Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam

MA Gang; CHANG Xiao-lin; ZHOU Wei; HUA Jun-jie

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Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam

更新时间：2026-06-25

- Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam
- Vol. 33, Issue 6, (2012)
- 作者机构：
  
  1. 武汉大学水资源与水电工程科学国家重点实验室,武汉,430072
  2. 武汉大学水工岩石力学教育部重点实验室,武汉,430072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TV641.4
- Online First：14 June 2012，
  
  Published：2012
- 稿件说明：
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MA Gang, CHANG Xiao-lin, ZHOU Wei, et al. Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam[J]. 2012, 33(6).
DOI：

MA Gang, CHANG Xiao-lin, ZHOU Wei, et al. Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam[J]. 2012, 33(6). DOI：

摘要

利用反演分析得到的参数进行高面板坝的应力、变形分析来预测长期变形。由于堆石坝的施工过程和变形机制比较复杂，很难将瞬时变形和流变变形分开，因此，有必要对静力本构模型参数和流变模型参数进行综合反演。利用实测位移资料，以对堆石坝变形较敏感的静力本构模型和流变模型参数为待反演参数，采用基于粒子迁徙的粒子群算法和径向基函数神经网络构建参数反演平台，该方法克服了粒子群算法易陷入局部最优和早熟收敛的缺点，采用经过训练的神经网络来描述模型参数和位移之间的映射关系，节省了参数反演的计算时间。对水布垭高面板坝的反演结果表明，基于反演参数的沉降计算值与实测值吻合得很好，坝体变形在合理范围以内并趋于稳定。

Abstract

Inversion parameters are used to calculate and predict long-term deformation of high concrete face rockfill dam (CRFD). Since the construction process and deformation mechanism of rockfill are complex

it is difficult to separate the instantaneous deformation and rheological deformation

and hence it is necessary to carry out the integrated inversion of static constitutive and rheological model parameters. Based on the measured displacement data

static constitutive and rheological model parameters sensitive to the deformation of rockfill dam are selected for inversion. Additionally

a modified particle swarm optimization algorithm (MPSO) based on particle migration and radial basis function neural network (RBF-ANN) are adopted. What is more

by using the trained neural network to describe the relationship between model parameters and displacements

shorts computing time of parametric inversion. The inversion result of Shuibuya CRFD shows that the calculated settlements agree well with the measured data; and dam deformation is within a reasonable range and tending towards stability.

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