• Journal of Korean Society for Atmospheric Environment
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• v.21 no.E1
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• pp.37-39
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• 2005

It is well perceived that micrometeorological approach is one of the most reliable method for the quantification of vertical fluxes of trace components in the atmosphere. In this study, the feasibility of relaxed eddy accumulation (REA) method is discussed with respect to its reliability in the field application. Knowing that the use of micrometeorological approaches requires validation of analytical uncertainties involved, the problems and issues associated with its application are discussed to stimulate the proper employment of such technique in the field study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.115-120
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• 2011

The strength of reinforced concrete members has uncertainty from material properties of, concrete and reinforcements, section dimensions, and construction errors and so on. The accurate evaluation of these uncertainties is necessary to assure the reasonable safety. The uncertainties should be taken into account in design using structural reliability theory which requires probabilistic models for such uncertainties. In current Korean design code, most reliability evaluations were performed based on foreign data because of lack of local data. In this paper, the probabilistic models for yield strength of reinforcements were developed based on local data. The effects of various factors, nominal yield strength, diameter of reinforcements, and companies, on the models are also examined. According to data analysed, the effects of those factors are not significant. The probability model for yield strength of reinforcements in Korea can be expressed with Beta distribution based on collected data.

• Journal of Environmental Policy
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• v.11 no.3
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• pp.3-24
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• 2012

Many ecosystems in Korea are currently managed by government organizations. Thus, public managers' decision-making has great influence on the management of ecosystems in Korea, and their decision-making could influence the matter of whether the ecosystems of Korea are managed effectively. This paper regards the goal of management of ecosystems as securing the sustainablilty of target ecosystems, and investigates public managers' decision-making and their psychological attitude on the management of ecosystems. Basically, managerial activities on ecosystems have uncertainties and usually public managers utilize the knowledge of law, science, intergovernmental relations, and local governance as their references for decision-making. To elucidate public managers' managerial decision-making on ecosystems, this paper adopts some psychological theories in explaining the judgment of human beings under uncertainties. Effective ecosystem management by public managers can be judged by how public managers adopt and utilize all of the above mentioned four kinds of knowledge on ecosystem management. An important factor in order to let them utilize the four kinds of knowledge is policy support. Therefore, as conclusion, this paper recommends some relevant policy measures that can support the ecosystem management of public managers.

• Magazine of the Korea Concrete Institute
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• v.9 no.2
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• pp.145-151
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• 1997

In concrete mix design we need the informations of the codes, the specifications, and the experiences of experts. However we can't consider all factors regarding concrete mix design. The final acceptance depends on concrete quality control test results. In this process we meet the uncertainties of materials. temperature, site environmental situations, personal skillfulness. and errors in calculations and testing process. Then the mix design adjustments must be made. Concrete mix design and adjustments arc somewhat complicated, time-consuming. and uncertain tasks. In this paper, as a tool to minimize the uncertainties and errors the neural network is applied to the concrete mix design. Input data to train and test the neural network are obtained numerically from the results of design following the concrete standard specifications of Korea. The 28-days compressive strengths which are variate according to the uncertainties and errors are considered. The results show that neural networks have a strong potential as a tool for concrete mix design.

• Journal of the Korean Geotechnical Society
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• v.33 no.7
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• pp.29-41
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• 2017

There are uncertainties about the seismic load caused by seismic waves, which cannot be predicted due to the characteristics of the earthquake occurrence. Therefore, it is necessary to consider these uncertainties by probabilistic analysis. In this paper, procedures to develop a fragility curve that is a representative method to evaluate the safety of a structure by stochastic analysis were proposed for cut slopes. Fragility curve that considers uncertainties of soil shear strength parameters was prepared by Monte Carlo Simulation using pseudo static analysis. The fragility curve considering the uncertainty of the input ground motion was developed by performing time-history seismic analysis using selected 30 real ground input motions and the Newmark type displacement evaluation analysis. Fragility curves are represented as the cumulative probability distribution function with lognormal distribution by using the maximum likelihood estimation method.

