• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.305-316
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• 2021

Construction accidents occur due to a number of reasons-worker carelessness, non-adoption of safety equipment, and failure to comply with safety rules are some examples. Because much construction work is done outdoors, weather conditions can also be a factor in accidents. Past construction accident data are useful for accident prevention, but since construction accident data are often in a text format consisting of natural language, extracting construction hazards from construction accident data can take a lot of time and that entails extra cost. Therefore, in this study, we extracted construction hazards from 2,026 domestic construction accident reports using text mining and performed a seasonal analysis of construction hazards through frequency analysis and centrality analysis. Of the 254 construction hazards defined by Korea's Ministry of Land, Infrastructure, and Transport, we extracted 51 risk factors from the construction accident data. The results showed that a significant hazard was "Formwork" in spring and autumn, "Scaffold" in summer, and "Crane" in winter. The proposed method would enable construction safety managers to prepare better safety measures against outdoor construction accidents according to weather, season, and climate.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.157-165
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• 2011

With respect to the trend of highway traffic accident, highway accident is in decline, whileas, the fatality is on an increasing trend. Thus, many efforts to decrease highway traffic accidents and improve the safety, are required. In particular, in case of highway, the management standard by grade for accident black spot is designated. Thus, investing the effect factors by grade for highway traffic accident is required in detail. Thus, in this study, the factors affecting the traffic accidents among the environmental factors based on the graded data for the accident black spot in the applicable section targeting the Seoul-Pusan Express Highway, were reviewed; accident forecasting model which would analyze the characteristics of the accidents for determining the accident grade, was developed. As a result of establishing a model by using Quantification Theory of Type II, considering the characteristics of the dependent and independent variables based on the geometric structure, 'the fixed variable' among the variables relating to the accident, for the variables influencing over the accident grade, 'the type of vans, a chassis and people', 'the trailers, special vehicles and chassis people' and 'the negligence of watching and cloudy weather' were analyzed as common factors, in case of 'horizontal alignment', 'longitudinal slope' and, 'barricade' respectively.

• Journal of Energy Engineering
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• v.23 no.3
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• pp.186-190
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• 2014

Nuclear Power Plants require the control ability of chemical condition (pH) because pH control during transient accident such as LOCA makes an able the fission product removal capability to be maintained, stress corrosion cracking of stainless steel equipment to be prevented and the production of hydrogen by aluminum and zinc to be minimized. An NPP is designed to control the pH of containment spray and sump coolant using the spray additives 30% NaOH in the event of loss of coolant accident. In this paper, the pH of sump coolant of an NPP during LOCA was analyzed and the fission products removal constant and decontamination factor were calculated according to Standard Review Plan 6.5.2 related to spray chemical conditions of pH. The calculated pH value of recirculation mode using the computer code corresponds to 8.09~9.67, which meets the chemical environment regulation requirements. The fission product removal capability caused by containment spray system is performed to provide input to radiation analysis.

• Journal of Environmental Policy
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• v.9 no.2
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• pp.21-39
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• 2010

In this paper, water consumption annually for industries in Korea was estimated for the first time and based on this, an input-output model was prepared for water consumption analysis. Also making use of this, the direct and indirect water consumption effect according to industrial activities was analyzed and the total effect based on volume was broken down into each factor. The amount of water consumed for industries in Korea (excluding agriculture, forestry and fishery) was estimated about 7 billion and 692 million ton in 2003(excluding sea water). Classifying by industry, the one for electric power & water service accounted for almost half, 49.5%, metalworking industry for 24.3% and chemical industry for 5.0%. As the result of estimation for the direct and indirect water consumption inducement coefficients, the amount of water consumed per the production of one million won ranked the highest for electric power & water service as 113.8 ton and the next highest ones ranked as 49.6 ton for the first metalworking, 16.8 ton for textile and leather goods, and 11.9 ton for general machinery respectively. In the meantime, as the result of breaking down into each factor of total amount of water consumed by industry, it appeared that the ripple effect having on other industries was more than the direct effect.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.219-243
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• 2004

Generally, electricity market has monopoly market structure because of need of enormous investment for infrastructure. However, the introduction of competition in network industry as electricity is a tendency of the world with decreasing the effects of economy of scale due to the advancement of technology. Now, electricity industry restructuring is in progress but the competition in electricity retail market is not in force yet in Korea. Whether a effective competition exist or not is very important to policy decision maker who drive restructuring, but there are small numbers of quantitative researches on that. In this study, we estimated the effectiveness of competition in the electricity retail market through switching costs. If switching costs are high, consumers actually can be locked in incumbent firm in spite of introduction of competition. Therefore switching is a critical factor to determine effectiveness of competition and to estimate the size of switching costs quantitatively can proffer the information about whether the competition in the electricity retail market is effective or not in the future. We estimated switching costs using consumer' stated-preference data by conjoint analysis. In according to estimation results, the cost of switching process is not so high, but the relative brand loyalty of an incumbent company is significantly high. And the price is considered as the most important factor choosing an electric service commodity. Based on the empirical results, it is possible to analyze the relationship between suppliers' competitiveness resulted from management efficiency and customers' switching possibilities. The paper therefore provides guidance for suppliers in deciding to enter into retail competition and for policy makers in introducing retail competition. And it has a significance of estimating the switching costs directly.

