• Geomechanics and Engineering
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• 제38권2호
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• pp.165-177
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• 2024

Liquefaction phenomenon refers to a phenomenon in which excess pore water pressure occurs when a dynamic load such as an earthquake is rapidly applied to a loose sandy soil ground where the ground is saturated, and the ground loses effective stress and becomes liquid. The laboratory repetition test for liquefaction evaluation can be performed through a repeated triaxial compression test and a repeated shear test. In this regard, this study attempted to evaluate the effects of the relative density of sand on the liquefaction resistance strength according to particle size distribution using repeated triaxial compression tests, and additional experimental verification using numerical analysis was conducted to overcome the limitations of experimental equipment. As a result of the experiment, it was confirmed that the liquefaction resistance strength increased as the relative density increased regardless of the classification of soil, and the liquefaction resistance strength of the SP sample close to SW was quite high. As a result of numerical analysis, it was confirmed that the liquefaction resistance strength increased as the confining pressure increased under the same relative density, and the liquefaction resistance strength did not decrease below a certain limit even though the confining pressure was significantly reduced at a relatively low relative density. This is judged to be due to a change in confining pressure according to the depth of the ground. As a result of analyzing the liquefaction resistance strength according to the frequency range, it was confirmed that there was no significant difference from the laboratory experiment results in the basic range of 0.1 to 1.0 Hz.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.187-191
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• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• 대한수학회지
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• 제48권3호
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• pp.475-486
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• 2011

The paper deals with repeated low-density burst error detecting codes with a specied weight or less. Linear codes capable of detecting such errors have been studied. Further codes capable of correcting and simultaneously detecting such errors have also been dealt with. The paper obtains lower and upper bounds on the number of parity-check digits required for such codes. An example of such a code has also been provided.

• Journal of the Korean Statistical Society
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• 제26권2호
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• pp.245-259
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• 1997

The paper shows that certain hierachical Bayes (HB) predictors for small domain data in repeated surveys "universally" or "stochastically" dominate all linear unbiased predictors. Also, the HB predictors are "best" within the class of all equivariant predictors under a certain group of transformations.tain group of transformations.

• Communications for Statistical Applications and Methods
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• 제14권3호
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• pp.593-600
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• 2007

Under repeated surveys, missing values often appear for various reasons and are replaced by new samples. It is investigated that the existing estimator in repeated survey by Jessen (1942), which has been originally developed for the new samples of fixed size, can be used in such situation where the size of new samples is random. It is shown that the proposed estimator has smaller variance than the sample mean.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2011년도 춘계학술대회
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• pp.95-99
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• 2011

The research analyzes structure models of Repeated Measurement Design (RMD) and Hierarchical Design (HD). The experimental unit of RMD model is living organisms, such as human. In contrast, HD is used when all the factors are random. The HD models are derived from R:B:A, R:C:B:A and R:C:($A{\times}B$).

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 추계 학술발표회 제16권2호
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• pp.627-630
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• 2004

This paper investigates a study on the bending behavior of beam strengthened with CFS under repeated loading. The test specimen consisted of 20cm in width, 40cm in depth, and 15cm in CFS width. We find the strength decrease under repeated loading test comparing with the monotonic incremental loading test.

• 한국독성학회:학술대회논문집
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• 한국독성학회 2003년도 추계학술대회
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• pp.150-150
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• 2003

Botulinum toxin type A was intramuscularly administered to Sprague-Dawley rats in both single and 28-day repeated dose toxicity studies. In the single dose toxicity study performed at 25, 50, 100, and 200 ng/kg, LD50 was estimated to be 70.71 ng/kg for males and 97.63 ng/kg for females.(omitted)

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• Journal of the Korean Data and Information Science Society
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• 제21권1호
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• pp.1-9
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• 2010

본 논문은 분할구 실험에서 반복측정 요인이 처치의 한 요인으로 고려될 때, 실험자료의 분석을 위한 혼합모형과 모형내 미지모수의 추론을 위한 방법을 논의한다. 반복측정 요인으로 공간요인을 고려하고 공간요인의 수준은 분할구에 할당되나 연구자가 임의로 배정할 수 없는 실험환경이 가정된다. 이러한 실험의 특성을 갖는 자료벡터의 확률분포로 복합대칭의 공분산 구조를 갖는 다변량 정규분포를 논의하고 있다. 또한, 가정된 실험환경에 부합하는 적합한 자료의 예를 통하여 제시된 모형의 타당성과 관련모수들의 추론방법을 다루고 있다.