• Steel and Composite Structures
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• v.26 no.3
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• pp.375-386
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• 2018

Existing design codes for steel-concrete composite structures give only general information about the shear connection provided by headed studs in group arrangement. Grouting of the openings in prefabricated concrete slabs, where the grouped headed studs are placed in the deck pockets is alternative to cast-in-place decks to accomplish fast execution of composite structures. This paper considers the possibility to reduce the distance between the studs within the group, bellow the Eurocode limitations. This may lead to increased competitiveness of the prefabricated construction because more studs are placed in the group if negative effectives of smaller distances between studs are limited. The main purpose of this work is to investigate these limits and propose an analytical calculation model for prediction of the shear resistance of grouped stud arrangements in the deck pockets. An advanced FEA model, validated by results of push-out experiments, is used to analyze the shear behavior of the grouped stud with smaller distance between them than recommended by EN 1994-1. Calculation model for shear resistance, which is consistent with the existing Eurocode rules, is proposed based on a newly introduced equivalent diameter of the stud group, $d_G$. The new calculation model is validated by comparison to the results of FE parametric study. The distance between the studs in the longitudinal direction and the number of stud rows and columns in the group are considered as the main variables.

• Ocean and Polar Research
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• v.27 no.2
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• pp.189-195
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• 2005

Several models predict large and potentially abrupt ocean circulation changes due to anthropogenic greenhouse-gas emissions. These circulation changes drive-in the models-considerable oceanic oxygen trend. A sound estimate of the observed oxygen trends can hence be a powerful tool to constrain predictions of future changes in oceanic deepwater formation, heat and carbon dioxide uptake. Estimating decadal scale oxygen trends is, however, a nontrivial task and previous studies have come to contradicting conclusions. One key potential problem is that changes in the historical observation network might introduce considerable errors. Here we estimate the likely magnitude of these errors for a subset of the available observations in the Southern Ocean. We test three common data analysis methods south of Australia and focus on the decadal-scale trends between the 1970's and the 1990's. Specifically, we estimate errors due to sparsely sampled observations using a known signal (the time invariant, temporally averaged, World Ocean Atlas 2001) as a negative control. The crossover analysis and the objective analysis methods are for less prone to spatial sampling location biases than the area averaging method. Subject to numerous caveats, we find that errors due to sparse sampling for the area averaging method are on the order of several micro-moles $kg^{-1}$. for the crossover and the objective analysis method, these errors are much smaller. For the analyzed example, the biases due to changes in the spatial design of the historical observation network are relatively small compared to the tends predicted by many model simulations. This raises the possibility to use historic oxygen trends to constrain model simulations, even in sparsely sampled ocean basins.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.188-194
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• 2006

Three-dimensional topological data is essential for 3D modeling and application such as emergency management and 3D network analysis. This paper reviewed current 3D topological data model and developed a method to construct 3D topological node-relation data structure from 2D computer aided design (CAD) data. The method needed two steps with medial axis-transformation and topological node-relation algorithms. Using a medial-axis transformation algorithm, the first step is to extract skeleton from wall data that was drawn polygon or double line in a CAD data. The second step is to build a topological node-relation structure by converting rooms to nodes and the relations between rooms to links. So, links represent adjacency and connectivity between nodes (rooms). As a result, with the conversion method 3D topological data for micro-level sub-unit of each building can be easily constructed from CAD data that are commonly used to design a building as a blueprint.

• Environmental Engineering Research
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• v.25 no.3
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• pp.309-315
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• 2020

Slaughterhouse wastewater (SWW) is characterized as one of the most harmful agriculture and food industrial wastewaters due to its high organic content. The emissions of SWW would cause eutrophication of surface water and pollution of groundwater. This study developed a pilot scale anaerobic-aerobic slaughterhouse wastewater treatment process (AASWWTP) to enhance the chemical oxygen demand (COD) and total nitrogen (TN) removal. The optimum supernatant reflux position and ratio for TN removal were investigated through the modified Box-Behnken design (BBD) experiments. Results showed that COD could be effectively reduced over the whole modified BBD study and the removal efficiency was all higher than 98%. The optimum reflux position and ratio were suggested to be 2 alure and 100%, respectively, where effluent TN concentration was satisfied with the forthcoming Chinese discharge standard of 25 mg/L. Anaerobic digestion and ammonia oxidation were considered as the main approaches for COD and TN removal in the AASWWTP. The results of inorganic nutrients (K⁺, Na⁺, Ca²⁺ and Mg²⁺) indicated that the SWW was suitable for biological treatment and the correspondingly processes such as AASWWTP should be widely researched and popularized. Therefore, AASWWTP is a promising technology for SWW treatment but more research is needed to further improve the operating efficiency.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.382-395
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• 2011

Considering that GIS (Geographic Information Systems) has not been widely adopted by teachers despite educators' considerable efforts to incorporate it into secondary education, if some teachers voluntarily participate in GIS in-service education without guaranteed benefits, those teachers deserve attention. This study investigates why teachers actively participated in a GIS staff-development program offered by Seoul National University in South Korea. Questionnaires were distributed to teachers and interviews were conducted. Results indicate that active participants are mostly young teachers who are expected to have had experiences with GIS during pre-service education. Teachers see the potential of GIS, but they also worry about several issues. Participating teachers' primary motivation was to learn enough about GIS to incorporate it into their teaching. Suggestions to facilitate the incorporation of GIS into education are provided.

