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121211

PREDICTION OF SCOUR DEPTH AROUND BRIDGE PILES USING ARTIFICIAL NEURAL NETWORKS

Article

Last updated: 23 Jan 2023

Subjects

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Tags

Civil Engineering: structural, Geotechnical, reinforced concrete and s…nd sanitary engineering, Hydraulic, Railway, construction Management.

Abstract

The safe and economical design of bridge piles requires prediction of the maximum expected depths of scour of the stream bed around them. Scour at bridge piles may be defined as a local lowering in the bed elevation around the piles. A study of the local scour at bridge piles groups was experimentally and mathematically investigated. The case of six piles having the same diameter aligned with the flow direction in two rows altering the separation distance between the centerline of the three piles was established. The aim of this study is the investigation of the preferable separation distance between three piles to reduce scour around them to its minimum value. Artificial Neural Network (ANN) prediction models are more efficient in predictions models once they are trained from examples or patterns. These types of ANN models need large amount of data which should be at hand before thinking to develop such models. In this paper, the capability of ANN model to predict the maximum scour depth around bridge piles is investigated.

DOI

10.21608/jesaun.2009.121211

Authors

First Name

K. A.

Last Name

Amen

MiddleName

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Affiliation

Lecturer of irrigation, water construction, and water resources, Civil Engineering Dept, Assiut university, Assiut, Egypt

Email

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City

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Orcid

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First Name

Yasser

Last Name

M. R

MiddleName

-

Affiliation

Lecturer of irrigation, water construction, and water resources, Civil Engineering Dept, Assiut university, Assiut, Egypt

Email

-

City

-

Orcid

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Volume

37

Article Issue

No 2

Related Issue

16738

Issue Date

2009-03-01

Receive Date

2008-11-04

Publish Date

2009-03-01

Page Start

257

Page End

268

Print ISSN

1687-0530

Online ISSN

2356-8550

Link

https://jesaun.journals.ekb.eg/article_121211.html

Detail API

https://jesaun.journals.ekb.eg/service?article_code=121211

Order

1

Type

Research Paper

Type Code

1,438

Publication Type

Journal

Publication Title

JES. Journal of Engineering Sciences

Publication Link

https://jesaun.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

23 Jan 2023