CLC number: U44; U66
On-line Access: 2008-04-15
Received: 2007-10-23
Revision Accepted: 2008-01-17
Crosschecked: 0000-00-00
Cited: 2
Clicked: 6903
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.
@article{title="Calculation method of ship collision force on bridge using artificial neural network",
author="Wei FAN, Wan-cheng YUAN, Qi-wu FAN",
journal="Journal of Zhejiang University Science A",
volume="9",
number="5",
pages="614-623",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071556"
}
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%A Qi-wu FAN
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%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
A1 - Wan-cheng YUAN
A1 - Qi-wu FAN
J0 - Journal of Zhejiang University Science A
VL - 9
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EP - 623
%@ 1673-565X
Y1 - 2008
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.
[1] AASHTO (American Association of State Highway and Transportation Official), 1994. Guide Specifications and Commentary for Vessel Collision Design of Highway Bridges. Washington, D.C.
[2] Aguirrea, L.A., Alvesa, G.B, Corrêa, M.V., 2007. Steady-state performance constraints for dynamical models based on RBF networks. Engineering Applications of Artificial Intelligence, 20(7):924-935.
[3] Amdahl, J., Kavlie, D., 1992. Experimental and Numerical Simulation of Double Hull Stranding. In: Astrup, O., Veritas, D.N. (Eds.), DNV-MIT Workshop on Mechanics of Ship Collision and Grounding. Høvik, Norway, 1992.
[4] Chen, C., 2006. Study on Design Collision Force and Simulation of Damage for Bridge Subjected to Ship Impact. M.S. Thesis, Tongji University (in Chinese).
[5] Choubey, A., Sehgal, D.K., Tandon, N., 2006. Finite element analysis of vessels to study changes in natural frequencies due to cracks. International Journal of Pressure Vessels and Piping, 83(3):181-187.
[6] Consolazio, G.R., Cowan, D.R., 2003. Nonlinear analysis of barge crush behavior and its relationship to impact resistant bridge design. Computers and Structures, 81(8-11):547-557.
[7] Consolazio, G.R., ASCE, A.M., Cowan, D.R., 2005. Numerically efficient dynamic analysis of barge collisions with bridge piers. Journal of Structural Engineering, 131(8):1256-1266.
[8] ENV, 1994. 1991-1 Eurocode 1, Basis of Design and Action on Structures. Part 1, Basis of Design, CEN/CS.
[9] Fan, W., Yang, J., Liu, T.T., 2005. Grey neural network composite model and its application in landslide forecast. Yangtze River, 36(11):48-50 (in Chinese).
[10] Larsen, O.D., 1993. Ship Collision with Bridges. IABSE Structural Engineering Documents. The Interaction Between Vessel Traffic and Bridge Structures. IABSE-AIPC-IVBH, Switzerland, p.53-56.
[11] Liu, J.C., Gu, Y.N., 2002. Simulation of the whole process of ship-bridge collision. China Ocean Engineering, 16(3):369-382.
[12] Ma, G., Huang, F.L., Zeng, C.H., 2005. Identification of the impact force during ship-bridge collision on the pier of NYRB based on techniques of ANN. Journal of Vibration and Shock, 24(6):127-130 (in Chinese).
[13] Minorsky, V.U., 1959. An analysis of ship collision to protection of nuclear powered plant. Journal of Ship Research, 1:1-4.
[14] Moody, J.E, Darken, C.J., 1989. Fast learning in networks of locally-turned processing units. Neural Computation, 1(2):281-294.
[15] Pedersen, P.T., Valsgard, S., Olsen, D., Spangenberg, S., 1993. Ship impacts: Bow collision. International Journal of Impact Engineering, 13(2):163-187.
[16] Proske, D., Curbach, M., 2005. Risk to historical bridges due to ship impact on German inland waterways. Reliability Engineering and System Safety, 90(2-3):261-270.
[17] Steve, A., Billings, C., Fung, F., 1995. Recurrent radial basis function networks for adaptive noise cancellation. Neural Networks, 82:273-290.
[18] Sun, J., 2005. FEM Analysis on Ship-Bridge Collision and Anti-collision Equipment. M.S. Thesis, Tongji University, p.47-79 (in Chinese).
[19] Wang, G.H., Wu, H.G., Hu, L.H., 2006. Study on the method of bridge structural damage identification based on RBF neural network. Journal of Lanzhou Jiaotong University (Nature Sciences), 25(4):18-23.
[20] Woisin, G., 1979. Design against collision. Schiff & Hafen, 31(2):1059-1069.
[21] Yan, H.Q., 2004. FEM Simulation Analysis of Ship-Bridge Collision. M.S. Thesis, Tongji University, p.23-47 (in Chinese).
[22] Zhang, G.G., 2004. Research on Bridge Damage Detection Method Based on RBF Neural Networks. M.S. Thesis, Chang’an University, p.33-59 (in Chinese).
[23] Zhou, J.P., Yan, S.W., 2002. Artificial neural networks-based model for forecasting critical height of GRW. Chinese Journal of Geotechnical Engineering, 24(6):782-786.
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