Prediction of backwater level of bridge constriction using an artificial neural network

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Tarih

2013

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

ICE PUBLISHING

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Bridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (n(mc)) and of the floodplain (n(fp)), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of n(mc), n(fp), b and Q on the backwater level due to a bridge constriction without piers. It is concluded that n(mc) and Q have a more profound effect on the backwater level than does n(fp), while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn.

Açıklama

Anahtar Kelimeler

bridges, floods & floodworks, mathematical modelling

Kaynak

PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-WATER MANAGEMENT

WoS Q Değeri

Q2

Scopus Q Değeri

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Cilt

166

Sayı

10

Künye