• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.75-82
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• 1994

It is known that collinearity among the explanatory variables in generalized linear models inflates the variance of maximum likelihood estimators. A ridge-type estimator is presented using penalized likelihood. A method for choosing a shrinkage parameter is discussed and this method is based on a prediction-oriented criterion, which is Mallow's $C_L$ statistic in a linear regression setting.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.501-504
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• 2004

This paper proposes degree of hydration based shrinkage prediction model of 40MPa HPC. This model shows degree of hydration which is defined as the ratio between the hydrated cement mass and the initial mass of cement is very closely related to shrinkage deformation. In this study, degree of hydration was determined by CEMHYD-3D program of NIST. Verification of the predicted degree of hydration is performed by comparison between test results of compressive strength and estimated one by CEMHYD-3D. Proposed model is determined by statistical nonlinear analysis using the program Origin of Origin Lab. Co. To get coefficients of the model, drying shrinkage tests of four specimen series were followed with basic material tests. Testes were performed in constant temperature /humidity chamber, with difference moisture curing ages to know initial curing time effect. Verification with another specimen, collected construction field of FCM bridge, was given in the same condition as pre-tested specimens. Finally, all test results were compared to propose degree of hydration based model and other code models; AASHTO, ACI, CEB-FIP, JSCE, etc.

• Structural Engineering and Mechanics
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• v.50 no.5
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• pp.635-652
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• 2014

Creep and shrinkage behaviour of an ultra lightweight cement composite (ULCC) up to 450 days was evaluated in comparison with those of a normal weight aggregate concrete (NWAC) and a lightweight aggregate concrete (LWAC) with similar 28-day compressive strength. The ULCC is characterized by low density < 1500 $kg/m^3$ and high compressive strength about 60 MPa. Autogenous shrinkage increased rapidly in the ULCC at early-age and almost 95% occurred prior to the start of creep test at 28 days. Hence, majority of shrinkage of the ULCC during creep test was drying shrinkage. Total shrinkage of the ULCC during the 450-day creep test was the lowest compared to the NWAC and LWAC. However, corresponding total creep in the ULCC was the highest with high proportion attributed to basic creep (${\geq}$ ~90%) and limited drying creep. The high creep of the ULCC is likely due to its low elastic modulus. Specific creep of the ULCC was similar to that of the NWAC, but more than 80% higher than the LWAC. Creep coefficient of the ULCC was about 47% lower than that of the NWAC but about 18% higher than that of the LWAC. Among five creep models evaluated which tend to over-estimate the creep coefficient of the ULCC, EC2 model gives acceptable prediction within +25% deviations. The EC2 model may be used as a first approximate for the creep of ULCC in the designs of steel-concrete composites or sandwich structures in the absence of other relevant creep data.

• Genomics & Informatics
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• v.17 no.4
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• pp.47.1-47.12
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• 2019

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5A
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• pp.307-315
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• 2012

Recently, ultra high performance fiber reinforced concrete (UHPFRC) having over 180 MPa compressive strength and 10 MPa tensile strength has been developed in Korea. However, UHPFRC represents different material properties with normal concrete (NC) and conventional high performance concrete (HPC) such as a high early age autogenous shrinkage and a rapid dry on the surface, because it has a low water-binder ratio and high fineness admixtures without coarse aggregate. In this study, therefore, to propose suitable experimental methods and regulations, and to evaluate mechanical properties at a very early age for UHPFRC, setting, shrinkage and tensile tests were performed. From the setting test results, paraffin oil was an appropriate material to prevent drying effect on the surface, because if paraffin oil is applied on the surface, it can efficiently prevent the drying effect and does not disturb or catalyze the hydration of cement. From the ring-test results, it was defined that the shrinkage stress is generated at the time when the graph tendency of temperature and strain of inner steel ring is changed. By comparing with setting test result, the shrinkage stress was firstly occurred as the penetration resistance of 1.5 MPa was obtained, and it was about 0.6 and 2.1 hour faster than those of initial and final sets. So, the starting time of autogenous shrinkage measurement (time-zero) of UHPFRC was determined when the penetration resistance of 1.5 MPa was obtained. Finally, the tensile strength and elastic modulus of UHPFRC were measured from near initial setting time by using a very early age tensile test apparatus, and the prediction models for tensile strength and elastic modulus were proposed.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.709-712
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• 2008

RC super tall buildings are planned and constructed recently in domestic area. Concrete is characterized by time dependant material such as creep and shrinkage. For this properties of concrete, differential shortening is one of the main issues on super tall buildings construction. This study includes material research, which is performing as a pre design stage to solve differential shortening on Lotte Super Tower Jamsil core structure(50, 60, & 70 MPa). The major part of this study is composed with comparison and analysis between experimental data and predicted data on total shrinkage and total compliance which were used on design stage. Four models, ACI209R Model, Ba${\check{z}}$ant-Baweja B3 Model, CEB MC99 Model, & GL2000 Model, were employed to predict them. It also tries to seek a proper model for high strength and high performance concrete in the case of no concrete test.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.442-445
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• 2006

This paper presents an analytical prediction of nonlinear characteristics and behavior characteristics PSC structures with un-bonded tendon system. In this paper, a numerical model for un-bonded tendon is proposed based on the finite element method, which can represent straight or curved un-bonded tendon behavior. this model and time-dependent material model used to investigate the time-dependent behavior of un-bonded prestressed concrete structures. The accuracy and objectivity of the assessment process may be enhanced by the use of sophisticated nonlinear finite element analysis program. A computer program, named RCAHEST(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of concrete structures and steel plate was used. The material nonlinearities are taken into account by comprising the tension, compression, and shear models of cracked concrete and models for reinforcements and tendons in the concrete. The smeared crack approach is incorporated. It accounts for the aging, creep and shrinkage of concrete and the stress relaxation of prestressed steel. The proposed un-bonded tendon model and numerical method of un-bonded prestressed concrete structures is verified by comparison with reliable experimental results.

• Journal of Korean Society of societal Security
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• v.1 no.3
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• pp.51-57
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• 2008

The long-term behaviors of prestressed concrete cable-stayed bridges are considerably influenced by the time dependant material characteristics such as creep and shrinkage. This study investigated the influences of the change of relative humidity by application of the CEB-FIP model and ACI model, which are generally used in the prediction of long-term behavior of concrete structures. In case of the moment of girder, CEB-FIP model predicted a bigger effect of relative humidity change than the ACI model. Furthermore, the effect was significant. Also, the long-term behaviors between these models were different each other even under the same material condition. Therefore, the prediction of the long-term behavior should be compensated after comparative analysis with the results of material tests of each construction site and between the different models.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.149-159
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• 1998

The reasonable prediction of time-dependent deformation of prestressed concrete(PSC) box girder bridges is very important for accurate construction as well as good serviceability. The long-term behavior is mostly influenced by the probabilistic characteristic of creep and shrinkage. This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box been taken into account - model uncertainty, parameter variation and environmental condition. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measure are examined to quantify the sensitivity of the outputs of each of the input variables. These are rank correlation coefficient(RCC), partical rank correlation coefficient(PRCC) and standardiozed rank regression coefficient(SRRC) computed on the ranks of the observations. Three creep and shrinkage models - i. e., ACI model. CEB-FIP model and the model in Korea Highway Bridge Specification - are studied. The creep model uncertainy factor and the relative humidity appear to be the most dominant factors with regard to the model output uncertainty.