• Nuclear Engineering and Technology
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• v.31 no.6
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• pp.8-22
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• 1999

This study was performed to comparatively evaluate selected Human Reliability Analysis (HRA) methods which mainly focus on cognitive error analysis, and to derive the requirement of a new human error analysis (HEA) framework for Accident Management (AM) in Nuclear Power Plants (NPPs). In order to achieve this goal, we carried out a case study of human error analysis on an AM task in NPPs. In the study we evaluated three cognitive HEA methods, HRMS, CREAM and PHECA, which were selected through the review of the currently available seven cognitive HEA methods. The task of reactor cavity flooding was chosen for the application study as one of typical tasks of AM in NPPs. From the study, we derived seven requirement items for a new HEA method of AM in NPPs. We could also evaluate the applicability of three cognitive HEA methods to AM tasks. CREAM is considered to be more appropriate than others for the analysis of AM tasks, HRMS is also applicable to the error analysis of AM tasks. But, PHECA is regarded less appropriate for the predictive HEA technique as well as for the analysis of AM tasks. In addition to these, the advantages and disadvantagesofeachmethodaredescribed.

• Journal of the Korea Institute of Building Construction
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• v.18 no.2
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• pp.151-159
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• 2018

Accident rate in the construction industry of South Korea is increasing every year, and it represents the highest percentage among industries. This shows that activities performed to prevent safety accidents in the country are not efficient when it comes to reduce the accident rate. In order to resolve this issue, a model for the prediction of human accidents should be established. In addition, it is required a quantification study based on pattern of human accidents. Therefore, the objective of this study is to quantify uncertainty of human accidents risk and predict how to change in various circumstances by using Monte Carlo Simulation. To achieve the objective, first, pattern of human accidents was defined. Second, insurance claim payout and information of human accidents during 14 years in construction site were collected. Third, descriptive analysis is conducted to determine the characteristics of the accident pattern. Fourth, to quantitatively analyze the pattern of the human accidents, the population of each accident occurrence and payout were estimated. Finally, estimated populations was analyzed according to characteristics of distribution by using Monte carlo simulation. In the future, this study can be used as a reference for developing the safety management checklist in construction site and development of prediction models of human accident.

• Journal of Navigation and Port Research
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• v.33 no.3
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• pp.181-191
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• 2009

In general, seafarer's human error is considered to be the preponderant muse for the majority of maritime transportation accidents in a merchant ship. The implementation techniques for Human Error Model (HEM) to assess possible accident risk by deck officers including captain, chief officer, second mate and third mate are described in this study. The scope of this work is focused to 642 deck officers in the ship management company with 130 vessels. At first, HEM can be constructed through the statistical analysis and expert's brainstorming process with human data to 642 deck officers. Then the variables $\upsilon$ for the human factors, the evaluation level EP($\upsilon$) for $\upsilon$, the weight $\alpha$ of $\upsilon$, and the title weight $\beta$ of each deck officers can be decided. In addition, through the analysis of ship's accident history, the accident causation ratios by human error ${\gamma}_H$ and by external error ${\gamma}_B$ can be found as 0.517(51.7%) and 0.483(48.3%), respectively. The correlation coefficients to $\upsilon$ are also shown significant for a 95% confidence interval (p < 0.05) for each coefficient. And the validity of HEM is also surveyed by the analysis of normal probability distribution of risk level RL to each deck officer.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1983-1989
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• 2020

The human reliability analysis is a method by which, in general terms, the human impact to the safety and risk of a nuclear power plant operation can be modelled, quantified and analysed. It is an indispensable element of the PSA process within the nuclear industry nowadays. The paper herein presents a sensitivity study of the human reliability analysis performed on a real nuclear power plant-specific probabilistic safety assessment model. The analysis is performed on a pre-selected set of post-initiator operator actions. The purpose of the study is to investigate the impact of these operator actions on the plant risk by altering their corresponding human error probabilities in a wide spectrum. The results direct the fact that the future effort should be focused on maintaining the current human reliability level, i.e. not letting it worsen, rather than improving it.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.87-98
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• 2011

