Bayesian forces identification in cable networks with small bending stiffness

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Davide Piciucco, Francesco Foti, Margaux Geuzaine, Vincent Denoël
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引用次数: 0

Abstract

The regular monitoring of cable forces is essential for ensuring the safety of cable structures both during construction and throughout their lifetime. This paper aims at developing a vibration-based identification procedure of the axial forces, bending stiffness, and, secondarily, the crossing point position of cable networks. A model constituted by two crossing stays having small bending stiffness and negligible sag effects is considered. The in-plane direct dynamic problem is solved both numerically and through a perturbation approach. The obtained results are compared to the outcomes of a finite element model for verification purposes. The theoretical studies are also supported by experimental tests performed on a real cable-stayed bridge (Haccourt bridge), which provide insights into the dynamics of the system showing that models of cables with small bending stiffness are more appropriate than taut string models. The inverse analysis based on non-linear Bayesian regression is developed and the closed-form asymptotic formulations are used to prove that the bending stiffness, the cable forces, and the crossing point position can be separately identified from a set of observed frequencies. The implemented procedure is then applied to the tested bridge as a proof of concept, showing that the proposed in-plane identification strategy provides satisfactory results.
小弯曲刚度索网的贝叶斯力辨识
索力的定期监测对于确保索结构在施工期间和整个使用寿命期间的安全至关重要。本文旨在开发一种基于振动的电缆网络轴向力、弯曲刚度和交叉点位置识别程序。考虑了由两个交叉构件组成的模型,其弯曲刚度较小,垂度可以忽略不计。用数值方法和摄动方法求解了平面内直接动力问题。所得结果与有限元模型的结果进行了比较,以进行验证。在实际斜拉桥(Haccourt桥)上进行的实验测试也支持了理论研究,这些测试提供了对系统动力学的见解,表明具有小弯曲刚度的索模型比拉紧索模型更合适。建立了基于非线性贝叶斯回归的反分析方法,并采用封闭式渐近公式证明了从一组观测频率中可以分别识别出弯曲刚度、索力和交叉点位置。然后将所实现的程序应用于测试桥梁作为概念验证,表明所提出的平面内识别策略提供了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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