• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 추계학술대회 논문집
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• pp.246-251
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• 2005

Accident scenarios analysis is a course to understand, analyze, and describe a process of an accident and behavior pattern of the parties to an accident. The method of accident scenarios is that we described patterns represented between accidents and hazardous conditions, and then provide data to prevent the accident. We have carried out scenarios analysis in various fields so far, but it was not taking account of system. In this research, we made a study on technology of accident scenarios analysis using QFD (Quality Function Deployment) to analyze systematically and evaluate quantitatively types of hazards and scenarios of railway accident. And we analyses accident scenarios of a subject of work-related fatality accident to railway employee and conducted risk assessment for different scenarios. Also we defined relation between unsafe events and hazardous conditions caused to work-related fatality accident, and attempted to quantitatively assess work-related fatality accident and the parties to accidents. The results of this research will be used in analyzing for important causes and contributing factors of work-related fatality accidents at the step of risk assessment of railway system, and quantitatively assessing frequency of work-related accidents and risk.

• 한국안전학회지
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• 제18권3호
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• pp.75-80
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• 2003

This paper presents a set of typical pseudo-accident scenarios related to major equipments in petrochemical plants, which would be useful for performing such quantitative risk analysis techniques as fault tree analysis, event tree analysis, etc. These typical scenarios address what the main hazard of each equipment might be and how the accident might develop from an "initiating event". The proposed set of accident scenarios consists of total thirteen (13) scenarios specific for five (5) major equipments like reactor, distillation column, etc., and has been determined and screened out of one hundred and twenty-five (125) potential accident scenarios that were generated by performing semi-quantitative risk analysis practically for twenty-five (25) petrochemical processes, considering advices from the operation experts. It is assumed that with simple consideration or incorporation of plant-specific conditions only, the proposed accident scenarios could be easily reorganized or adapted for the relevant process with less time and labor by the safety engineers concerned in the petrochemical industries.ndustries.

• 산업공학
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• 제15권2호
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• pp.114-125
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• 2002

The objective of this paper is to describe a systematic accident scenario analysis method(SASA) adept at creating accident scenarios for the design of safer products. This approach was inspired by the Quality Function Deployment(QFD) method, which is conventionally used in quality management. In this study, the QFD provides a formal and systematic scheme to devise accident scenarios while maintaining objectivity. SASA consists of three key stages to be broken down into a series of consecutive steps:(1) developing an accident analysis tableau,(2) devising the accident scenarios using the accident analysis tableau,(3) performing a feasibility test, a clustering process and a patterning process, and finally(4) performing quantitative evaluation of each accident scenario. The SASA was applied to a case study of child safety seats. The accident analysis tableau devised 2828(maximum) accident scenarios from all possible relationships between the hazard factors and situation characteristics. Among them, 270 scenarios were devised through the feasibility test and the clustering process. The patterning process reduced them to 29 patterns representative of all accident scenarios. Based on an intensive analysis of the accident patterns, design guidelines for a safer child safety seat were recommended. The implications of the study on the child safety seat case were then discussed.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3336-3346
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• 2022

In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.

• Nuclear Engineering and Technology
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• 제44권4호
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• 한국항해항만학회지
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• 제41권3호
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• pp.165-172
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• 2017

현행 해양유출사고 시나리오는 예상가능한 최대 유출사고를 근거로 하여 시나리오가 작성되었다. 하지만, 최대유출사고 시나리오와 유사한 규모의 사고는 실제 거의 일어나지 않았는데, 이러한 시나리오를 바탕으로 한 훈련이나 대응장비배치 등은 대비 측면에서 본다면 낮은 비용효율을 가지는 것으로 볼 수 있다. 현행의 시나리오는 활용성과 현장도가 높은 시나리오 구현을 통한 실전에 가까운 형태로 구성될 필요가 있고, 활용 목적에 적합하도록 설계될 필요가 있다. 따라서, 본 연구에서는 과거 사고사례를 바탕으로 한 시나리오 작성을 위해 기 개발된 HNS 사고 표준코드를 활용하여 현행 시나리오를 대체할 수 있는 대체 사고 시나리오(Alternative Accident Scenario)를 구성하고자 하였다. 시나리오는 HNS 사고 표준코드를 모듈화하여 최대 빈도 시나리오, 최대 피해 시나리오, 최대 취약성 시나리오 3가지로 구분하여 작성하였다. 이런 과정을 거쳐 제시된 각 시나리오별로 제시된 사고발생 상황은 실제 사고와 유사한 형태를 나타내므로 현장에서의 합목적적인 활용이 가능할 것으로 판단된다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.661-670
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• 2007

