Picture of UK Houses of Parliament

Leading national thinking on politics, government & public policy through Open Access research

Strathprints makes available scholarly Open Access content by researchers in the School of Government & Public Policy, based within the Faculty of Humanities & Social Sciences.

Research here is 1st in Scotland for research intensity and spans a wide range of domains. The Department of Politics demonstrates expertise in understanding parties, elections and public opinion, with additional emphases on political economy, institutions and international relations. This international angle is reflected in the European Policies Research Centre (EPRC) which conducts comparative research on public policy. Meanwhile, the Centre for Energy Policy provides independent expertise on energy, working across multidisciplinary groups to shape policy for a low carbon economy.

Explore the Open Access research of the School of Government & Public Policy. Or explore all of Strathclyde's Open Access research...

Fusion of monitoring data from cable-stayed bridge

Bruschetta, F. and Zonta, D. and Cappello, C. and Zandonini, R. and Pozzi, M. and Glisic, B. and Inaudi, D. and Posenato, D. and Wang, M. L. and Zhao, Y. (2013) Fusion of monitoring data from cable-stayed bridge. In: 2013 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems, EESMS. IEEE, pp. 1-6. ISBN 9781479906284

Full text not available in this repository.Request a copy from the Strathclyde author

Abstract

This contribution illustrates an application of Bayesian logic to monitoring data analysis and structural condition state inference. The case study is a cable-stayed bridge 260 m long spanning the Adige River ten kilometers north of the town of Trento, Italy. It is a statically indeterminate structure, consisting of a steel-concrete composite deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that longterm load redistribution between cables can be expected. To monitor load redistribution, the owner decided to install a monitoring system that combines built-on-site elasto-magnetic and fiber-optic sensors. In this article, we discuss a rational way to improve the accuracy of the load variation, estimated using the elasto-magnetic sensors, taking advantage of the fiber-optic sensors information. More specifically, we use a multi-sensor Bayesian data fusion approach, which combines the information from the two sensing systems with the prior knowledge including design information and outcomes of laboratory calibration. Using the data acquired to date, we demonstrate that combining the two measurements allows a more accurate estimate of the cable load, to better than 50 kN.