Abstract
The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts to reveal its deformation mechanism. Firstly, we designed a strategy for high-precision GNSS data processing aiming at facilitating deformation extraction and analysis. Then, 33 months of vertical deformation time series of the southern tower of the Forth Road Bridge (FRB) in the UK were processed, and the accurate subsidence and the parameters of seasonal signals were estimated based on a classic function model that has been widely studied to analyse GNSS coordinate time series. We found that the subsidence rate is about 4.7 mm/year, with 0.1 mm uncertainty. Meanwhile, a 15-month meteorological dataset was utilised with a thermal expansion model (TEM) to explain the effects of seasonal signals on tower deformation. The amplitude of the annual signals correlated quite well that obtained by the TEM, with the consistency reaching 98.9%, demonstrating that the thermal effect contributes significantly to the annual signals. The amplitude of daily signals displays poor consistency with the ambient temperature data. However, the phase variation tendencies between the daily signals of the vertical deformation and the ambient temperature are highly consistent after February 2016. Finally, the potential contribution of the North Atlantic Drift (NAD) to the characteristics of annual and daily signals is discussed because of the special geographical location of the FRB. Meanwhile, this paper emphasizes the importance of collecting more detailed meteorological and other loading data for the investigation of the vertical deformation mechanism of the bridge towers over time with the support of GNSS.
Highlights
Suspension bridges are widely utilised worldwide due to their economical nature, longevity, safety and environmentally friendly properties
It is important to understand the behavior of towers and their corresponding deformation mechanism, especially the vertical deformation that consists of subsidence, periodic signals caused by different kinds of loading forces and noise, for ensuring the structural stability, safety, serviceability and sustainability of bridges [2]
A new measurement and data processing strategy is proposed in this paper, aimed at facilitating the signal extraction of vertical deformation time series
Summary
Suspension bridges are widely utilised worldwide due to their economical nature, longevity, safety and environmentally friendly properties. The main task of an SHM system is to detect fatigue damages, define and update structural and early warning models, identify key parameters, and provide timely and reliable assessment to the operation and health condition of large structures To achieve these targets, for SHM of bridges, it is of fundamental importance to accurately quantify different loading effects (environment, traffic or geo-hazard), precisely measure corresponding responses and understand deformation mechanism [4]. Denser monitoring network can be deployed to support the more detailed health condition analysis and assessment of the large infrastructures Due to these unique characteristics, GNSS can provide an opportunity to understand the behavior and deformation mechanism of the suspension bridge tower based on the long-term deformation time series, which has not yet been properly studied so far.
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