TY - GEN
T1 - Analysis of monitoring data from cable-stayed bridge using sensor fusion techniques
AU - Zonta, Daniele
AU - Bruschetta, Federico
AU - Zandonini, Riccardo
AU - Pozzi, Matteo
AU - Wang, Ming
AU - Zhao, Yang
AU - Inaudi, Daniele
AU - Posenato, Daniele
AU - Glisic, Branko
PY - 2013
Y1 - 2013
N2 - This paper illustrates an application of Bayesian logic to monitoring data analysis and structural condition state inference. The case study is a 260 m long cable-stayed bridge spanning the Adige River 10 km north of the town of Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that long-term load redistribution between cables can be expected. To monitor load redistribution, the owner decided to install a monitoring system which combines built-on-site elasto-magnetic and fiber-optic sensors. In this note, we discuss a rational way to improve the accuracy of the load estimate from the EM sensors taking advantage of the FOS 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 the 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 paper illustrates an application of Bayesian logic to monitoring data analysis and structural condition state inference. The case study is a 260 m long cable-stayed bridge spanning the Adige River 10 km north of the town of Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that long-term load redistribution between cables can be expected. To monitor load redistribution, the owner decided to install a monitoring system which combines built-on-site elasto-magnetic and fiber-optic sensors. In this note, we discuss a rational way to improve the accuracy of the load estimate from the EM sensors taking advantage of the FOS 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 the 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 inference
KW - Cable-Stayed bridge
KW - Elasto-magnetic sensors
KW - Fiber optic sensors
KW - Sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=84878741913&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878741913&partnerID=8YFLogxK
U2 - 10.1117/12.2014733
DO - 10.1117/12.2014733
M3 - Conference contribution
AN - SCOPUS:84878741913
SN - 9780819494757
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013
T2 - 2013 SPIE Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013
Y2 - 10 March 2013 through 14 March 2013
ER -