Temporal Variation of Scour Around Circular Bridge Piers

Kothyari, Umesh C.; Garde, Ram Chandra J.; Raju, K. G. Ranga

doi:10.1061/(asce)0733-9429(1992)118:8(1091)

Cited by 230 publications

(71 citation statements)

References 5 publications

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“…The current study used 13 sets of data to predict equilibrium scour depth: Chabert and Engeldinger (1956), Hancu (1971), Ettema (1980), Jain and Fischer (1980), Chee (1982), Chiew (1984), Kothyari et al (1992), Yanmaz and Altinbilek (1991), Graf (1995), Melville (1997), Melville and Chiew (1999), Oliveto and Hager (2002) and unpublished data from the University of Auckland. The whole data set consisting of 263 data points which was divided into two parts randomly-a training or calibration set consisting 180 data points and a validation or testing set consisting of 83 data points.…”

Section: Development Of Ann and Anfis Modelsmentioning

confidence: 99%

“…Besides that, the boundary layer upstream of the pier undergoes a three-dimensional separation. The shear stress distribution around the pier is drastically changed due to the formation of a horseshoe vortex, resulting in the formation of a scour hole around the pier, which, in turn, changes the flow pattern and shear (Kothyari et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Bateni

Borghei

Jeng

2007

Engineering Applications of Artificial Intelligence

186

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Section: Development Of Ann and Anfis Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Bateni

Borghei

Jeng

2007

Engineering Applications of Artificial Intelligence

186

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“…The most promising research approaches for studying the scour process are certainly those that model the temporal evolution of the scour hole (Franzetti et al 1989;Kothyari et al 1992;Briaud et al 1999;Oliveto & Hager 2002;Mia & Nago 2003;Brandimarte et al 2006) and, for real-world applications, the effect of variable streamflow should be taken into account: in fact, the time needed to get to an equilibrium scour depth may be extremely long in comparison with the short duration of the scour-generating flood events (characterized by high flow depths and velocities), thus leading to the risk of overestimating the actual erosion in unsteady flow conditions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks

Toth

Brandimarte

2011

Journal of Hydroinformatics

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The scouring effect of the flowing water around bridge piers may undermine the stability of the structure, leading to extremely high direct and indirect costs and, in extreme cases, the loss of human lives. The use of Artificial Neural Network (ANN) models has been recently proposed in the literature for estimating the maximum scour depth around bridge piers: this study aims at further investigating the potentiality of the ANN approach and, in particular, at analysing the influence of the experimental setting (laboratory or field data) and of the sediment transport mode (clear water or live bed) on the prediction performances. A large database of both field and laboratory observations has been collected from the literature for predicting the maximum local scour depth as a function of a parsimonious set of variables characterizing the flow, the sediments and the pier. Neural networks with an increasing degree of specialization have been implemented -using different subsets of the calibration data in the training phase -and validated over an external validation dataset. The results confirm that the ANN scour depths' predictions outperform the estimates obtained by empirical formulae conventionally used in the literature and in the current engineering practice, and demonstrate the importance of taking into account the differences in the type of available datalaboratory or field data -and the sediment transport mode -clear water or live bed conditions.

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“…The main factors in erosion of river bed are vortex system, downward flow and turbulence as well (Kothyari et al, 1992). At upstream face of the abutment, at stagnation point, the approach flow velocity reaches zero, which causes an increase in pressure.…”

Section: Physics Of Scouringmentioning

confidence: 99%

Prediction of time-varying maximum scour depth around short abutments using soft computing methodologies - A comparative study

et al. 2015

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Maximum depth of scouring around bridge abutments is a significant criterion in design of safe depth for abutment foundation. Many studies done on maximum scouring depth are limited to specific shape of abutment and there is no general equation in the estimation of time varying scour depth at short abutments. In this research, maximum depth and also the temporal variation of local scour at a vertical-wall, wing-wall and semicircular abutments, and spur dike as well, were investigated experimentally. About 1400 sets of experimental data were collected. The results indicated that 70-90% of the equilibrium scouring depths occurred during the first 20% of overall time of scouring tests. According to the collected data, Multiple Nonlinear Regression (MNLR), Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were adopted to predict the time variation of scour depth around abutments. The computer models were compared to the other empirical equations presented in the literature. The study showed that the conducted regression model is rather precise and practical (R 2 = 0.88). Also, the results of the numerical modeling indicated that ANFIS model produced the best results (R 2 = 0.98) in comparison with ANN models using feed forward back propagation (R 2 =0.96) and radial basis function (R 2 = 0.94).

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Temporal Variation of Scour Around Circular Bridge Piers

Cited by 230 publications

References 5 publications

Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks

Prediction of time-varying maximum scour depth around short abutments using soft computing methodologies - A comparative study

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