• Journal of the Korea Concrete Institute
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• v.28 no.3
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• pp.365-373
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• 2016

Ultra-High-Performance Steel Fiber-Reinforced Concrete (SUPER Concrete) exhibits improved compressive and tensile strengths far superior to those of conventional concrete. These characteristics can significantly reduce the cross sectional area of the member and the anchorage strength of a headed bar is expected to be improved. In this study, the anchorage strengths of headed bars with $4d_b$ or $6d_b$ embedment length were evaluated by simulated exterior beam-column joint tests where the headed bars were used as beam bars and the joints were cast of 120 or 180 MPa SUPER Concrete. In all specimens, the actual yield strengths of the headed bars over 600 MPa were developed. Some headed bars were fractured due to the high anchorage capacity in SUPER Concrete. Therefore, the headed bar with only $4d_b$ embedment length in 120 MPa SUPER Concrete can develop a yield strength of 600 MPa which is the highest design yield strength permitted by the KCI design code. The previous model derived from tests with normal concrete and the current design code underestimate the anchorage capacity of the headed bar anchored in SUPER Concrete. Because the previous model and the current design code do not consider the effects of the high tensile strength of SUPER Concrete. From a regression analysis assuming that the anchorage strength is proportional to $(f_{ck})^{\alpha}$, the model for predicting anchorage strength of headed bars in SUPER Concrete is developed. The average and coefficient of variation of the test-to-prediction values are 1.01 and 5%, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.1-20
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• 2020

The purpose of this study was to analyze the distribution characteristics of spatial and temporal PM_2.5 in urban areas of Changwon-si, and to identify the causes of PM_2.5 by comparing the characteristics of land-use, and to suggest the direction of reduction measures. As the basic data, the every hour average from September 2017 to August 2018 of Airpro data, which has measurement points in kindergartens, elementary schools, and some middle and high schools in Changwon-si was used. Also, by using IDW method among spatial interpolation methods of GIS, monthly and time-slot distribution maps were constructed, and based on this, spatial and temporal PM_2.5 distribution characteristics were confirmed. First, to verify the accuracy of the Airpro data, the correlation with AirKorea data managed by the Ministry of Environment was confirmed. As a result of the analysis, R² was 0.75~0.86, showing a very high correlation and the data was judged that it was suitable for the study. In the monthly analysis, January was the highest year, and August was the lowest. As a result of analysis by time-slot, The clock-in time at 06-09 was the highest, and the activity time at 09-18 was the lowest. By administrative district, Sangnam-dong, Happo-dong, and Myeonggok-dong were the most severe regions of PM_2.5 and Hoeseong-dong was the lowest. As a result of analyzing the land-use characteristics by administrative area, it was confirmed that the ratio of traffic area and commercial area is high in the serious area of PM_2.5. In conclusion, the results of this study will be used as basic data to grasp the characteristics of PM_2.5 distribution in Changwon-si. Also, it is thought that the severe regions and the direction of establishing reduction measures derived from this study can be used to prepare more effective policies than before.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.4
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• pp.403-415
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• 2014

Recently, the temperature rise in the summer due to climate change, power usage is increasing rapidly. As a result, power generation facilities have been newly completed and the need for ultra-high-voltage transmission line for power transmission of electricity to the urban area has increased. The mechanized tunnelling method using a shield TBM have an advantage that it can minimize vibrations transmitted to the ground and ground subsidence as compared with the conventional tunnelling method. Despite the popularity of shield TBM for cable tunnel construction, study on the mechanical behavior of cable tunnel driven by shield TBM is insufficient. Thus, in this study, the effect of fractured zone ahead of tunnel face on the mechanical behavior of the shield TBM cable tunnel is investigated. In addition, it is intended to compare the behavior characteristics of the fractured zone with continuous model and applying the interface elements. Tunnelling with shield TBM is simulated using 3D FEM. According to the change of the direction and magnitude of the fractured zone, Sectional forces such as axial force, shear force and bending moment are monitored and vertical displacement at the ground surface is measured. Based on the stability analysis with the results obtained from the numerical analysis, it is possible to predict fractured zone ahead of the shield TBM and ensure the stability of the tunnel structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.