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基于RBF神经网络的土石坝渗透系数反演及演化规律研究

陈功元

陕西水利Issue(2):32-35,4.
陕西水利Issue(2):32-35,4.

基于RBF神经网络的土石坝渗透系数反演及演化规律研究

Research on Inversion and Evolution Patterns of Permeability Coefficient for Earth-Rock Dams Based on RBF Neural Network

陈功元1

作者信息

  • 1. 安徽省淠史杭灌区管理总局,安徽 六安 237100
  • 折叠

摘要

Abstract

The dynamic variation of the permeability coefficient in earth-rock dams affects the seepage characteristics and stability of the dam body.Taking an earth-rock dam of a reservoir as the research subject,a seepage model is constructed based on finite element numerical simulation.Combined with Radial Basis Function(RBF)neural network training and inversion,the evolution patterns of the horizontal and vertical permeability coefficients of the dam body are analyzed.The results show that:(1)Among the inverted anisotropic permeability coefficients,the horizontal permeability coefficient gradually decreases with the increase of years,while the vertical permeability coefficient gradually increases.(2)The inverted isotropic permeability coefficient gradually increases over the years.(3)The seepage pressure curve obtained from the isotropic permeability coefficient is close to the measured seepage pressure curve,indicating that the isotropic permeability coefficient obtained through the RBF neural network model training has higher accuracy.

关键词

土石坝/RBF神经网络模型/渗透系数反演/数值模拟

Key words

Earth-rock dam/RBF neural network model/Permeability coefficient inversion/Numerical simulation

分类

建筑与水利

引用本文复制引用

陈功元..基于RBF神经网络的土石坝渗透系数反演及演化规律研究[J].陕西水利,2026,(2):32-35,4.

陕西水利

1673-9000

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