In this study, we investigated and identified criterial human factors(errors), most of which lead to terrible ship accidents such as collisions, sinking, fire and explosions resulting both in human lives and physical damages to ships as well as surrounding environments. To this end, we went through the accident reports of 413 cases over 2005~2009 period and classified the human factors into 6 major factors with 19 sub ones which were constructed in hierarchical order. The relative importance of major factors was calculated and among others the lack of awareness turned out to be the most important factor with the weight of 0.391. The contributions of the results in the research are two fold: it will help (i) identify the root causes of ship accidents and prevent further potential ship related incidents, (ii) analyze the degree of the risk associated with the ship accidents, when risk analysis is performed.

• Journal of the Korean Society of Safety
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• v.17 no.2
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• pp.106-111
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• 2002

Recently the car and elevator accidents as well as the fire, explosion and collapse increased. The passenger elevator accident from 1993 year to 2001 year has about 56.7% of total accidents and the human victims of 180 according to the Korea Elevator Safety Institute. Accident cause and prevention were investigated for the elevator having the fall, impact and narrowness accident types. The prevention and protection methods of the elevator accident from analysis results were suggested by comparing and analogizing the accident statistics about the accident investigation of the passenger elevator according to the elevator accidents.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• Journal of the Korean Institute of Navigation
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• v.24 no.1
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• pp.23-34
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• 2000

The human error or error involved with human is still the major portion of the causes of marine casualties and attracting a great concern in ship operation. However, there are not so many researches conducted to investigate or develop methodologies for analyzing such causes of human error in maritime industry, which may be caused by the variety of factors affecting the performance of ship operation and the characteristics of human being. This paper aims to study the methodologies used in investigating human factors or errors in maritime field through the investigation of researches performed so far. The methodology for human factors can be usually classified into one of three types; descriptive studies, experimental research and evaluation research. Also there are many different kinds of applied researches for some specific subjects. Prior to the investigation of the root cause of marine accident related with human error, clear concept of the human factors and systematic taxonomy shall be established. Moreover, it is very important considerations in case of casualty inspection on human factors that the inspector on the accident shall have adequate knowledges, understandings on the concept of human factors including the way of research more than those of methodologies and techniques.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.1-8
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• 2009

The studies on semiconductor industrial accident in korea have been focused on the frequencies of each type of safety management employee, characteristics, cause and unsafe conditions, acts and so on. Those attributes of semiconductor industrial accidents were usually analyzed independently, so that it was hard to provides a wel-developed process and systematic guidelines for efficient safety management. Therefore, there were a few studies based on comprehensive survey in terms of the shape type of safe management. The questionnaire survey carried out for the 284 workers who were responsible for safety management in center with cooperation companies in semiconductor industry factor analysis showed that there were three factors of safety management. First, investment and operation and management for accident prevention, Second, unsafe act and condition, safety management Third, general human error and behavior. The industries of respondents were correlative with three groups. Three groups showed statistically significant differences on the number of cases. Actually, the group with the larger investment and the more unsafe cause, human error of accidents prevention had a smaller causes of accident cases.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.5
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• pp.161-168
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• 2019

Construction industry is becoming more advanced, but safety accidents are not decreasing and unsafe act (UA) and human errors (HE) are the main causes of safety accidents. Therefore, this study aims to analyze the relationships between unsafe acts and human errors for construction accident prevents. Specifically, the Correlation Analysis is used to quantify 24 combinations of the relationship between the UA and HE. Then, the Kano Model, and Timko Satisfied Coefficient was utilized to find 6 combinations for construction accident prevention plans. As the result of Timko Satisfied Coefficient, an interview was conducted with three safety managers and 6 safety prevention plan is proposed. Through these results, it is expected that the combination of 24 accidents will be basic data of safety management. Especially, the proposed safety prevention plans considering the characteristics of 6 combinations with high correlation can contribute to prevention of safety accidents at the construction site.