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C.-P. Tsai, Y.-T. Lee, "The Application of Neural Networks to Determine Wave Overtopping of Coastal Dikes", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 35, 2013. doi:10.4203/ccp.103.35

Keywords: back-propagation neural network, wave overtopping, surf similarity parameter.

Summary

This paper reports the applications of the back-propagation neural network (NN) to determine wave overtopping rates of sloping dikes. The wave overtopping data of sloping dikes selected from CLASH database is used for the training and testing in the NN model. This paper first configures the optimum architecture of the NN using different combinations of input factors. The test results show that the present NN model could achieve good prediction for the dimensionless wave overtopping rates of sloping dikes using three significant physical parameters; the relative freeboard, the surf similarity parameter and the relative water depth at the toe. The results show that the present NN model obtains better predictions comparing with the previous empirical formula.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 103 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis Paper 35 The Application of Neural Networks to Determine Wave Overtopping of Coastal Dikes C.-P. Tsai and Y.-T. Lee Department of Civil Engineering, National Chung Hsing University, Taichung, Taiwan doi:10.4203/ccp.103.35 purchase the full-text of this paper Full Bibliographic Reference for this paper C.-P. Tsai, Y.-T. Lee, "The Application of Neural Networks to Determine Wave Overtopping of Coastal Dikes", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 35, 2013. doi:10.4203/ccp.103.35 Keywords: back-propagation neural network, wave overtopping, surf similarity parameter. Summary This paper reports the applications of the back-propagation neural network (NN) to determine wave overtopping rates of sloping dikes. The wave overtopping data of sloping dikes selected from CLASH database is used for the training and testing in the NN model. This paper first configures the optimum architecture of the NN using different combinations of input factors. The test results show that the present NN model could achieve good prediction for the dimensionless wave overtopping rates of sloping dikes using three significant physical parameters; the relative freeboard, the surf similarity parameter and the relative water depth at the toe. The results show that the present NN model obtains better predictions comparing with the previous empirical formula. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £42 +P&P)
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