TY - GEN
T1 - Fusion of monitoring data from cable-stayed bridge
AU - Bruschetta, F.
AU - Zonta, D.
AU - Cappello, C.
AU - Zandonini, R.
AU - Pozzi, M.
AU - Glisic, B.
AU - Inaudi, D.
AU - Posenato, D.
AU - Wang, M. L.
AU - Zhao, Y.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Bayesian approach
KW - Cable-stayed bridge
KW - Data fusion
KW - Elasto-magnetic sensors
KW - Fiber-optic sensors
KW - Monitoring system
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U2 - 10.1109/EESMS.2013.6661702
DO - 10.1109/EESMS.2013.6661702
M3 - Conference contribution
AN - SCOPUS:84892632205
SN - 9781479906284
T3 - 2013 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems, EESMS 2013 - Proceedings
BT - 2013 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems, EESMS 2013 - Proceedings
PB - IEEE Computer Society
T2 - 2013 5th IEEE Workshop on Environmental, Energy and Structural Monitoring Systems, EESMS 2013
Y2 - 11 September 2013 through 12 September 2013
ER -