• Title/Summary/Keyword: human accident

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Time uncertainty analysis method for level 2 human reliability analysis of severe accident management strategies

  • Suh, Young A;Kim, Jaewhan;Park, Soo Yong
    • Nuclear Engineering and Technology
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    • v.53 no.2
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    • pp.484-497
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    • 2021
  • This paper proposes an extended time uncertainty analysis approach in Level 2 human reliability analysis (HRA) considering severe accident management (SAM) strategies. The method is a time-based model that classifies two time distribution functions-time required and time available-to calculate human failure probabilities from delayed action when implementing SAM strategies. The time required function can be obtained by the combination of four time factors: 1) time for diagnosis and decision by the technical support center (TSC) for a given strategy, 2) time for strategy implementation mainly by the local emergency response organization (ERO), 3) time to verify the effectiveness of the strategy and 4) time for portable equipment transport and installation. This function can vary depending on the given scenario and includes a summation of lognormal distributions and a choice regarding shifting the distribution. The time available function can be obtained via thermal-hydraulic code simulation (MAAP 5.03). The proposed approach was applied to assess SAM strategies that use portable equipment and safety depressurization system valves in a total loss of component cooling water event that could cause reactor vessel failure. The results from the proposed method are more realistic (i.e., not conservative) than other existing methods in evaluating SAM strategies involving the use of portable equipment.

Prediction of Unsafe Factors for Industrial Accident Prevention (재해예방을 위한 사업장 불안전 요인의 유형 예측)

  • 임현교;장성록;김주홍
    • Journal of the Korean Society of Safety
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    • v.9 no.2
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    • pp.26-32
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    • 1994
  • It is quite similar in the current automated works likewise in the past manual works that single trivial human error and/or unsafe acts may lead to serious industrial accidents. Though the traditional approach for accident prevention focused on the serious injuries or losses, that was misleaded by failure of accident perception. As Heinrich pointed out, there are still enormous numbers of unsafe acts or near-misses before a real accident happen. Thus, for industrial accident prevention, a research on unsafe acts was committed. With accident data occurred during the last decade, statistics were analyzed for extracting behavioral characteristics. After that, a practical method Integrating AHP and statistics which shows possible accident factors and their priority at an individual factory was suggested. A computer program was developed also.

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Injuries Analysis and Interpretation of Standard Age and Sex in KIDAS Accident Statistics (KIDAS 사고 통계에서 표준 연령 남녀의 상해 분석 및 해석연구)

  • Park, Jiyang;Youn, Younghan
    • Journal of Auto-vehicle Safety Association
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    • v.11 no.1
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    • pp.30-35
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    • 2019
  • KIDAS (Korean In-Depth Accident Study) is a data structure of accident investigation type, vehicle breakage and human injury database. A consortium of research institutes, universities, and medical institutions has been established and operated. KIDAS has the strongest difference from the TAAS (Traffic Accident Analysis System), which is the data of the National Police Agency, that it can grasp the injury information of passengers. In this study, the mean age and weight of the most frequent accident types in the KIDAS accident statistics were calculated to determine the degree of injury according to gender. Through the MADYMO analysis, it is aimed to grasp the difference of dummy injury using commercial dummy models and scaling models are currently used.

A Case Study on Aircraft Accidents Due to Air Traffic Controller's Human Error - Applying TEM (Threat & Error Management) Analysis - (항공교통관제사의 휴먼에러에 기인한 국내외 항공기 사고 사례연구 - TEM(Threat & Error Management) 분석법을 적용하여 -)

  • Kim, Jung-Bin;Park, Sung-Sik
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.29 no.4
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    • pp.124-133
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    • 2021
  • The airline industry has been growing steadily since 2016 with more than 100 million air passengers, renewing the largest number of air passengers every year. Increasing air demand leads to an increase in air traffic in limited airspace, increasing the likelihood of accidents between aircraft. Due to the massive human and material damage caused by a single mistake, aviation safety is being heavily focused around the world to efficiently use limited airspace. Studies related to various human factors are underway as most of the aviation accidents are found to be caused by human factors, but research on human factors by controllers is insufficient while they are active in terms of control and operation. Given that 82% of air accidents caused by controllers are caused by human error, the importance of management of human error and changes in perception are urgently needed. This study aims to understand the seriousness of the controller's human error by analyzing the accident cases caused by the controller's human error using TEM to identify threats and errors and derive common human factors.

