• Geomechanics and Engineering
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• v.35 no.2
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• pp.165-179
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• 2023

This study investigates the uplift capacity of a single vertical belled pile buried at shallow depth in dry sand. The laboratory model experiments are conducted with different pile-tip angles and relative densities. In addition, image and FEM analyses are performed to observe the failure surface of the belled pile for different pile-tip angles and relative densities. Accordingly, the uplift capacity and failure angle in the failure surface of the belled pile were found to depend on the belled pile-tip angle and relative density. A predictive model for the uplift capacity of the belled pile was proposed considering the relative density and belled pile-tip angle based on a previous limit equilibrium equation. To validate the applicability of the proposed model, the values calculated using the proposed and previous models were compared to those obtained through a laboratory model experiment. The proposed model had the best agreement with the laboratory model experiment.

• Korea-Australia Rheology Journal
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• v.14 no.1
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• pp.25-32
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• 2002

We report the first steps of a collaborative project between the University of Queensland, Polyflow, Michelin, SK Chemicals, and RMIT University, on simulation, validation and application of a recently introduced constitutive model designed to describe branched polymers. Whereas much progress has been made on predicting the complex flow behaviour of many - in particular linear - polymers, it sometimes appears difficult to predict simultaneously shear thinning and extensional strain hardening behaviour using traditional constitutive models. Recently a new viscoelastic model based on molecular topology, was proposed by McLeish and carson (1998). We explore the predictive power of a differential multi-mode version of the porn-pom model for the flow behaviour of two commercial polymer melts: a (long-chain branched) low-density polyethylene (LDPE) and a (linear) high-density polyethylene (HDPE). The model responses are compared to elongational recovery experiments published by Langouche and Debbaut (19c99), and start-up of simple shear flow, stress relaxation after simple and reverse step strain experiments carried out in our laboratory.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.22 no.1
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• pp.17-25
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• 2024

Hydraulic conductivity is a critical design parameter for buffers in high-level radioactive waste repositories. Most employed prediction models for hydraulic conductivity are limited to various types of bentonites, the main material of the buffer, and the associated temperature conditions. This study proposes the utilization of a novel integrated prediction model. The model is derived through theoretical and regression analyses and is applied to all types of compacted bentonites when the relationship between hydraulic conductivity and dry density for each compacted bentonite is known. The proposed model incorporates parameters such as permeability ratio, dynamic viscosity, and temperature coefficient to enable accurate prediction of hydraulic conductivity with temperature. Based on the results obtained, the values are in good agreement with the measured values for the selected bentonites, demonstrating the effectiveness of the proposed model. These results contribute to the analysis of the hydraulic behavior of the buffer with temperature during periods of high-level radioactive waste deposition.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.109-120
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• 1993

Using a noninformative prior and a gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p-th order statistic of n' future observations from the Burr distribution have been obtained. In additions, we examine the sensitivities of the results to the choice of model.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.139-145
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• 2002

This paper deals with the problem of obtaining the Bayesian predictive density function and the prediction intervals for a future observation and the p-th order statistics of n future observations for the exponential model under the censored sampling with incomplete information.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.179-192
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• 2001

When a statistical model has a hierarchical structure such as multilayer perceptrons in neural networks or Gaussian mixture density representation, the model includes distribution with unidentifiable parameters when the structure becomes redundant. Since the exact structure is unknown, we need to carry out statistical estimation or learning of parameters in such a model. From the geometrical point of view, distributions specified by unidentifiable parameters become a singular point in the parameter space. The problem has been remarked in many statistical models, and strange behaviors of the likelihood ratio statistics, when the null hypothesis is at a singular point, have been analyzed so far. The present paper studies asymptotic behaviors of the maximum likelihood estimator and the Bayesian predictive estimator, by using a simple cone model, and show that they are completely different from regular statistical models where the Cramer-Rao paradigm holds. At singularities, the Fisher information metric degenerates, implying that the cramer-Rao paradigm does no more hold, and that he classical model selection theory such as AIC and MDL cannot be applied. This paper is a first step to establish a new theory for analyzing the accuracy of estimation or learning at around singularities.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.535-541
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• 2013

Expressways are built to provide for high levels of safety and efficiency in the movement of large traffic volumes at high speeds. However, Gyeongbu and Namhae expressways in the country which are often suffering from the transportation problems with increased import and exoprt cargo trucks including the container vehicles are the major expressways having the Busan Port as the origin and destination regions, and are required to study the expressway traffic characteristics for reducing the transportation problems. We therefore attempted to suggest the expressway density predictive model through the correlation analysis between lane occupancy and mean density at the 8-lane basic segments of the above major expressways in the country.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.850-855
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• 2005

Model was developed to predict the growth of Listeria monocytogenes in raw pork. Experiment condition for model development was full 5-by-7 factorial arrangements of temperature (0, 5, 10, 15, and $20^{\circ}C$) and time (0, 1, 2, 3, 18, 48, and 120 hr). Gompertz values A, C, B, and M, and growth kinetics, exponential growth rate (EGR), generation time (GT), lag phase duration (LPD), and maximum population density (MPD) were calculated based on growth increased data. GT and LPD values gradually decreased, whereas EGR value gradually increased with increasing temperature. Response surface analysis (RSA) was carried out using Gompertz B and M values, to formulate equation with temperature being main control factor. This equation was applied to Gompertz equation. Experimental and predictive values for GT, LPD, and EGR, compared using the model, showed no significant differences (p<0.01). Proposed model could be used to predict growth of microorganisms for exposure assessment of MRA, thereby allowing more informed decision-making on potential regulatory actions of microorganisms in raw pork.