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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