Design and deployment of a monitoring system on a long-span suspension bridge

Monitoring systems have become a standard component for many landmark bridges. This paper describes the design of a new monitoring system for the Hålogaland bridge, a suspension bridge has a main span of 1145 m located in an arctic environment in northern Norway. This bridge being a prime example of...

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Bibliographic Details
Main Authors: Petersen, Øyvind Wiig, Frøseth, Gunnstein Thomas, Øiseth, Ole Andre
Format: Article in Journal/Newspaper
Language:English
Published: ISHMII 2021
Subjects:
Online Access:https://hdl.handle.net/11250/2982037
Description
Summary:Monitoring systems have become a standard component for many landmark bridges. This paper describes the design of a new monitoring system for the Hålogaland bridge, a suspension bridge has a main span of 1145 m located in an arctic environment in northern Norway. This bridge being a prime example of a wind-sensitive structure, the monitoring system is designed with focus on wind engineering research for long-span bridges. Previous monitoring projects on bridges of similar scale have also revealed some knowledge gaps (e.g. discrepancies in predicted and measured responses) and interesting observations (e.g. strong effects on the surrounding terrain on the wind loads). Such results should be further investigated and cross-checked for bridges in other locations. The monitoring system is custom designed and built by researchers at NTNU, using NI CompactRIO controllers as base data acquisition units for sampling and controlling the system. The CompactRIOs are programmed using the LabVIEW software. Multiple types of sensors are employed; sonic anemometers for wind measurements, accelerometers in the bridge deck and hangers for structural responses, strain gauges, and temperature sensors. Timestamps from GPS antennas are used to sync the measured data between the different CompactRIOs. Ultimately, the acquired data is planned to be used in research on modal parameter identification under the influence on wind, identification of wind loading, modelling of spatial wind fields, serviceability limits with respect to road accidents during strong winds, in addition to new techniques on machine learning in structural health monitoring. publishedVersion