• Proceedings of the Korean Nuclear Society Conference
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• 2001.10a
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• pp.181.2-181
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• 2001

• Journal of the Korean Society of Safety
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• v.33 no.5
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• pp.141-149
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• 2018

In a PCB factory, there is a corrosive chemical substance supply system that can causes major leakage accidents. These accidents can give rise to shut down the factory and do residents damage that cause enormous loss of properties. To mitigate these risks, it is necessary to provide a chemical disaster prevention system. Moreover, after considering the situation and environment of the production site, it is of great importance to build an optimal chemical accident prevention system by reflecting risk reduction measures from the point of process design and by assessing quantitative risk based on reliability data. However, because there was no established database of the reliability about facilities and equipment that can be used in the domestic, the business site and consulting organization had being used the reliability data such as USA CCPS(Center for Chemical Process Safety). In these days, Korean institutes are studying on reliability data utilization method of quantitative risk assessment for preventing chemical accidents and domestic utilization algorithms and storage bed of reliability data. This study presents samples of reliability database about the chemical substance supply system that constructed from the history data such as failure, maintenance for 10 years at a PCB factory. Also, this work proposes the safety design criteria for supply facilities of corrosive chemical substance by assessing quantitative risk on the basis of the reliability data.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.721-733
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• 2017

When temporary spent fuel storage pools at nuclear power plants reach their capacity limit, the spent fuel must be moved to an alternative storage facility. However, radioactive materials must be handled and stored carefully to avoid severe consequences to the environment. In this study, the risks of three potential accident scenarios (i.e., maritime transportation, an aircraft crashing into an interim storage facility, and on-site transportation) associated with the spent fuel transportation process were analyzed using a probabilistic approach. For each scenario, the probabilities and the consequences were calculated separately to assess the risks: the probabilities were calculated using existing data and statistical models, and the consequences were calculated using computation models. Risk assessment software was developed to conveniently integrate the three scenarios. The risks were analyzed using the developed software according to the shipment route, building characteristics, and spent fuel handling environment. As a result of the risk analysis with varying accident conditions, transportation and storage strategies with relatively low risk were developed for regulators and licensees. The focus of this study was the risk assessment methodology; however, the applied model and input data have some uncertainties. Further research to reduce these uncertainties will improve the accuracy of this model.

• Korean Journal of Construction Engineering and Management
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• v.8 no.4
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• pp.90-98
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• 2007

Although the construction industry has been rapidly increased at its size and technologies as a locomotive of the economy development in Korea, most construction companies have not paid their attention on the construction risks seriously. However, they have been experienced a lot of business deterioration and project instability during the IMF crisis and WTO entry, and trying to figure out a way to manage the construction intrinsic risks systematically. Some top ranked construction companies have already developed and been implementing a risk management system for the oversea construction projects applying for the marketing and bidding stage, but not for the domestic construction projects during construction phase yet. This paper proposes a construction risk management system model for the site personnel to understand and manipulate very easily in the construction project.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Earthquakes and Structures
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• v.21 no.6
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• pp.627-639
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• 2021

Fragility curves are being more significant as a useful tool for evaluating the relationship between the earthquake intensity measure and the effects of the engineering demand parameter on the buildings. In this paper, the effect of different site conditions on the vulnerability of the structures was examined through the fragility curves taking into account different strength capacities of the precast columns. Thus, typical existing single-story precast RC industrial buildings which were built in Turkey after the year 2000 were examined. The fragility curves for the three typical existing industrial structures were derived from an analytical approach by performing non-linear dynamic analyses considering three different soil conditions. The Park and Ang damage index was used in order to determine the damage level of the members. The spectral acceleration (Sa) was used as the ground motion parameter in the fragility curves. The results indicate that the fragility curves were derived for the structures vary depending on the site conditions. The damage probability of exceedance values increased from stiff site to soft site for any Sa value. This difference increases in long period in examined buildings. In addition, earthquake demand values were calculated by considering the buildings and site conditions, and the effect of the site class on the building damage was evaluated by considering the Mean Damage Ratio parameter (MDR). Achieving fragility curves and MDR curves as a function of spectral acceleration enables a quick and practical risk assessment in existing buildings.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.27-27
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• 2003

Lake Texoma is located on the border of southern Oklahoma and northern Texas. It has 93,000 surface acres, and is a focus of the recreation, and farming industries in the region. There are potential stressors around the Lake Texoma watershed that may cause adverse ecological effects in the lake. System assimilative capacity (SAC) is the ability of abiotic and biotic processes to atteuniate the stressors. SAC Exceeded indicates potential of occuring adverse eco-effects. A number of representative chemical release sites and stressor sources in the surrounding watershed were characterized, and several impact sites having stressors sources, such as being near agriculture, landfills, housing areas, oil production fields and heavy use recreational activity, were selected for surface water, sediment, and groundwater monitoring. A paired reference site, having similar physical characteristics as its impact site, was also chosen based on its proximity to the impact site. Lake water samples were collected at locations identified as marina entrance, gasoline filling station, and boat dock at five marinas selected on Lake Texoma from September 1999 to December 2001. Paired water and sediment samples were also collected. Groundwater samples were collected at about 70 producing monitoring wells. Water quality parameters measured were inorganics (nitrate, nitrite, orthophosphate, ammonia, sulfate, and chloride), dissolved methane, total organic carbon (TOC) (or DOC), volatile organic compounds (VOCs) such as methyl tert-butyl ether (MTBE) and BTEX, and a suite of metals. Biotic communities were evaluated at impact and reference sites. Five basic components were measured; two terrestirial components (plants and bird comminitires) and three aquatic components (benthic inverbrates, litteral-zone fishes, ecosystem attribures). Potential impacts to these comminites were evaluated.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.44-52
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• 2007

This study reviewed the overall process of application of contaminant fate and transport model as part of risk assessment. Site characterization and establishment of a conceptual model prior to establishing or selecting a appropriate model were described. Types of models, model selection guidance, and generic site conditions for model application were presented, the process of model calibration, validation, and sensitivity analysis were reviewed. Objectives of modeling should be defined before model selection, and the complexity of selected models should balance the quantity and quality of available input data with the desired model output. If model output is highly sensitive to an assumed or default value of input parameter, or fate and transport models cannot be adequately calibrated or validated, consideration should be given to other options such as using measured data or using another model.

• Fire Science and Engineering
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• v.32 no.3
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• pp.76-87
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• 2018

This study describes the damage effects modeling for a quantitative prediction about the hazardous distances from pressurized chlorine saturated liquid tank, which has two-phase leakage. The heavy gas, chlorine is an accidental substance that is used as a raw material and intermediate in chemical plants. Based on the evaluation method for damage prediction and accident effects assessment models, the operating conditions were set as the standard conditions to reveal the optimal variables on an accident due to the leakage of a liquid chlorine storage vessel. A model of the atmospheric diffusion model, ALOHA (V5.4.4) developed by USEPA and NOAA, which is used for a risk assessment of Off-site Risk Assessment (ORA), was used. The Yeosu National Industrial Complex is designated as a model site, which manufactures and handles large quantities of chemical substances. Weather-related variables and process variables for each scenario need to be modelled to derive the characteristics of leakage accidents. The estimated levels of concern (LOC) were calculated based on the Gaussian diffusion model. As a result of ALOHA modeling, the hazardous distance due to chlorine diffusion increased with increasing air temperature and the wind speed decreased and the atmospheric stability was stabilized.