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C.-P. Tsai, T.-J. Chang, C. Lin, "Prediction of Scour Depth in front of a Seawall using Neural Networks", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 286, 2015. doi:10.4203/ccp.108.286

Keywords: toe scour, sloped seawall, back-propagation neural network, steep slope, mild slope.

Summary

The physical factors influencing toe scours at sloped seawalls are very complicated. This study employs the technique of back-propagation neural networks to estimate the scour depth at the toe of a seawall using relative physical parameters based on experimental data. The results show that the neural network can predict satisfactorily the normalized toe scour depth using four major inputs: the relative water depth, the incident wave steepness, the beach slope, and the seawall slope. A more desirable prediction could be obtained if the data of beach slopes were classified into the steep and the mild slopes, respectively, as a result of their slightly different mechanisms for toe scour. Compared with the previous empirical formulas, the present neuaral network model obtains better predictions.

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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: 108 PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping Paper 286 Prediction of Scour Depth in front of a Seawall using Neural Networks C.-P. Tsai, T.-J. Chang and C. Lin Department of Civil Engineering, National Chung Hsing University, Taichung, Taiwan doi:10.4203/ccp.108.286 purchase the full-text of this paper Full Bibliographic Reference for this paper C.-P. Tsai, T.-J. Chang, C. Lin, "Prediction of Scour Depth in front of a Seawall using Neural Networks", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 286, 2015. doi:10.4203/ccp.108.286 Keywords: toe scour, sloped seawall, back-propagation neural network, steep slope, mild slope. Summary The physical factors influencing toe scours at sloped seawalls are very complicated. This study employs the technique of back-propagation neural networks to estimate the scour depth at the toe of a seawall using relative physical parameters based on experimental data. The results show that the neural network can predict satisfactorily the normalized toe scour depth using four major inputs: the relative water depth, the incident wave steepness, the beach slope, and the seawall slope. A more desirable prediction could be obtained if the data of beach slopes were classified into the steep and the mild slopes, respectively, as a result of their slightly different mechanisms for toe scour. Compared with the previous empirical formulas, the present neuaral network model obtains better predictions. 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 £75 +P&P)
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