Application of GeoSHM System in Monitoring Extreme Wind Events at the Forth Road Bridge

Meng, Xiaolin; Nguyen, Dinh Tung; Owen, John S.; Xie, Yilin; Psimoulis, Panagiotis; Ye, George

Abstract

© 2019 by the authors. Implementation of Structural Health Monitoring systems on long-span bridges has become mandatory in many countries to ascertain the safety of these structures and the public, taking into account an increase in usage and threats due to extreme loading conditions. However, the successful delivery of such a system is facing many challenges including the failure to extract damage and reliability information from monitoring data to assist bridge operators with their maintenance planning and activities. Supported by the European Space Agency under the Integrated Applications Promotion scheme, the project 'GNSS and Earth Observation for Structural Health Monitoring of Long-span Bridges' or GeoSHM aims to address some of these shortcomings (GNSS stands for Global Navigation Satellite System). In this paper, the background of the GeoSHM project as well as the GeoSHM sensor system on the Forth Road Bridge (FRB) in Scotland will be briefly described. The bridge response and wind data collected over a two-year period from 15 October 2015 to 15 October 2017 will be analysed to demonstrate the high susceptibility of the bridge to wind loads. Close examination of the data associated with an extreme wind event in 2018-Storm Ali-will be conducted to reveal the relationship between the wind speed and some monitored parameters such as the bridge response and modal frequencies.

Citation

Meng, X., Nguyen, D. T., Owen, J. S., Xie, Y., Psimoulis, P., & Ye, G. (2019). Application of GeoSHM System in Monitoring Extreme Wind Events at the Forth Road Bridge. Remote Sensing, 11(23), Article 2799. https://doi.org/10.3390/rs11232799

• Journal Article Type: Article
• Acceptance Date: Nov 22, 2019
• Online Publication Date: Nov 26, 2019
• Publication Date: Nov 26, 2019
• Deposit Date: May 20, 2020
• Publicly Available Date: May 27, 2020
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 23
• Article Number: 2799
• DOI: https://doi.org/10.3390/rs11232799
• Public URL: https://nottingham-repository.worktribe.com/output/3437495
• Publisher URL: https://www.mdpi.com/2072-4292/11/23/2799