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Prediction of temporal scour hazard at bridge abutment

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  • Reza Mohammadpour
  • Aminuddin Ghani
  • Mohammadtaghi Vakili
  • Tooraj Sabzevari

Abstract

The scour around abutments is a major damage of bridge which appears during the flood hazard. Accurate prediction of scour depth at abutment is very essential to estimate foundation level for a cost-effective design. The accuracy of conventional method is low for prediction of temporal scour depth. However, in this study, two robust techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), were employed to estimate temporal scour depth at abutment. All experiments were conducted under clear-water conditions. Extensive data sets were collected from present and previous studies. To determine the best method, two models of ANNs, feed forward back propagation (FFBP) and radial basis function (RBF), and two kinds of ANFIS, subtractive clustering and grid partition, were investigated. The results showed that the accuracy of the FFBP with two hidden layers (RMSE = 0.011) is higher than that of RBF (RMSE = 0.055), multiple linear regression method (RMSE = 0.049) and previous empirical equations. A comparable prediction was provided by the ANFIS-grid partition method with RMSE = 0.041. This research highlights that the ANN-FFBP and ANFIS-grid partition can be successfully employed for prediction of scour hazard and reduction in bridge failure. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Reza Mohammadpour & Aminuddin Ghani & Mohammadtaghi Vakili & Tooraj Sabzevari, 2016. "Prediction of temporal scour hazard at bridge abutment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1891-1911, February.
    • Handle: RePEc:spr:nathaz:v:80:y:2016:i:3:p:1891-1911
    DOI: 10.1007/s11069-015-2044-8
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    References listed on IDEAS

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    1. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    2. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    3. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
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    Cited by:

    1. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2145-2162, December.
    2. Manish Pandey & Masoud Karbasi & Mehdi Jamei & Anurag Malik & Jaan H. Pu, 2023. "A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3745-3767, July.

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