Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number4187
Journal / PublicationSensors (Switzerland)
Volume18
Issue number12
Online published29 Nov 2018
Publication statusPublished - Dec 2018
Externally publishedYes

Link(s)

Abstract

The tensile force on the hanger cables of a suspension bridge is an important indicator of the structural health of the bridge. Tensile force estimation methods based on the measured frequency of the hanger cable have been widely used. These methods empirically pre-determinate the corresponding model order of the measured frequency. However, because of the uncertain flexural rigidity, this empirical order determination method not only plays a limited role in high-order frequencies, but also hinders the online cable force estimation. Therefore, we propose a new method to automatically identify the corresponding model order of the measured frequency, which is based on a Markov chain Monte Carlo (MCMC)-based Bayesian approach. It solves the limitation of empirical determination in the case of large flexural rigidity. The tensile force and the flexural rigidity of cables can be calculated simultaneously using the proposed method. The feasibility of the proposed method is validated via a numerical study involving a finite element model that considers the flexural rigidity and via field application to a suspension bridge.

Research Area(s)

  • Bayesian approach, Cable force estimation, Model order identification, Optimization, Suspension bridge hanger cable

Citation Format(s)

Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach. / Zhan, Shaodong; Li, Zhi; Hu, Jianmin et al.
In: Sensors (Switzerland), Vol. 18, No. 12, 4187, 12.2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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