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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blench T (1962) Discussion of scour at bridge crossings. In: Laursen EM (eds) Transactions of American society of civil engineers, Vol. 127, pp 180–183
Lee SO, Sturm TW (2009) Effect of sediment size scaling on physical modeling of bridge pier scour. J Hydraul Eng 135(10):793–802
Neill CR (1964) River-bed scour—a review for engineers. Canadian Good Roads Association Technical Publication No. 23, Ottawa, Canada
Melville BW, Sutherland AJ (1988) Design method for local scour at bridge piers: American society of civil engineering. J Hydraul Div 114(10):1210–1225
Melville BW, Chiew Y (1999) Time scale for local scour at bridge piers. J Hydraul Eng 125(1):59–65
Kothyari UC, Garde RJ, Ranga Raju KG (1992) Temporal variation of scour around circular bridge piers. J Hydraul Eng 118(8):1091–1106
Lee TL, Jeng DS, Zhang GH, Hong JH (2007) Neural network modeling for estimation of scour depth around bridge piers. J Hydrodyn 19(3):378–386
Azamathulla HM, Deo MC, Deolalikar PB (2008) Alternative neural networks to estimate the scour below spillways. Adv Eng Softw 39:689–698
Toth E, Brandimarte L (2011) Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks. J Hydroinf 13(4):812–824
Chou J-S, Pham AD (2014) Hybrid computational model for predicting bridge scour depth near piers and abutments. Autom Constr 48:88–96
Mueller DS, Wagner CR (2005) Field observations and evaluations of streambed scour at bridges, Federal Highway Administration, U.S. Department of Transportation, Publication No. FHWA-RD-03–052
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-19-0707-4_67
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0706-7
Online ISBN: 978-981-19-0707-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)