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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

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.

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