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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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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	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 doi:10.4203/ccp.16.4.1 purchase the full-text of this paper 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. 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 £34 +P&P)
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