Neural Network assessment for scour depth around bridge piers (No. R855)
Access status:
Open Access
Type
Report, ResearchAbstract
The mechanism of flow around a pier structure is so complicated that, it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. In this study, ...
See moreThe mechanism of flow around a pier structure is so complicated that, it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. In this study, an alternative approach, artificial neural networks (ANN), is proposed to estimate the equilibrium and timedependent scour depth with numerous reliable data base. Numerous ANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis using orthogonal least-squares algorithm (RBF/OLS), Bayesian neural Network (BNN) and single artificial Neural Network (SANN) were used. The equilibrium scour depth was modeled as a function of five variables; flow depth, mean velocity, critical flow velocity, mean grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The training and testing data are selected from the experimental data of several valuable references.
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See moreThe mechanism of flow around a pier structure is so complicated that, it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. In this study, an alternative approach, artificial neural networks (ANN), is proposed to estimate the equilibrium and timedependent scour depth with numerous reliable data base. Numerous ANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis using orthogonal least-squares algorithm (RBF/OLS), Bayesian neural Network (BNN) and single artificial Neural Network (SANN) were used. The equilibrium scour depth was modeled as a function of five variables; flow depth, mean velocity, critical flow velocity, mean grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The training and testing data are selected from the experimental data of several valuable references.
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Date
2005Publisher
School of Civil Engineering, The University of SydneyLicence
Copyright All Rights ReservedRights statement
This publication may be redistributed freely in its entirety and in its original form without the consent of the copyright owner. Use of material contained in this publication in any other published works must be appropriately referenced, and, if necessary, permission sought from the author.Faculty/School
Faculty of Engineering, School of Civil EngineeringDepartment, Discipline or Centre
Centre for Advanced Structural EngineeringShare