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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 16
NEURAL NETWORKS & COMBINATORIAL OPTIMIZATION IN CIVIL & STRUCTURAL ENGINEERING
Edited by: B.H.V. Topping and A.I. Khan
Paper IV.1

Prediction of Maximum Scour Depth at Spur Dikes with Adaptive Neural Networks

X. Wu and S.Y. Lim

School of Civil & Structural Engineering, Nanyang Technological University, Singapore

Full Bibliographic Reference for this paper
X. Wu, S.Y. Lim, "Prediction of Maximum Scour Depth at Spur Dikes with Adaptive Neural Networks", in B.H.V. Topping, A.I. Khan, (Editors), "Neural Networks & Combinatorial Optimization in Civil & Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 61-66, 1993. doi:10.4203/ccp.16.4.1
Abstract
This paper describes the application of multilayer feedforward neural networks in correlating the maximum local scour depth at a spur dike with all the major affecting factors, using published laboratory flume data. For both uniform and non-uniform sediments, neural network based models for predicting the maximum local scour depth in clear water and live bed scour conditions are constructed. A composite model that uses the combined data on clear water and live bed scour is also established. Predictions of the maximum local scour depth from the neural network models are reasonably accurate compared with the experimental observations in both training and testing cases. This neural network based approach has the advantage of establishing correlations directly from the experimental data through learning without making assumptions and simplifications on the relationships.

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