In this study, collision accident scenarios are derived for crashworthy design of rolling stocks because the detailed guidelines to complement domestic safety regulations with respect to collision accidents of rolling stocks are under preparation. Through this study, several collision accident scenarios are broadly investigated for those of advanced countries like USA, UK and EU. Next, the basic engineering considerations which are necessary to derive the collision accident scenarios are reviewed and analysed in some details. Finally, two collision accident scenarios are derived considering the circumstances of domestic railroads.

• 한국산업정보학회논문지
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• 제27권6호
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• pp.95-103
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• 2022

원자력발전소의 중대사고 발생 시 신속하고 정확하게 사고 상황을 파악해야 하며, 이러한 사고진단 정보를 획득했을 때 적절한 사고관리 및 대응을 수행할 수 있다. 본 연구에서는 국가원자력 재난관리 시스템인 AtomCARE (Computerized technical Advisory system for a Radiological Emergency)로 전송되는 주요 발전소 정보로부터 중대사고 상황을 진단하는데 있어 딥러닝 기술의 접목을 고려하였다. 이를 위하여 주요 시나리오를 선정하고 사고 진행에 따른 상세 시나리오에 대하여 중대사고 해석 코드인 MAAP5 다량 계산을 통한 학습 DB를 구축하였다. 그리고 이 DB의 학습을 통하여 주요 발전소 정보로부터 중대사고 상세 시나리오를 분류할 수 있는, 즉 중대사고 상황을 진단할 수 있는 기술을 개발하였다. 또한 블라인드 테스트와 주성분분석을 통한 검증을 수행하였다. 본 연구에서 개발한 기술은 향후 전체 중대사고 시나리오로 확장 및 적용 가능할 것으로 판단되며 신속하고 정확한 사고진단의 기반기술로 활용 가치가 높을 것으로 기대된다.

• 자동차안전학회지
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• 제9권3호
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• pp.46-52
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• 2017

In this study, safety evaluation scenarios on "cut-in" situation are presented to assess the performance of automatic emergency braking systems. The ASSESS project in EU is surveyed for derive efficient test scenarios for cut-in situation. The TASS database are also analyzed to find representative accident scenarios in Korea. With the results of the ASSESS and TASS, the safety evaluation scenarios in cut-in situations are suggested and the scenarios are tested with simulation software PRESCAN.

• 자동차안전학회지
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• 제10권1호
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• pp.27-31
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• 2018

This paper reports an analysis of cyclist accident cases with respect to passenger vehicles on Korean roads. A typology based on Initiative for the Global Harmonization of Accident Data (iGLAD) code book is applied to a traffic accident analysis system(TAAS), which has the real-world crash data on Korea roads, to understand the accident scenarios in more detail and efficiently. Similarly this typology has been used for Germany In-Depth Accidents Study (GIDAS) as well. The accident data analysis with consideration of the typology of Korean road conditions may prioritize traffic safety issues regarding cyclists and is aimed to develop an Automatic Emergency Braking (AEB) system for cyclist. In summary, this paper characterizes and analyzes the scenarios of cyclist crashes with passenger car. The most common accident scenarios on Korean roads are Car-to-Bicyclist Nearside Adult (CBNA) and Car-to-Bicyclist Longitudinal Adult (CBLA), which are more than 86% of total accidents cases. Therefore, it is inferred that AEB cyclist system should include these accident types in the operational design domain to reduce more fatality in Korea.