Abstract
Scour around bridge pier is a significant problem worldwide. The empirical formula developed so far is applicable to particular circumstances. In this paper, hybrid genetic algorithm-based artificial neural network (GA-ANN) model is employed for prediction of scour depth upstream of bridge piers and compared the results with existing empirical equations. Scour depth was modeled as a function of six parameters such as, pier length, pier width, flow velocity, flow depth, skew and median sediment size which are used as input parameter to the hybrid model. The developed hybrid model is trained and tested with data compiled from published literature. The study demonstrates that GA-ANN model can estimate scour depth with higher accuracy and wider range of situations.
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Fujail, A.K.M. (2022). Hybrid Artificial Neural Network Model for Prediction of Scour Depth Upstream of Bridge Piers. In: Kumar, R., Ahn, C.W., Sharma, T.K., Verma, O.P., Agarwal, A. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-19-0707-4_67
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DOI: https://doi.org/10.1007/978-981-19-0707-4_67
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