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

• Main Authors: Shaodong Zhan, Zhi Li, Jianmin Hu, Yiping Liang, Guanglie Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Sensors
• Subjects: suspension bridge hanger cable; cable force estimation; model order identification; Bayesian approach; optimization
• Online Access: https://www.mdpi.com/1424-8220/18/12/4187

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.