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Proceedings of Fourth International Conference on Soft Computing for Problem Solving pp 147–157Cite as

Neuro-genetic Approach to Predict Scour Depth Around Vertical Bridge Abutment

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 335))

Abstract

Scour is caused by the erosive action of flowing water. Although, different researchers have proposed various empirical models to predict the equilibrium local scour depth around bridge abutment, these are suitable to a particular abutment condition. In this study, an integrated model that combines genetic algorithms (GA) and multilayer perceptron (MLP) network, a class of artificial neural network (ANN), is developed to estimate the scour depth around vertical bridge abutment. The equilibrium scour depth was modeled as a function of four affecting parameters of scour, abutment length, median grain size, approaching flow depth, and average approach flow velocity, and these parameters are considered as input parameter to the MLP model. The efficiency of the developed models is compared with the empirical equations over a dataset collected from literature. The MLP is found to outperform the empirical equations for the dataset considered in the present study. The performance of the best case MLP is further improved by applying GA for weight initialization. The results indicate that the GA-based MLP is more effective in terms of accuracy of predicted results and is a promising approach compared to MLP as well as the previous empirical approaches in predicting the scour depth at bridge abutments.

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Authors and Affiliations

  1. Department of Computer Science, Assam University, Silchar, Assam, India

    Abul Kashim Md Fujail & Shahin Ara Begum

  2. Department of Civil Engineering, National Institute of Technology, Silchar, Assam, India

    Abdul Karim Barbhuiya

Authors
  1. Abul Kashim Md Fujail
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  2. Shahin Ara Begum
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  3. Abdul Karim Barbhuiya
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Corresponding author

Correspondence to Shahin Ara Begum .

Editor information

Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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© 2015 Springer India

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Md Fujail, A.K., Begum, S.A., Barbhuiya, A.K. (2015). Neuro-genetic Approach to Predict Scour Depth Around Vertical Bridge Abutment. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_13

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  • DOI: https://doi.org/10.1007/978-81-322-2217-0_13

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2216-3

  • Online ISBN: 978-81-322-2217-0

  • eBook Packages: EngineeringEngineering (R0)

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