Adaptive inversion analysis of material parameters of rock-fill dam
based on QGA-MMRVM

MA Chun-hui; YANG Jie; CHENG Lin; LI Ting; LI Ya-qi

doi:10.16285/j.rsm.2018.0320

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Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM

更新时间：2026-06-25

- Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM
- Vol. 40, Issue 6, Pages: 2397-2406(2019)
- 作者机构：
  
  1. 西安理工大学水利水电学院,陕西,西安,710048
  2. 西安理工大学省部共建西北旱区生态水利国家重点实验室,陕西,西安,710048
  3. 南京水利科学研究院生态环境研究中心,江苏,南京,210029
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2018.0320
  CLC： TV641
- Online First：22 June 2019，
  
  Published：2019
- 稿件说明：
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MA Chun-hui, YANG Jie, CHENG Lin, et al. Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM[J]. 2019, 40(6): 2397-2406.
DOI：

MA Chun-hui, YANG Jie, CHENG Lin, et al. Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM[J]. 2019, 40(6): 2397-2406. DOI： 10.16285/j.rsm.2018.0320.

摘要

为进一步提高堆石坝材料参数反演模型的计算精度与适用性，建立了基于量子遗传算法（QGA）与多输出混合核相关向量机（MMRVM）的自适应反演模型。通过引入混合核函数，使所构建的MMRVM能够高精度地模拟材料参数与大坝沉降间的复杂非线性关系，从而代替耗时较长的有限元（FEM）计算。通过利用参数较固化的QGA优化确定MMRVM核参数，使反演模型具有自适应性。以实测沉降数据为依据，充分发挥QGA的全局搜索能力反演筑坝材料本构模型参数。在分析模型所需测点个数与信噪比对计算结果影响的基础上，通过公伯峡堆石坝应用实例证明：QGA-MMRVM可快速、精确地反演堆石坝筑坝材料本构模型参数，模型凭借其自适应性在实际工程中具有良好的应用前景和推广价值。

Abstract

In order to improve the accuracy and applicability of inversion analysis model of material parameters for rockfill dam

an adaptive model based on quantum genetic algorithm (QGA) and multi-output mixed kernel relevance vector machine (MMRVM) is established. By introducing mixed kernel function

the MMRVM can accurately simulate the nonlinear relationship between the material parameters and the settlement of rockfill dam. Therefore

the finite element method (FEM) can be replaced by the MMRVM to reduce time consumption. Then

the kernel parameters of the MMRVM is optimized by the QGA

thus the QGA-MMRVM is adaptable to different inversion analysis problems. The parameters of constitutive model of dam materials can be inverted by fully utilizing QGA's global searching ability. Finally

the influences of the signal-noise ratio and the number of measured points on the calculation result are analyzed. The examples of Gongboxia dam show that the parameters of constitutive model of material can quickly and accurately calculated by the QGA-MMRVM. With its adaptability

the QGA-MMRVM has good application prospect and popularization value in practical engineering.

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