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.
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.
The motivation of this work is the installation of a monitoring system on a new cable-stayed bridge spanning the Adige
River 10 km north of the town of Trento. This is a statically indeterminate structure, having a composite steel-concrete
deck of length 260 m overall, supported by 12 stay cables, 6 per deck side. These are full locked steel cables of diameters
116 mm and 128 mm, designed for operational loads varying from 5000 to 8000 kN. The structural redundancy suggests
that plastic load redistribution among the cables can be expected in the long term. To monitor such load redistribution,
the owner decided to install a monitoring system to measure cable stress; the precision specified was of the order of few
MPa. However no cable release or any form of on-site calibration involving tension change was allowed. The solution
found was a combination of built-on-site electromagnetic and fiber-optic elongation gauges, these appropriately
distributed on both the cables and the anchorages. We discuss how the set of gauges allows tension and elongation
measurement with the appropriate precision, and compare the initial monitoring results with the tension estimates made
using a non-destructive vibration test.
This paper is focus on the applications of EM sensor on cable force measurement for large bridges.
The sensors are entirely suitable for sheathed cables and require no physical contact with the cable itself. In
order to meet the requirement of observing structure behavior under extreme events, a high sampling rate of
EM technology has been developed. The sampling rate of the EM sensor can be as high as 0.1 Hz which is
faster than the current available technology for sensor size of up to 250mm. Both laboratory and field
calibrations were conducted. The relationship between the relative incremental permeability and tensile
stress is derived from these calibrations. Field measurements on tendons for Stonecutters Bridge in Hong
Kong demonstrate the reliability and accuracy of the EM stress sensors using the updated technology.
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