• Environmental Engineering Research
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• v.23 no.3
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• pp.288-293
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• 2018

The main objective of this study is to investigate the influence of hydraulic retention time (HRT) on biogas production during leachate treatment using an anaerobic reactor type Upflow Anaerobic Sludge Blanket. For this purpose, four HRTs ranging from 12 to 48 h were experienced. The obtained results showed that higher amount of biogas could be produced during longer HRTs. However, HRTs longer than 48 h could not affect clearly the biogas generation and considered as unnecessary given the small additional amount of biogas produced during the degradation process. A volume of $0.434L/L_{leachate}/d$ was achieved during the treatment with a HRT of 48 h. The higher biogas production, the smaller chemical oxygen demand (COD) values achieved. Besides, COD removal and biogas production positively correlate, showing that the active biomass has degraded effectively the organic load to produce biogas. Moreover, all the analyzed physicochemical parameters have experienced a decrease after 48 h except for the pH, which increased to approximately neutral value. More precisely, a decrease of 93.8%, 89.7%, 95%, 70%, 77%, and 84.4% was recorded for COD, electrical conductivity, total suspended solid, turbidity, $NH_4{^+}-N$, and $NO_3{^-}-N$, respectively.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.99-111
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• 2005

A fuzzy logic based data integration was applied for geothermal potential mapping in Jeju Island. Several data sets, such as geological map, the density of drainage system, the distribution density of cinder cones, density of lineaments, aerial survey map for total magnetic intensity and total gamma ray, were collected as thematic map for the integration. Fuzzy membership function for all thematic maps were compared to the locations of the spa, which were used as ground-truth control points. The older geology, the lower density of drainage, cinder cones and lineaments, and the lower intensity of magnetic and gamma ray were showed the higher fuzzy membership function values, respectively. After integrating all thematic maps, the results of gamma operator with the gamma value of 0.75 was the highest success rate, and new geothermal potential zone is prospected in western north part of Jeju Island.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.34-40
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• 2005

We have used the microtremor method, with arrays of up to 96 m diameter, to carry out non-invasive estimation of shear-wave velocity profiles to a depth of 30 to 50 m in unconsolidated Quaternary Yarra Delta sediments. Two silt units (Coode Island Silt, and Fishermans Bend Silt) dominate our interpretation; the method yields shear velocities for these units with precision of $5\%$, and differentiates between the former, softer unit ($V_s$=130 m/sec) and the latter, firmer unit ($V_s$=235 m/sec). Below these silts, the method resolves a firm unit correlating with known gravels ($V_s$ 500 to 650 m/sec). Using surface traverses with the single-station H/V spectral ratio method, we show that the variation in thickness of the softer silt can be mapped rapidly but only qualitatively. The complexity of the geological section requires that array methods be used when quantitative shear-wave velocity profiles are desired.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• STI Policy Review
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• v.9 no.1
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• pp.99-132
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• 2018

Developing countries face numerous challenges in the process of building science, technology, and research capacity; in particular, the formation and accumulation of skilled S&T workforce. The lack of organized and sustainable higher education options (Master and Doctoral programs), nonexistent or low-quality academic programs, and the absence of research-oriented study options are some of the strong contributors for talented students to emigrate to developed countries. At the same time, the consolidation of a global knowledge economy, the internationalization of higher education, and the competition to attract foreign talent in industrialized countries present challenges for underdeveloped nations to retain their already scarce skilled human resources. In this context, student mobility has been used as a policy mechanism to cope with S&T workforce shortages in S&T laggard nations. It has also enabled opportunities for international cooperation to play a key role. While significant literature has been devoted to studying the gains of developed nations with the arrival and potential migration of the mobilized students, few scholarly inquiries have addressed the benefits and losses experienced by their countries of origin. More importantly, limited research can be found on policy options and policy implications for developing countries to deal with the dilemmas presented by the brain-drain/brain-circulation debate. The goal of this article is to study empirical evidence of an international cooperation initiative for student mobility between the Republic of Korea and Guatemala (implemented during 2009-2015). The paper analyzes this particular international cooperation experience from the perspective of the different actors involved and attempts to draw policy implications and policy options for developing countries to deal with potential risks and gains derived from international mobility for their S&T capacity building.