Analysis of Scour around a Group of Vertical Piles in the Field

Bayram, Atilla; Larson, Magnus

doi:10.1061/(asce)0733-950x(2000)126:4(215)

Cited by 46 publications

(35 citation statements)

References 6 publications

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“…Figure 5 shows scour depth values using two data-mining models and the existing formulae for the same dataset. As illustrated, the methods proposed by Myrhaug & Rue (2005), Bayram & Larson (2000) and the ANN model overestimate the scour depth, while other approaches underestimate it. Table 7 illustrates the error statistics of all models.…”

Section: Comparison With Previous Modelsmentioning

confidence: 99%

“…This is because the interaction of piles and vortices decreases as the gap between piles increases. In this case, the number of piles is less important in the scour process since they act Sumer & Fredsoe (1998) and Bayram & Larson (2000) about the relative importance of the input parameters on equilibrium scour depth around pile groups due to waves. Table 8 shows that sensitivity analysis of To extend the outcomes of the study, a parametric analysis was conducted using the trained SVM and ANN models.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The equations given in this table were originally for single piles and are modified to be used for pile groups using the concept of effective diameter, D e (Bayram & Larson 2000). D e is the diameter of a hypothetical circle, having an area equal to that of a parallelogram circumscribing the pile group .…”

Section: Scour Around Pilesmentioning

confidence: 99%

“…datasets collected by Bayram & Larson (2000) in the field and by Sumer & Fredsoe (1998) in the laboratory were used. The details of these experiments are as follows.…”

Section: Dataset Usedmentioning

confidence: 99%

“…Chow & Hebrich (1986) and Sumer & Fredsoe (1998) studied various arrangements of piles in pile groups but no formula was given in their studies. Bayram & Larson (2000) studied the scour depth around pile groups in the field. Their study also showed that the equilibrium scour depth is governed by KC number.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Prediction of pile group scour in waves using support vector machines and ANN

Ghazanfari-Hashemi

Etemad‐Shahidi

Kazeminezhad

et al. 2010

Journal of Hydroinformatics

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Scour around pile groups is rather complicated and not yet fully understood due to the fact that it arises from the triple interaction of fluid-structure-seabed. In this study, two data mining approaches, i.e. Support Vector Machines (SVM) and Artificial Neural Networks (ANN), were applied to estimate the wave-induced scour depth around pile groups. To consider various arrangements of pile groups in the development of the models, datasets collected in the field and laboratory studies were used and arrangement parameters were considered in the models.Several non-dimensional controlling parameters, including the Keulegan-Carpenter number, pile Reynolds number, Shield's parameter, sediment number, gap to diameter ratio and number of piles were used as the inputs. Performances of the developed SVM and ANN models were compared with those of existing empirical methods. Results indicate that the data mining approaches used outperform empirical methods in terms of accuracy. They also indicate that SVM will provide a better estimation of scour depth than ANN (back-propagation/multi-layer perceptron). Sensitivity analysis was also carried out to investigate the relative importance of non-dimensional parameters. It was found that the Keulegan-Carpenter number and gap to diameter ratio have the greatest effect on the equilibrium scour depth around pile groups.

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Section: Comparison With Previous Modelsmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Scour Around Pilesmentioning

confidence: 99%

“…datasets collected by Bayram & Larson (2000) in the field and by Sumer & Fredsoe (1998) in the laboratory were used. The details of these experiments are as follows.…”

Section: Dataset Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of pile group scour in waves using support vector machines and ANN

Ghazanfari-Hashemi

Etemad‐Shahidi

Kazeminezhad

et al. 2010

Journal of Hydroinformatics

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show abstract

Assessment of artificial intelligence‐ based techniques for the estimation of pile group scour depth

Jafari‐Asl,

Seghier,

Panagiotis

et al. 2023

ce papers

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The scour phenomenon around piles is regarded as one of the main causes of serious damages to the pile‐supported structures such as bridges, jetties, wind turbines, and offshore platforms threatening their stability and sustainability in the long term. Thus, accurate forecast of scouring is vital for the design and operation of these structures. In this paper, three artificial intelligence‐based techniques including support vector regression, artificial neural network and random forest were applied to predict the local scour depth around pile groups. An experimental dataset is collected and used to construct the machine learning‐based models. The sediment number, shields parameter spacing, Keulegan‐Carpenter number and pile Reynolds number were used as input variables for the model development. Results assessment indicate that the artificial neural network model anticipated the highest performance among the three machine learning based models, with coefficient of determination of 0.97, and root mean square error of 0.15.

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Evaluation of the group efficiency factor for laterally loaded pile groups

Zhang,

Dai,

et al. 2024

Acta Geotech.

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Analysis of Scour around a Group of Vertical Piles in the Field

Cited by 46 publications

References 6 publications

Prediction of pile group scour in waves using support vector machines and ANN

Prediction of pile group scour in waves using support vector machines and ANN

Assessment of artificial intelligence‐ based techniques for the estimation of pile group scour depth

Evaluation of the group efficiency factor for laterally loaded pile groups

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