• Earthquakes and Structures
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• v.10 no.1
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• pp.141-161
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• 2016

In seismic fragility and risk analysis, the definition of structural limit state (LS) capacities is of crucial importance. Traditionally, LS capacities are defined according to design code provisions or using deterministic pushover analysis without considering the inherent randomness of structural parameters. To assess the effects of structural randomness on LS capacities, ten structural parameters that include material strengths and gravity loads are considered as random variables, and a probabilistic pushover method based on a correlation-controlled Latin hypercube sampling technique is used to estimate the uncertainties in LS capacities for four typical reinforced concrete frame buildings. A series of ten LSs are identified from the pushover curves based on the design-code-given thresholds and the available damage-controlled criteria. The obtained LS capacities are further represented by a lognormal model with the median $m_C$ and the dispersion ${\beta}_C$. The results show that structural uncertainties have limited influence on $m_C$ for the LSs other than that near collapse. The commonly used assumption of ${\beta}_C$ between 0.25 and 0.30 overestimates the uncertainties in LS capacities for each individual building, but they are suitable for a building group with moderate damages. A low uncertainty as ${\beta}_C=0.1{\sim}0.15$ is adequate for the LSs associated with slight damages of structures, while a large uncertainty as ${\beta}_C=0.40{\sim}0.45$ is suggested for the LSs near collapse.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.57-65
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• 2024

In a fire-resistant structure, uncertainties arise in factors such as ventilation, material elasticity modulus, yield strength, coefficient of thermal expansion, external forces, and fire location. The ventilation uncertainty affects thefactor contributes to uncertainties in fire temperature, subsequently impacting the structural temperature. These temperatures, combined with material properties, give rise to uncertain structural responses. Given the nonlinear behavior of structures under fire conditions, calculating fire fragility traditionally involves time-consuming Monte Carlo simulations. To address this, recent studies have explored leveraging machine learning algorithms to predict fire fragility, aiming to enhance efficiency while maintaining accuracy. This study focuses on predicting the fire fragility of a steel moment frame building, accounting for uncertainties in fire size, location, and structural material properties. The fragility curve, derived from nonlinear structural behavior under fire, follows a log-normal distribution. The results demonstrate that the proposed method accurately and efficiently predicts fire fragility, showcasing its effectiveness in streamlining the analysis process.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.10
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• pp.1065-1071
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• 2005

Many techniques of leak detection in pipeline systems have developed based on the propagation wave speeds and wave attenuation. In this paper, the transient analysis methodology is used for calculating the wave speed in the plastic pipe and a frequency analysis methodology is developed for leakage detection in water pipe networks. Data acquisition system for pressurized pipeline system were designed md fabricated to obtain high frequency pressure data. The methodology properly handles the unavoidable uncertainties in measurement and modeling error. Based on information from head pressure test data, it provides leak prediction capability from the transient events with leakage.

• Journal of Environmental Science International
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• v.18 no.3
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• pp.271-281
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• 2009

In order to reduce the uncertainties and improve the air flow field, objective analysis using observational data is chosen as a method that enhances the reality of meteorology. To improve the meteorological components, the radius influence and nudging coefficient of the objective analysis should perform a adequate value on complex area for the objective analysis technique which related to data reliability and error suppression. Several numerical experiments have been undertaken in order to clarify the impacts of the radius influence and nudging coefficient of the objective analysis on meteorological environments. By analyzing practical urban ground conditions, we revealed that there were large differences in the meteorological differences in each case. In order to understand the quantitative impact of each run, the Statistical analysis by estimated by MM5 revealed the differences by the synoptic conditions. The strengthening of the synoptic wind condition tends to be well estimated when using quite a wide radius influence and a small nudging coefficient. On the other hand, the weakening of the synoptic wind is opposite.

• Analytical Science and Technology
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• v.23 no.4
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• pp.363-370
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• 2010

This study was performed to validate reference materials (RMs) for proficiency testing (PT) in waste inter-laboratories. Dioxin RMs were prepared from fly ash in industrial incinerators. The relative standard deviations (RSDs) of analytical results were 2.6~15.7% for the dioxin RMs in 10 replicates (between and withinbottles). Data were collected and statistical analysis was performed by the One-way ANOVA test. The combined uncertainties of target isomers in dioxin analysis were 0.114~7.091.