• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.295-311
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• 2021

In this study, an experiment was carried out on the field applicability of tension measuring devices of the cables in cable-stayed bridges. The vibration method was used to estimate the tension of cables of cable-stayed bridge, and the mode characteristics of the cable were analyzed using a cable tension measuring device. GTDL360, NI Module, and 9 Axes Motion Sensorwere applied to estimate the cable tension of five target bridges. Numerical analysis of the five target bridges was conducted to analyze the natural frequency of the cable and cable tension. The estimated tension of the cable based on field measurements and estimated tension of cable by numerical analysis were compared with the estimated tension of the cable based on field measurements. The analysis showed that the measured tension of the cable based on field measurements was within the margin of error. Therefore, it is safe to apply these measuring devices to the site. As a result of comparing and analyzing the values of the acceleration-based cable estimation tension and numerical analysis of the field demonstration bridge, the acceleration-based cable estimation of tension is deemed appropriate within the allowable range. On-site applicability analysis revealed limitations of the measuring devices, such as the installation location of sensors and weather conditions, so continuous follow-up research on smart cable tension measuring systems is expected.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.507-520
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• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.