Bayesian model averaging and Bayesian inference-based probabilistic inversion method for arch dam zonal material parameters

IF 3.9 2区工程技术 Q1 ENGINEERING, CIVIL

Structures Pub Date : 2024-10-23 DOI:10.1016/j.istruc.2024.107605

Lin Cheng , Anan Zhang , Jiamin Chen , Chunhui Ma , Zengguang Xu

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引用次数: 0

Abstract

Inversion analysis based on structural monitoring data is an important part of evaluating the working behaviour of structures. In this paper, a probabilistic inversion method for the elastic modulus of concrete in arch dams based on Bayesian model averaging (BMA) and Bayesian inference is proposed. According to the measured displacement data of an arch dam project, the influence of the separation accuracy of water pressure component, the inversion method and the selection of displacement measuring points on the inversion results of the elastic modulus of the dam body is studied. Example analysis results show that the accuracy of the multi-measurement point displacement and hydraulic pressure component separation model based on BMA-HST (Hydrostatic-seasonal-time) is at least 18.97 % higher than that of the single measurement point, and the relative error of the elastic modulus value of the multi-measurement point zonal inversion based on the Polynomial chaos-Kriging (PCK) proxy model and the Bayesian inference is only 6 % at the maximum, which indicates that the inversion model described in this paper is able to realize the high-precision inversion analysis of the material parameters of the zonal analysis of concrete dams at a relatively low computational cost.

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基于贝叶斯模型平均和贝叶斯推理的拱坝分区材料参数概率反演方法

基于结构监测数据的反演分析是评估结构工作行为的重要组成部分。本文提出了一种基于贝叶斯模型平均（BMA）和贝叶斯推理的拱坝混凝土弹性模量概率反演方法。根据某拱坝工程实测位移数据，研究了水压力分量分离精度、反演方法和位移测点选择对坝体弹性模量反演结果的影响。实例分析结果表明，基于 BMA-HST（Hydrostatic-seasonal-time）的多测点位移与水压分量分离模型的精度比单测点位移与水压分量分离模型至少高出 18.97 %，基于多项式混沌-克里金（PCK）代理模型和贝叶斯推断的多测点分区反演弹性模量值的相对误差最大仅为 6 %，这表明本文所述反演模型能够以较低的计算成本实现混凝土坝分区分析材料参数的高精度反演分析。

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来源期刊

Structures Engineering-Architecture

CiteScore

5.70

自引率

17.10%

发文量

1187

期刊介绍： Structures aims to publish internationally-leading research across the full breadth of structural engineering. Papers for Structures are particularly welcome in which high-quality research will benefit from wide readership of academics and practitioners such that not only high citation rates but also tangible industrial-related pathways to impact are achieved.