吴西杰,张龑,尹进步,等. 基于贝叶斯理论的渡槽结构有限元模型更新方法[J]. 水利水运工程学报.. DOI: 10.12170/20231011001
引用本文: 吴西杰,张龑,尹进步,等. 基于贝叶斯理论的渡槽结构有限元模型更新方法[J]. 水利水运工程学报.. DOI: 10.12170/20231011001
(WU Xijie, ZHANG Yan, YIN Jinbu, et al. A finite element model updating method for aqueduct structures based on Bayesian theory[J]. Hydro-Science and Engineering(in Chinese)). DOI: 10.12170/20231011001
Citation: (WU Xijie, ZHANG Yan, YIN Jinbu, et al. A finite element model updating method for aqueduct structures based on Bayesian theory[J]. Hydro-Science and Engineering(in Chinese)). DOI: 10.12170/20231011001

基于贝叶斯理论的渡槽结构有限元模型更新方法

A finite element model updating method for aqueduct structures based on Bayesian theory

  • 摘要: 渡槽在服役过程中,其结构状态受到多种因素影响会发生改变,在进行有限元分析时,建模所用参数并不是结构实时参数。因此,需要通过监测结构的动力响应来对结构的有限元模型参数进行更新,达到准确评价结构实际运行状态的目的。提出了一种基于贝叶斯理论的有限元模型更新方法,该方法以确定性模型更新结果作为不确定性更新的初始模型参数值,使用MH算法得到模型参数的后验分布,通过ABAQUS有限元模型与MATLAB更新程序的交互访问,实现复杂结构有限元模型参数的自动更新。开展了渡槽模型试验,利用所提方法对渡槽有限元模型进行更新。结果表明,以确定性更新的结果作为贝叶斯更新的参数初始值,有限元模型更新的计算效率提高了25%,更新后的误差为0.19%~6.42%,该更新方法能有效改善有限元模型的预测能力。

     

    Abstract: The structural condition of aqueducts changes over time due to various influencing factors. Consequently, parameters used in finite element analysis may not reflect the real-time state of the structure. Therefore, it is necessary to update the finite element model parameters by monitoring the dynamic response of the structure to accurately assess its operational state. This paper proposes a finite element model updating method based on Bayesian theory. The method uses deterministic model updating results as the initial parameters for the uncertain model update. The posterior distribution of model parameters is obtained using the Metropolis-Hastings (MH) algorithm. By integrating ABAQUS finite element models with MATLAB updating programs, automatic parameter updating for complex structural finite element models is achieved. An aqueduct model test was conducted to validate this method. Results indicate that using deterministic updating results as initial parameters for Bayesian updating improves computational efficiency by 25%, with an updated error range of 0.19% to 6.42%. This updating method effectively enhances the predictive capability of finite element models.

     

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