The effects of uniform and nonuniform pile spacing variations on local scour at pile groups

Amini, Ata; Solaimani, Nina

doi:10.1080/1064119x.2017.1392658

Cited by 23 publications

(5 citation statements)

References 14 publications

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“…These results are in agreement with the findings of Ferraro et al [90] and Moreno et al [89]. It should be noted that unlike the simple piers and pile group [91], the role of the parameters at producing LSCP is various versus the pile cap level in respect to undisturbed streambed. Particularly when the pile cap is lower or inside the scour hole, the pile cap prevents scouring.…”

Section: Discussionsupporting

confidence: 92%

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

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Section: Discussionsupporting

confidence: 92%

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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“…Since the maximum equilibrium scour depth occurs in clear-water conditions, the experiments were carried out in clear water at a velocity of U=0.95Uc. More details of these experiments are available at [3,1].…”

Section: Research Datamentioning

confidence: 99%

“…Bridges play an important role in transportation. The most important factor in the collapse of the bridges is the creation and expansion of scouring [1]. Therefore, understanding the scouring mechanisms and scour depth determination around the bridges is of great importance.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers

Amini

Hamidi

Shirzadi

et al. 2019

Preprint

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Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feedforward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

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“…Francis [5] has analyzed the influencing factors on the scour depth at the piles, such as the pile diameter, sediment particle size, pile type, flow velocity, etc. From Amini's [6] expression of the pile, group scour depth, the number of pile group rows has little effect on the scouring depth. It can be approximately considered that the scouring depth coefficient of pile groups is only related to the number and spacing of the upstream columns.…”

Section: Introductionmentioning

confidence: 99%

Countermeasure of Local Scour around Offshore Wind Power Piles

Qian¹

2023

J. Phys.: Conf. Ser.

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By using the CFD (Computational Fluid Dynamics) software, the RNG (Renormalization Group) turbulence model and the mud shield module are employed to establish a three-dimensional mathematical model of scouring around a wind power pile foundation. The model reliability is verified by literature results. A new wind power pile foundation protection method is proposed. The thickness of the diaphragm protective layer is 0.5 cm, 1 cm, 2 cm, 4 cm, 6 cm, 8 cm, and 10 cm. Through the numerical simulation results, the protective effect is invalid when the thickness of the protective layer is small, however, after the thickness of the protective layer exceeds a certain value, the local scouring is more significant.

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The effects of uniform and nonuniform pile spacing variations on local scour at pile groups

Cited by 23 publications

References 14 publications

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers

Countermeasure of Local Scour around Offshore Wind Power Piles

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