• Journal of the Korea Concrete Institute
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• v.18 no.1 s.91
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• pp.3-12
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• 2006

The behavior of concrete structures subject to fire is complex, depending on many factors. The factors usually considered in research include the level and endurance of temperatures in concrete and reinforcing bars, the mechanical properties of the steel and concrete, moisture contents, cover thickness, existence of eccentricity, and member geometry among others. Although there are a few sophisticated numerical models which can trace the effects of these important parameters on the residual capacity of reinforced concrete columns damaged by fire, practical predictive formulas are in need for rapid yet reasonable assessment in practice. The practical formulas are developed in this study for fire-damaged normal strength reinforced concrete square columns, which can approximate the predictions of those sophisticated numerical models with ease in use. The formulas take into account the effects of exposure time to fire, concrete strength, reinforcement ratio and sectional area. The developed formulas are seen to correlate with the predictions of numerical model in a reasonable agreement. Some examples are also presented in determining the residual strength, safety and additionally needed strengths for a fire-damaged reinforced concrete column.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Nutrition Research and Practice
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• v.9 no.4
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• pp.370-378
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• 2015

BACKGROUND/OBJECTIVES: Athletes generally desire changes in body composition in order to enhance their athletic performance. Often, athletes will practice chronic energy restrictions to attain body composition changes, altering their energy needs. Prediction of resting metabolic rates (RMR) is important in helping to determine an athlete's energy expenditure. This study compared measured RMR of athletic and non-athletic adolescents with predicted RMR from commonly used prediction equations to identify the most accurate equation applicable for adolescent athletes. SUBJECTS/METHODS: A total of 50 athletes (mean age of $16.6{\pm}1.0years$, 30 males and 20 females) and 50 non-athletes (mean age of $16.5{\pm}0.5years$, 30 males and 20 females) were enrolled in the study. The RMR of subjects was measured using indirect calorimetry. The accuracy of 11 RMR prediction equations was evaluated for bias, Pearson's correlation coefficient, and Bland-Altman analysis. RESULTS: Until more accurate prediction equations are developed, our findings recommend using the formulas by Cunningham (-29.8 kcal/day, limits of agreement -318.7 and +259.1 kcal/day) and Park (-0.842 kcal/day, limits of agreement -198.9 and +196.9 kcal/day) for prediction of RMR when studying male adolescent athletes. Among the new prediction formulas reviewed, the formula included in the fat-free mass as a variable [$RMR=730.4+15{\times}fat-free\;mass$] is paramount when examining athletes. CONCLUSIONS: The RMR prediction equation developed in this study is better in assessing the resting metabolic rate of Korean athletic adolescents.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.837-851
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• 2013

In construction industry, strength is a primary criterion in selecting a concrete for a particular application. The concrete used for construction gains strength over a long period of time after pouring the concrete. The characteristic strength of concrete is defined as the compressive strength of a sample that has been aged for 28 days. Neither waiting for 28 days for such a test would serve the rapidity of construction, nor would neglecting it serve the quality control process on concrete in large construction sites. Therefore, rapid and reliable prediction of the strength of concrete would be of great significance. On this backdrop, the method is proposed to establish a predictive relationship between properties and proportions of ingredients of concrete, compaction factor, weight of concrete cubes and strength of concrete whereby the strength of concrete can be predicted at early age. Multiple regression analysis was carried out for predicting the compressive strength of concrete containing Portland Pozolana cement using statistical analysis for the concrete data obtained from the experimental work done in this study. The multiple linear regression models yielded fairly good correlation coefficient for the prediction of compressive strength for 7, 28 and 40 days curing. The results indicate that the proposed regression models are effectively capable of evaluating the compressive strength of the concrete containing Portaland Pozolana Cement. The derived formulas are very simple, straightforward and provide an effective analysis tool accessible to practicing engineers.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.81-94
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• 2000

For the balanced variance component model, sometimes intraclass correlation coefficient is of interest. If there is little information about the parameter, then the reference prior(Berger and Bernardo, 1992) is widely used. Pettit nd Young(1990) considered a measrue of the effect of a single observation on a logarithmic Bayes factor. However, under such a reference prior, the Bayes factor depends on the ratio of unspecified constants. In order to discard this problem, influence diagnostic measures using the intrinsic Bayes factor(Berger and Pericchi, 1996) is presented. Finally, one simulated dataset is provided which illustrates the methodology with appropriate simulation based computational formulas. In order to overcome the difficult Bayesian computation, MCMC methods, such as Gibbs sampler(Gelfand and Smith, 1990) and Metropolis algorithm, are empolyed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.801-804
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• 2008

