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Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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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

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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.