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http://dx.doi.org/10.7843/kgs.2008.24.11.17

Estimation of Local Scour at Piers Using Artificial Neural Network  

Park, Hyun-Il (Research Center, Samsung E & C)
Shin, Jong-Hyun (Research Center, Samsung E & C)
Publication Information
Journal of the Korean Geotechnical Society / v.24, no.11, 2008 , pp. 17-24 More about this Journal
Abstract
It is known that scour at bridge piers is one of the leading causes of bridge failure. However, the mechanism of flow around a pier structure is so complicated that it is difficult to establish a general empirical model to provide accurate estimation for scour. Especially, each of the proposed empirical formula yields good results for a particular data set but can't show reliable predictability for various scouring data set. In this study, an alternative approach, that is, artificial neural networks (ANN), is proposed to estimate the local scour depth with numerous field data base. The local scour depth was modeled as a function of seven variables; pier shape, pier width, pier length, skew angle, stream velocity, water depth, $D_{50}$. 426 field data were used for the training and testing of ANN model. The predicted results showed that the neural network could provide a better alternative to the empirical equations.
Keywords
Artificial neural network; Local scour; Pier; Scour depth;
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