• Journal of the Korean earth science society
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• v.22 no.3
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• pp.237-247
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• 2001

The concentrations of gaseous mercury (Hg) determined between two different time periods of the late 1980s and the late 1990s were compared to account for the effects of changes between source/sink relationships of atmospheric Hg in an urban area. The Hg concentration levels were different remarkably between the two time periods due possibly to changes in source/sink relationships. The results showed that the Hg levels in the former period were measured to be 14.4${\pm}$9.56ngm$^{-3}$ (N = 2714), whereas those of the latter period were characterized by approximately three-fold decreased values of 5.34${\pm}$3.92 ngm$^{-3}$ (N=2576). Using two independent measurement data sets, we examined the patterns of Hg distribution at different time scales. When analyzed over 24 hour scale, these data sets exhibited two distinctive distribution patterns. The former period showed enhanced concentration levels during daytime, while the latter period showed relative depletion during daytime. The patterns of the two data sets were also examined over seasonal scale. The results of two different time periods consistently showed the occurrences of maximum seasonal values during winter. The former period was characterized by seasonal patterns of fuel consumption with excessive Hg levels during winter. Conversely, no distinctive trend was apparent for the latter period with slight changes in concentration levels across seasons. In order to analyze the factors affecting Hg distributions between two different periods, we conducted both correlation and factor analysis on both all data sets and on seasonally divided data groups. The results of these analyses consistently indicate that the Hg concentration levels for two different time periods are regulated by distinctive source processes that are characteristic of each period.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.2
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• pp.127-135
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• 1983

A new approach is developed to analyze the reliability of the earth retaining wall using the concept of probability of failure, instead of conventional factor of safety. Many uncertainties, which are included in the conventional stability analysis, can be excluded by using the stochastic approach. And the reliability, more consistent with the reality, can be obtained by the simulation. The strength parameters of soil properties are assumed to be random variables to follow a generalized beta distribution. The interval [A, B] of the random variables could be determined using the maximum likelihood estimation. The pseudo-random values corresponding to the proposed beta distribution are generated using the rejection method. The probability of failure defined as follows, is obtained by using the Monte Carlo Method. $$P_f=\frac{M}{N}$$ where, $P_f$ : Probability of failure N : Total number of trials M : Total number of failure out of N A computer program is developed for the computation procedure mentioned above. Finally, a numerical example is solved using the developed program.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.597-605
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• 2012

This study was conducted in order to determine changes in the growth responses of Quercus serrata and Q. aliena which are potential natural vegetation of riverine in Korea under four light gradients within ambient and elevated $CO_2$ concentration and temperature levels. As a result, growth responses of two species were affected by light factor. Aboveground, belowground, plant biomass and root:shoot ratio of two species grown under the control and treatment were increased in the highest light level. Plant biomass and root:shoot ratio of two oak species were not significantly affected by elevated $CO_2$ and temperature, while aboveground biomass of them was lower in the treatment than control. Belowground biomass of Q. serrata was lower in the treatment than control under the gradients that are more than 70% of light level. As light intensity increases, elevated $CO_2$ and temperature promoted root growth of two oak species but had a negative effect on aboveground growth. According to the principal component analysis(PCA), two oak species were discriminatively arranged based on factor 1 and 2. Also, the reactions towards the ambient and elevated $CO_2$ and temperature were slightly different. It is clearly visible that all features relied on axis 1 and axis II are highly correlated with most variables except for stem and shoot length.

• Journal of Conservation Science
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• v.34 no.5
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• pp.387-396
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• 2018

Exposure to the natural environment can cause damage to domestic wooden cultural assets, such as temples. Deterioration is accelerated by biological damage and various environmental factors. In this study, meteorological factors were monitored by equipment installed at Beopjusa temple of Boeun province and Seonamsa temple of Suncheon province. A statistical model was applied to these data to predict the meteorological factors and to compare the predictive performance of each meteorological factor. The resulting correlation coefficient between air and dew point temperatures was highest, at 0.95, while the correlation coefficient for relative humidity had a moderate value(0.65) at both the Beopjusa and Seonamsa temples. Thus, a general linear model was found to be suitable for predicting air and dew point temperatures. An analysis of correlation between meteorological factors showed that there was strong positive correlation between air temperature and dew point temperature, and between solar radiation and evaporation at both sites. There was a weak positive correlation between air temperature and evaporation at Beopjusa temple. Wind speed was negatively correlated with both air temperature and relative humidity at Seonamsa temple. The wind speed at this location is higher than average in winter and lower than average in summer, and it was hypothesized that the low wind speed plays a role in reducing water evaporation in summer, when both air temperature and relative humidity are high. As a result, damage to the wooden buildings of Seonamsa temple is accelerated.