Natural Hazards

, Volume 80, Issue 3, pp 1891–1911 | Cite as

Prediction of temporal scour hazard at bridge abutment

  • Reza Mohammadpour
  • Aminuddin Ab. Ghani
  • Mohammadtaghi Vakili
  • Tooraj Sabzevari
Original Paper


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.


Scour hazard Flood hazard Erosion Abutment scour Time variation Artificial neural networks Scour time 

List of symbols




Scour depth at time t


Equilibrium scour depth


Median size of the bed material


\({U \mathord{\left/ {\vphantom {U {\sqrt {\Delta \,gd_{50} } }}} \right. \kern-0pt} {\sqrt {\Delta \,gd_{50} } }}\) (Particles Froude number)


Gravity acceleration


Flow intensity


Coefficient of channel cross-section geometry


Coefficients of abutment alignment


Coefficients of abutment shape


Abutment length


Number of data


Observed value


Average of observed value


Predicted value


UL/ν (Abutment Reynolds number)


Time of scouring


Equilibrium time of scouring


Time when d s = 0.632 d se


Mean flow velocity


Critical velocity for the beginning of motion of bed material


ith weight of network


Normalized value of X


Neuron value


Approach flow depth


Softmax transfer function


Fluid density


Sediment density


Fluid kinematic viscosity


Geometric standard deviation


(ρ s − ρ)/ρ (Relative density)


Center of radial basis function


Radius of radial basis function

µAi(x) and µBi(x)

Membership functions



The authors would like to thank Estahban Branch, Islamic Azad University, for the financial support under the Research grant.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Reza Mohammadpour
    • 1
    • 2
  • Aminuddin Ab. Ghani
    • 2
  • Mohammadtaghi Vakili
    • 3
  • Tooraj Sabzevari
    • 1
  1. 1.Department of Civil Engineering, Estahban BranchIslamic Azad UniversityEstahbanIran
  2. 2.REDACUniversiti Sains MalaysiaNibong TebalMalaysia
  3. 3.School of Industrial TechnologyUniversiti Sains MalaysiaNibong TebalMalaysia

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