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ANFIS-based approach for scour depth prediction at piers in non-uniform sediments

机译：基于ANFIS的非均匀沉积物码头冲刷深度预测方法

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摘要

An estimation of scour depth is a prerequisite for the efficient foundation design of importantnhydraulic structures such as bridge piers and abutments. Most of the scour depth predictionnformulae available in the literature have been developed based on the analysis of thenlaboratory/field data using statistical methods such as the regression method (RM). Conventionalnstatistical analysis is generally replaced in many fields of engineering by the alternative approachnof artificial neural networks (ANN) and adaptive network-based fuzzy inference systems (ANFIS).nThese recent techniques have been reported to provide better solutions in cases where thenavailable data is incomplete or ambiguous by nature. An attempt has been made to compare thenperformance of ANFIS over RM and ANN in modeling the depth of bridge pier scour in non-uniformnsediments. It has been found that the ANFIS performed best amongst all these methods.
机译：冲刷深度的估算是重要的液压结构（如桥墩和桥台）高效基础设计的先决条件。文献中可用的大多数冲刷深度预测公式是基于对实验室/现场数据的分析，并使用统计方法（例如回归方法（RM））开发的。在许多工程领域中，传统的统计分析通常都被人工神经网络（ANN）和基于自适应网络的模糊推理系统（ANFIS）的替代方法所取代。天生就模棱两可。试图比较ANFIS与RM和ANN在非均匀沉积中模拟桥墩冲刷深度的性能。已经发现，在所有这些方法中，ANFIS表现最佳。

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  • 来源
    《Journal of Hydroinformatics》 |2010年第3期|p.303-317|共15页
  • 作者

    Muzzammil M. - Ayyub M.;

  • 作者单位

    M. Muzzammil (corresponding author)Department of Civil Engineering,Aligarh Muslim University,Aligarh PIN-202002,IndiaE-mail: muzzammil786@rediffmail.comM. AyyubDepartment of Electrical Engineering,Aligarh Muslim University,Aligarh PIN-202002,India;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ANN, bridge pier, neuro-fuzzy, regression method, scour depth;

    机译：人工神经网络;桥墩;神经模糊;回归方法;冲刷深度;

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