• Korean Journal of Construction Engineering and Management
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• v.6 no.4 s.26
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• pp.91-100
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• 2005

Design-build that a single entity performs whole construction process under singular responsibility is one of the project delivery system comes to take expectation effects of time savings, cost saving, and quality assurance. On the other hand, a current domestic method for selecting a design-build contractor is difficult to reflect the purpose of owner and the project characteristics when owner selects the design-build contractor. The purpose of this research is to suggest a model for deciding evaluation weights in design-build which can reflect the purpose of owner and the project characteristics. This research can help owner to select the best suitable design-build contractor for the project.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.213-223
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• 2009

In geoid modelling field, it is very important the GPS/leveling data because it could be check-out the accuracy of gravimetric geoid and computed the hybrid geoid. In this study, GPS surveying was accomplished in the test area including mountainous area to improve the GPS/leveling data density in Korea. And the geometric geoidal heights was calculated using the GPS/leveling data in the test area and the accuracy of the geoidal heights was analyzed. For this study, GPS surveying was accomplished on the 211 1st and 2nd order benchmarks in Gyeongbuk province and 198 GPS/leveling data were achieved after both baseline analysis and network adjustment. Geometric geoidal heights were calculated using these 198 GPS/leveling data and the accuracy analysis was done by comparison with the geoidal heights from EGM2008 geopotential model. The results showed that the bias and standard deviation computed from 190 GPS/leveling data after gross removal was -0.185$\pm$0.079m. And also, the accuracy analyses according to the benchmark order, baseline length, and altitude were accomplished.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.509-520
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• 2015

College Scholastic Ability Test(CSAT) is a primary test to evaluate the study achievement of high-school students and used by most universities for admission decision in South Korea. Because its level of difficulty is a significant issue to both students and universities, the government makes a huge effort to have a consistent difficulty level every year. However, the actual levels of difficulty have significantly fluctuated, which causes many problems with university admission. In this paper, we build two types of data-driven prediction models to predict correct answer rate and to identify significant factors for CSAT English test through accumulated test data of CSAT, unlike traditional methods depending on experts' judgments. Initially, we derive candidate question-specific factors that can influence the correct answer rate, such as the position, EBS-relation, readability, from the annual CSAT practices and CSAT for 10 years. In addition, we drive context-specific factors by employing topic modeling which identify the underlying topics over the text. Then, the correct answer rate is predicted by multiple linear regression and level of difficulty is predicted by classification tree. The experimental results show that 90% of accuracy can be achieved by the level of difficulty (difficult/easy) classification model, whereas the error rate for correct answer rate is below 16%. Points and problem category are found to be critical to predict the correct answer rate. In addition, the correct answer rate is also influenced by some of the topics discovered by topic modeling. Based on our study, it will be possible to predict the range of expected correct answer rate for both question-level and entire test-level, which will help CSAT examiners to control the level of difficulties.