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Prediction of temporal scour hazard at bridge abutment

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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.

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Abbreviations

b i :

Bias

d s :

Scour depth at time t

d se :

Equilibrium scour depth

d 50 :

Median size of the bed material

F d :

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

g :

Gravity acceleration

I :

Flow intensity

K G :

Coefficient of channel cross-section geometry

K θ :

Coefficients of abutment alignment

K s :

Coefficients of abutment shape

L :

Abutment length

n :

Number of data

O i :

Observed value

\(\bar{O}_{i}\) :

Average of observed value

P i :

Predicted value

R e :

UL/ν (Abutment Reynolds number)

t :

Time of scouring

t e :

Equilibrium time of scouring

T * :

Time when d s = 0.632 d se

U :

Mean flow velocity

U c :

Critical velocity for the beginning of motion of bed material

w i :

ith weight of network

X n :

Normalized value of X

x i :

Neuron value

y :

Approach flow depth

ϕ(x):

Softmax transfer function

ρ :

Fluid density

ρ s :

Sediment density

ν :

Fluid kinematic viscosity

σ g :

Geometric standard deviation

Δ:

(ρ s − ρ)/ρ (Relative density)

μ j :

Center of radial basis function

σ j :

Radius of radial basis function

µ Ai (x) and µ Bi (x) :

Membership functions

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Acknowledgments

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

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Correspondence to Reza Mohammadpour.

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Mohammadpour, R., Ghani, A.A., Vakili, M. et al. Prediction of temporal scour hazard at bridge abutment. Nat Hazards 80, 1891–1911 (2016). https://doi.org/10.1007/s11069-015-2044-8

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  • DOI: https://doi.org/10.1007/s11069-015-2044-8

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