• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3990-4002
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• 2021

CSPACE (Core meltdown, Safety and Performance Analysis CodE for nuclear power plants) for a simulation of severe accident progression in a Pressurized Water Reactor (PWR) is developed by coupling of verified system thermal hydraulic code of SPACE (Safety and Performance Analysis CodE for nuclear power plants) and core damage progression code of COMPASS (Core Meltdown Progression Accident Simulation Software). SPACE is responsible for the description of fluid state in nuclear system nodes, while COMPASS is responsible for the prediction of thermal and mechanical responses of core fuels and reactor vessel heat structures. New heat transfer models to each phase of the fluid, flow blockage, corium behavior in the lower head are added to COMPASS. Then, an interface module for the data transfer between two codes was developed to enable coupling. An implicit coupling scheme of wall heat transfer was applied to prevent fluid temperature oscillation. To validate the performance of newly developed code CSPACE, we analyzed typical severe accident scenarios for OPR1000 (Optimized Power Reactor 1000), which were initiated from large break loss of coolant accident, small break loss of coolant accident, and station black out accident. The results including thermal hydraulic behavior of RCS, core damage progression, hydrogen generation, corium behavior in the lower head, reactor vessel failure were reasonable and consistent. We demonstrate that CSPACE provides a good platform for the prediction of severe accident progression by detailed review of analysis results and a qualitative comparison with the results of previous MELCOR analysis.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2987-3002
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• 2023

In 2015, the number of senior citizens aged 65 and over in Korea was 6,662,400, accounting for 13.1% of the total population. Along with these social phenomena, risk information related to the elderly is increasing every year. In particular, a fall accident caused by a fall can cause serious injury to an elderly person, so special attention is required. Therefore, in this paper, we implemented a system that monitors fall accidents and informs them in real time to minimize damage caused by falls. To this end, beacon-based indoor location positioning was performed and biometric information based on an integrated module was collected using various sensors. In other words, a multi-functional sensor integration module was designed based on Arduino to collect and monitor user's temperature, heart rate, and motion data in real time. Finally, through the analysis and prediction of measurement signals from the integrated module, damage from fall accidents can be reduced and rapid emergency treatment is possible. Through this, it is possible to reduce the damage caused by a fall accident, and rapid emergency treatment will be possible. In addition, it is expected to lead a new paradigm of safety systems through expansion and application to socially vulnerable groups.

• Journal of the Society of Disaster Information
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• v.14 no.1
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• pp.59-64
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• 2018

In the trend of increasing awareness of chemical accidents, hazardous chemical transport vehicle accidents are occurring every year. In this study, we analyzed improvement of accident prevention and countermeasures through statistical analysis of hazardous chemical transport vehicle accidents. A total of 383 chemical accidents between January 2014 and December 2017 were analyzed. During this period, number of transportation accidents was 83 cases, accounting for 21.67% of total chemical accidents. In the current system, despite the direct handling of hazardous chemical, it is out of regulation of damage prediction unlike the workplace. In order to effectively respond to actual accident, information on damage prediction is required and should be shared with related ministry. And it should be developed to real-time monitoring of hazardous chemical transport vehicle through integrated control tower.

• Journal of the Korean Society of Safety
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• v.26 no.6
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• pp.130-136
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• 2011

Since out of a lot of traffic problems traffic accidents cause damage to life and properties of people, it stands out as one of traffic problems which needs improvement, and the loss due to traffic accident negatively affects not only the parties to the accident but also the national economy. Thus, continual concern of the government toward traffic safety is getting bigger and lately each local government is preparing a basic plan for traffic safety and vitalizing traffic safety policies. As expanding the responsibility and role of local governments for traffic safety, traffic safety measures which are based on the characteristics of each local government should be studied. Most of analytical methods in the existing traffic accidents prediction models with macroscopic vision focus on socioeconomic variables such as local population and the number of registered vehicles, and present a great deal of prediction error when they are applied in practice. In this context, this study proposed a traffic accident prediction model in respect of macroscopic level for autonomous districts (administrative districts) of Seoul City. The model development was not based on the entire city but on the type of local land usage (development density) whose relationship with traffic accident frequency was analyzed.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1255-1266
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• 2020

In this study, a complex simulation prototype was developed for rapid and accurate prediction of chemical dispersion range in order to reduce human casualties caused by chemical accidents. Complex simulation considered the leakage momentum during the near-field dispersion to take into account the leakage characteristics of the chemical. In the far-distance dispersion process, the wind distribution of the existing model, which was presented uniformly, was improved using weather and topographical information around the accident site, to realize a wind field similar to the actual one. Finally, the damage range was more precise than the existing model in line with the improved near- and far-distance dispersion process. Based on the results of damage range prediction of the complex simulation, it is expected that it will be highly utilized as a system to support policy decision-making such as evacuation and return of residents after a chemical accident.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.36-37
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• 2014

It is necessary to develop highly sophisticated Modeling & Simulation (M&S) system for the scientific investigation of marine accident causes and for the systematic reproduction of accidental damage procedure. To ensure an accurate and reasonable prediction of marine accidental causes, such as collision, grounding and flooding, full-scale ship M&S simulations would be the best approach using hydrocode, such as LS-DYNA code, with its Fluid-Structure Interaction (FSI) analysis technique. The objectivity of this paper is to present three full-scale ship collision, grounding and flooding simulation results of marine accidents, and to show the possibility of the scientific investigation of marine accident causes using highly sophisticated M&S system.

• Journal of the Korean Institute of Gas
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• v.7 no.4 s.21
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• pp.61-66
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• 2003

The objective of this study is to develop a Cost-Benefit analysis system which would help us to make optimal decision among safety investment alternatives, calculating and comparing costs and benefits for facilities in chemical plants. So, the accident frequency analysis module and the accident damage prediction module were developed for estimating quantitative risks in chemical facilities, and domestic societal risk criterion was presented after the comparative analysis of major industrial cases and societal risk criteria of advanced countries like the Netherlands, Australia, U.S.A., U.K., and Germany. Also, the Cost-Benefit Analysis System which compares the safety investment alternatives based on their deduced net present values was developed through the selection of proper cost and benefit items by field studies

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.251-252
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• 2023

Compared to other industries the construction industry experiences more casualties and property damage due to safety accidents. One of the reasons is the increasing number of foreign workers. For this reason, past studies have found that foreign workers at construction sites are more exposed to safety accidents than non-foreign workers. Nevertheless the proportion of foreign workers involved in safety accidents at construction sites is increasing, and there has been a lack of research to predict the risk of safety accidents at construction sites. Additionally, realistic safety management is lacking due to a lack of safety accident risk prediction research. Therefore, in this study, we would like to propose basic research that proposes an AI-based safety accident prediction model framework for predicting safety accidents of foreign workers at construction sites. The framework and results of this study will contribute to reducing and preventing the risk of safety accidents for foreign workers through risk prediction for safety management of foreign workers at construction sites.