• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.42-49
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• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.120-130
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• 2019

In this study, based on the correlation between traffic volume data and vertical displacement data developed in previous research using the bridge maintenance big data of 2006, the vertical displacement estimation model using the traffic volume data of Gwangan Bridge for 10 years A comparison of the performance of the developed model with the current applicability is presented. The present applicability of the developed model is analyzed that the estimated displacement is similar to the actual displacement and that the displacement estimation performance of the model based on the structured regression analysis and the principal component analysis is not significantly different from each other. In conclusion, the vertical displacement estimation model using the traffic volume data developed by this study can be effectively used for the analysis of the behavior according to the traffic load of Gwangan Bridge.

• Journal of the Korean GEO-environmental Society
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• v.19 no.11
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• pp.15-22
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• 2018

The main objective of this study was to analyse heavy metals in sediments obtained from Gwangan bridge and to evaluate pollution intensity of the sites. To evaluate pollution intensity of the sites, we used enrichment factor (EF), geoaccumulation index, potential ecological risk factor (PERF), and mean PEL quotient. Pollution intensities of these sites were evaluated by above methods, and we found most dangerous heavy metal and polluted sites. All sites showed non polluted or low risk for the heavy metals such as Cr, Cu, Ni, Pb, and Zn, but all sites were categorized as minor enrichment for Cd. G4 was evaluated as moderately polluted by Cd ($I_{geo}$) but other sites were unpolluted by heavy metals. In summary, Cd was found to be higher concentrations for all sites. For G4 and G5 sites, Pb and Zn in addition to Cd were higher than other sites.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.11
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• pp.809-814
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• 2013

In this study, physical properties and heavy metal contents of sediments obtained from the bottom of Gwangan bridge were measured to determine pollution level of the sediments. From the results of the oxide contents of the sediments, $SiO_2$ was decreased as the sampling points became more distant from the stream of river. On the contrary, CaO showed opposition aspect to $SiO_2$. Ignition loss of sediments ranged from 7.2 and 14.3% and 0.9 and 5.5% for TOC. For EPA guidelines of ignition loss, all sampling points were classified as heavily polluted areas. When TOC was considered, all areas were classified as lowest effect level except for GW7 where classified as no effect level. All areas were free of heavy metal contamination evaluated by USEPA and Canadian guidelines. However, all areas were classified as heavily contaminated areas due to the high value of ignition loss when USEPA was used.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.933-939
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• 2011

Recent heavy rains have caused natural disasters such as flooding and landslides nationwide. Because of flooding occurrence in most of the roads, traffic congestion and isolation caused many loss especially at rush hour. Constant monitoring and analysis of past disaster history data are needed to prevent disasters on areas prone to floods and disaster risk areas. If we managed to obtain traffic volume, speed, phase around intersection using disaster history data when disasters occurred, we can analyse traffic congestion, change of disaster scale and rainfall. In this study, We select a target district to develop by using a route from Dae-nam intersection in Busan Namgu Daeyoeon-dong, over Gwangan large bridge up until Haeundae Olympic intersection, We developed a system which searches disaster history information, traffic volume using disaster history data based on user selection of the road.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.87-94
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• 2018

Intelligent transportation systems are one of the most important new forms of infrastructure on domestic roads, and is a system that makes possible the most efficient movement of vehicles on a road. The High Pass system, which is a domestic intelligent transportation system, started a little later than in other countries but developed at a rapid pace. With the recent introduction of smart tolling technology, it provided an opportunity to stop and review the tolling system. This study aims to investigate the driving method and results of LDCC for domestic smart towing through case study. Unlike other companies, Lotte Data Communication Company has long invested in payment systems. It has little experience investing in infrastructure, but participated in the Smart Toll System at the Gwangan Bridge in cooperation with the Busan City government, to lead the development of intelligent transportation systems. LDCC, which has made new investments, not only exceeded its existing core competencies, but also upgraded Korea's tolling system's ability to reduce greenhouse gas emissions and improved its financial performance.