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Prediction of scour below submerged pipeline crossing a river using ANN

机译:基于人工神经网络的淹没式穿越河道冲刷预测

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摘要

The process involved in the local scour below pipelines is so complex that it makes it difficult tonestablish a general empirical model to provide accurate estimation for scour. This paper describesnthe use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets ofnlaboratory measurements were collected from published works and used to train the network ornevolve the program. The developed networks were validated by using the observations that were notninvolved in training. The performance of ANN was found to be more effective when compared withnthe results of regression equations in predicting the scour depth around pipelines.
机译:管道下方局部冲刷涉及的过程非常复杂,以至于很难建立一个通用的经验模型来提供准确的冲刷估算。本文介绍了使用人工神经网络(ANN)估算管道冲刷深度的方法。实验室测量的数据集是从已发表的作品中收集的,用于训练网络或使程序发展。通过使用与训练无关的观察结果验证了开发的网络。与回归方程的结果相比,在预测管道周围的冲刷深度时,ANN的性能更为有效。

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  • 来源
    《Water Science and Technology》 |2011年第10期|p.2225-2230|共6页
  • 作者单位

    H. Md. Azamathulla (corresponding author)River Engineering and Urban Drainage ResearchCentre (REDAC),Universiti Sains Malaysia,Engineering Campus, Seri Ampangan,14300 Nibong Tebal, Pulau Pinang,MalaysiaE-mail: redacazamath@eng.usm.my,mdazmath@gmail.comNor Azazi ZakariaREDAC, Universiti Sains Malaysia,Engineering Campus, Seri Ampangan,14300 Nibong Tebal, Pulau Pinang,Malaysia,;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural networks, local scour, pipelines, regression;

    机译:人工神经网络;局部冲刷;管道;回归;

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