A Study on the Analysis Method of Safety Cost of Tunnel Accident (터널사고 재난 안전비용 분석 방법에 관한 연구)

  • Baek, Chung-Hyun
    • Journal of the Korea Safety Management & Science
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    • v.23 no.1
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    • pp.23-30
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    • 2021
  • This paper analyzed a survey of 388 general target samples to analyze the correlation between disaster safety costs and human risk factor analysis and evacuation behavior due to tunnel accidents. Considering the impact of the tunnel accident on disaster safety costs and the correlation between human evacuation and risk factors in the tunnel environment, the system should be reorganized to reflect the tunnel's basic plan, tunnel cross-section, tunnel installation.

Comparison of Association Rule Learning and Subgroup Discovery for Mining Traffic Accident Data (교통사고 데이터의 마이닝을 위한 연관규칙 학습기법과 서브그룹 발견기법의 비교)

  • Kim, Jeongmin;Ryu, Kwang Ryel
    • Journal of Intelligence and Information Systems
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    • v.21 no.4
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    • pp.1-16
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    • 2015
  • Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

A Study on Performance Shaping Factors of Human Error in Toxic Gas Facilities (독성가스시설의 인적오류 수행영향인자에 관한 연구)

  • Kim, Youngran;Jang, Seo-Il;Shin, Dongil;Kim, Tae-Ok;Park, Kyoshik
    • Journal of the Korean Institute of Gas
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    • v.18 no.4
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    • pp.68-75
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    • 2014
  • It is necessary to control and evaluate human factors to reduce economic loss by major accident in toxic gas facilities. Conventional works to evaluate hazards have been focused on mechanical and systematic failure, while only a little works have been studied on managing human errors. In this work, a classification system of performance shaping factor (PSF) was suggested to consist human error in managing accident in the toxic gas facilities. Four types of PSFs (human, system, task characteristics, and task environment) were collected, reviewed, and analyzed to be categorized selected according their characteristics of situational, task, and environmental parameters. The PSFs were further modified to set up PSF systems adequate to evaluate human error, and the proposed system to consist PSFs to evaluate human error was further studied through accident analysis in toxic gas facilities.

A study on the moving picture transmission method by railway fibers optics cable (광선로를 경유한 철도현장의 영상전송방안에 관한 연구)

  • Chang Seok-Gahk;Cho Bong-Kwan
    • Proceedings of the KSR Conference
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    • 2004.06a
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    • pp.1465-1467
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    • 2004
  • Compared with other transport means, safety and timeliness are the merits of railways. Unexpectedly when accident happens, much time and human strength are required to cope with the accident. And for swift recovery, systematic rehabilitation is needed. Recently using MTS(Moving picture Transmission System), we can perform accident rehabilitation and recording work efficiently. MTS is the device that transmits continuous picture information from accident field to control center. e are developing the appropriate system to railway situation to make use of the existing information communication technology, processing technology of video-tex, super high speed transmission technology through fiber-optic, copper cable and network description of information Technology, etc. If these communication-based can technologies are applied to railway system, railway managers can control the accident by inspecting the picture of accident field and can contribute to the safe train operation and the improvement of railway management. In this paper, we investigate the connecting methods when optical fiber is used for moving picture data transmission of train accidents, and its problems. And, we validate MTS's performance through about 28km section of field test.

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An Analysis of Safety Accident Trend and Severity for General Workers (보통인부의 안전재해 변화추이 및 재해강도 분석)

  • Shin, Won-Sang;You, Sung-Gon;Lee, Gun-Hyung;Son, Chang-Baek
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2017.11a
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    • pp.50-51
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    • 2017
  • The safety accident of construction industries occur variously in other industries, including other industries, resulting in significant losses of human and material losses. In particular, General worker represents the highest safety accident rate each year, and the various types of accidents are the ones that show the greatest interest in the field, which is the most interesting job in the field. This study aims to identify trends in safety hazards and to analyze the accident severity for major types and influence factors.

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LPG 이송작업시 인적과오에 대한 사상수목분석

  • 김호영;김성영;임현교
    • Proceedings of the Korean Institute of Industrial Safety Conference
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    • 1998.05a
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    • pp.277-284
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    • 1998
  • LPG refueling include a lot of risk done by human beings, dealing with highly combustible gas, so, during the refueling, the leakage initiated by human errors can result in a catastrophic accident. Therefore, this research tried to show what kind of tasks would include the high probability of the human errors and what should be considered for effective safety management in the LPG refueling. At first, 4 typical cases were taken through surveying various accident cases, and then a prototype of the refueling task was presented. And each task was analysed by FTA and ETA. The results showed that overpressure occupies 64.64% of the major reasons for gas leakage, and its probability was approximately 6.62E-06. Among the tasks, gas leakage resulted from mal-assembly of lorry hoses had the highest rate, and human errors related to opening and closing valves of pipe lines were most frequent. Also, the effects of confirming tasks were analyzed quantitatively.

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