Method for determining displacement monitoring indexes of arch dams based on stochastic back analysis of parameters

贾冬焱; 马春辉; 杨杰; 冉蠡; 仝飞

doi:10.11660/slfdxb.20240111

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Method for determining displacement monitoring indexes of arch dams based on stochastic back analysis of parameters

更新时间：2026-07-22

- Method for determining displacement monitoring indexes of arch dams based on stochastic back analysis of parameters
- Journal of Hydroelectric Engineering Vol. 43, Issue 1, (2024)
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- DOI：10.11660/slfdxb.20240111
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- Online First：25 January 2024，
  
  Published：2024
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贾冬焱, 马春辉, 杨杰, et al. Method for determining displacement monitoring indexes of arch dams based on stochastic back analysis of parameters[J]. Journal of Hydroelectric Engineering, 2024, 43(1).
DOI：

贾冬焱, 马春辉, 杨杰, et al. Method for determining displacement monitoring indexes of arch dams based on stochastic back analysis of parameters[J]. Journal of Hydroelectric Engineering, 2024, 43(1). DOI： 10.11660/slfdxb.20240111.

摘要

针对传统混凝土拱坝位移监控指标拟定中未考虑大坝材料参数不确定性的问题，结合贝叶斯推断理论、粒子群优化和长短时记忆网络提出一种基于随机反分析结果的拱坝位移监控指标拟定方法。利用粒子群方法优化长短时记忆神经网络作为代理模型；基于不同监测点实测位移值和代理模型计算位移值建立联合似然函数；在已知材料参数先验分布下，通过贝叶斯推断获得材料参数的后验分布；利用材料参数的后验分布计算监控指标的水压分量，结合实测位移数据确定混凝土拱坝位移监控指标。工程实例分析表明：通过贝叶斯推断方法，可以反演得到准确的材料参数后验结果；以混合法确定的位移监控指标充分利用了材料参数的随机反分析结果，具有更好的预警效果。

Abstract

In determining the displacement monitoring indicators of a concrete arch dam

the traditional method did not consider the uncertainty of dam material parameters. This paper develops a new method for the determination of these indicators based on the results of stochastic parameter back analysis by combining the Bayesian inference theory

particle swarm optimization

and a long short-term memory network. We use the particle swarm optimization to optimize the long short-term memory network as the surrogate model

and derive a joint likelihood function using the displacement values measured at different monitoring points and the ones calculated by the surrogate model. With the prior distribution of material parameters given

we can use the Bayesian inference to calculate their posterior distribution. This distribution is used to calculate the dam displacement component due to water pressure; then the displacement monitoring index of the dam can be determined using displacement measurements. Analysis of engineering cases shows the Bayesian inference method gives accurate posterior results of the material parameters. The monitoring indicators

determined using the mixed method

fully utilize the results of stochastic back analysis and have better warning effects.

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