Fiber reinforced plastics (FRP) are light in weight, non-corrosive and exhibits high tensile strength. FRPs having superior material properties to corrosive steels have been widely replacing steel bars or tendons used in concrete structures as flexural reinforcements. Although current design guidelines for estimating shear strength of FRP concrete beam follow the format of conventional reinforced concrete design method, there are noticeable differences among the existing formulas in calculating the contributions of concrete to shear resistance. In this paper, the artificial neural network (ANN) technique is employed as an analytical alternative to existing methods for predicting shear capacity of FRP concrete beams. Influential factors on shear strength were identified through literature review and input in ANN and the ANN was trained for the target ultimate shear obtained from database. The results from ANN were compared with existing formulas for its accuracy. It was found that the developed ANN were more closely predicting the test data than those of the currently available predictive equations.

• Nutrition Research and Practice
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• v.7 no.4
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• pp.302-308
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• 2013

The emerging role of endothelial inflammation in diabetes has stimulated research interest in the effects of nutrition on related indices. In the current study we investigated whether the nutrient composition of dietary formula as reflected in glycemic index (GI) may be predictive of postprandial endothelial inflammation in non-diabetic subjects. A double-blinded, randomized, crossover study was conducted in non-diabetic subjects (n = 8/group). Each subject consumed three types of diabetes-specific dietary formulas (high-fiber formula [FF], high-monounsaturated fatty acid (MUFA) formula [MF] and control formula [CF]) standardized to 50 g of available carbohydrates with a 1-week interval between each. The mean glycemic index (GI) was calculated and 3-hour postprandial responses of insulin, soluble intercellular adhesion molecule-1 (sICAM-1), nitrotyrosine (NT) and free fatty acids (FFA) were measured. The MF showed the lowest mean GI and significantly low area under the curve (AUC) for insulin (P = 0.038), but significantly high AUCs for sICAM-1 (P<0.001) and FFA (P < 0.001) as compared to the CF and FF. The FF showed intermediate mean GI, but significantly low AUC for NT (P<0.001) as compared to the CF and MF. The mean GI was not positively correlated to any of the inflammatory markers evaluated, and in fact negatively correlated to changes in FFA (r = -0.473, P = 0.006). While the MF with the lowest GI showed the highest values in most of the inflammatory markers measured, the FF with intermediate GI had a modest beneficial effect on endothelial inflammation. These results suggest that nutrient composition of dietary formula as reflected in the GI may differently influence acute postprandial inflammation in non-diabetic subjects.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.165-172
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• 2021

Breaking wave is one of the important design factors in the design of coastal and port structures as they are directly related to various physical phenomena occurring on the coast, such as onshore currents, sediment transport, shock wave pressure, and energy dissipation. Due to the inherent complexity of the breaking wave, many empirical formulas have been proposed to predict breaker indices such as wave breaking height and breaking depth using hydraulic models. However, the existing empirical equations for breaker indices mainly were proposed via statistical analysis of experimental data under the assumption of a specific equation. In this study, a new Munk-type empirical equation was proposed to predict the height of breaking waves based on a representative linear supervised machine learning technique with high predictive performance in various research fields related to regression or classification challenges. Although the newly proposed breaker height formula was a simple polynomial equation, its predictive performance was comparable to that of the currently available empirical formula.

• Food Science of Animal Resources
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• v.32 no.1
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• pp.118-124
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• 2012

This study was conducted for quantitative microbial risk assessment (QMRA) of Clostridium perfringens with consumption on ham and sausage products in Korea, according to Codex guidelines. Frame-work model as product-retail-consumption pathway composed with initial contamination level, the time and temperature in distributions, and consumption data sets for ham and sausage products and also used the published predictive growth and dose-response models for Cl. perfringens. The simulation model and formulas with Microsoft@ Excel spreadsheet program using these data sets was developed and simulated with @RISK. The probability of foodborne disease by Cl. perfringens with consumption of the ham and sausage products per person per day was estimated as $3.97{\times}10^{-11}{\pm}1.80{\times}10^{-9}$. There were also noted that limitations in this study and suggestion for development of QMRA in the future in Korea.