• Economic and Environmental Geology
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• v.56 no.1
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• pp.103-114
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• 2023

The Christian Museum of Gongju Jeil Church was first built in 1931 and was largely damaged during the Korean War, but the walls and chimneys have been preserved. This building has a high architectural values in that the chapel was reconstructed in 1956, and maintains its original form through repair of damaged parts rather than new construction. The stained glass windows were as installed in 1979 and has a great significance in the Dalle de Verre method using lump glass. However, some of the stained glass damaged partially, such as various cracks and splits, and vertical and horizontal cracks in the joint fillers of supporting the colored glass. As the structural materials of the stained glass window, an iron frame and cement mortar filled with it were used, and corrosion of iron, cracking of mortar and granular decomposition appear partially due to weathering. In the joint fillers, the content of Ca and S is very high, indicating that gypsum were used as admixtures, and the gypsums grow in a rhombohedral and forms a bundle, which is investigated to have undergone recrystallization. As a result of modeling the ultrasonic velocity at the joint fillers, the left and right windows at the entrance show relatively weak in the range of 800 to 1,600m/s, and the lower right corner of the altar window and the upper left corner of the center window were also 1,000 to 1,800m/s, showing relatively low physical properties. And gypsums produced during the neutralization of lime mortar were detected in the joint fillers and contaminants on the surface. Such salts may cause damage to the joint material due to freezing and thawing, so appropriate preventive conservation is required. Also, since various damage types are complexly appearing in stained glass window and joint filler, customized conservation treatment should be reviewed through clinical tests.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.2
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• pp.183-193
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• 1995

Assuming that fishing nets are porous structures to suck water into their mouth and then filtrate water out of them, the flow resistance N of nets with wall area S under the velicity v was taken by $R=kSv^2$, and the coefficient k was derived as $$k=c\;Re^{-m}(\frac{S_n}{S_m})n(\frac{S_n}{S})$$ where $R_e$ is the Reynolds' number, $S_m$ the area of net mouth, $S_n$ the total area of net projected to the plane perpendicular to the water flow. Then, the propriety of the above equation and the values of c, m and n were investigated by the experimental results on plane nettings carried out hitherto. The value of c and m were fixed respectively by $240(kg\cdot sec^2/m^4)$ and 0.1 when the representative size on $R_e$ was taken by the ratio k of the volume of bars to the area of meshes, i. e., $$\lambda={\frac{\pi\;d^2}{21\;sin\;2\varphi}$$ where d is the diameter of bars, 21 the mesh size, and 2n the angle between two adjacent bars. The value of n was larger than 1.0 as 1.2 because the wakes occurring at the knots and bars increased the resistance by obstructing the filtration of water through the meshes. In case in which the influence of $R_e$ was negligible, the value of $cR_e\;^{-m}$ became a constant distinguished by the regions of the attack angle $ \theta$ of nettings to the water flow, i. e., 100$(kg\cdot sec^2/m^4)\;in\;45^{\circ}<\theta \leq90^{\circ}\;and\;100(S_m/S)^{0.6}\;(kg\cdot sec^2/m^4)\;in\;0^{\circ}<\theta \leq45^{\circ}$. Thus, the coefficient $k(kg\cdot sec^2/m^4)$ of plane nettings could be obtained by utilizing the above values with $S_m\;and\;S_n$ given respectively by $$S_m=S\;sin\theta$$ and $$S_n=\frac{d}{I}\;\cdot\;\frac{\sqrt{1-cos^2\varphi cos^2\theta}} {sin\varphi\;cos\varphi} \cdot S$$ But, on the occasion of $\theta=0^{\circ}$ k was decided by the roughness of netting surface and so expressed as $$k=9(\frac{d}{I\;cos\varphi})^{0.8}$$ In these results, however, the values of c and m were regarded to be not sufficiently exact because they were obtained from insufficient data and the actual nets had no use for k at $\theta=0^{\circ}$. Therefore, the exact expression of $k(kg\cdotsec^2/m^4)$, for actual nets could De made in the case of no influence of $R_e$ as follows; $$k=100(\frac{S_n}{S_m})^{1.2}\;(\frac{S_m}{S})\;.\;for\;45^{\circ}<\theta \leq90^{\circ}$$, $$k=100(\frac{S_n}{S_m})^{1.2}\;(\frac{S_m}{S})^{1.6}\;.\;for\;0^{\circ}<\theta \leq45^{\circ}$$