• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.50-59
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• 2021

The main purpose of accident analysis is to identify the causal factors and the mechanisms of those factors leading to the accident. However, current accident analysis techniques focus only on finding the factors related to the accident without providing more insightful results, such as structures or mechanisms. For this reason, preventive actions for safety management are concentrated on the elimination of causal factors rather than blocking the connection or chain of accident processes. This greatly reduces the effectiveness of safety management in practice. In the present study, a technique to model the correlational structure of accident risk factors is proposed by using the co-occurrence keyword network analysis technique. To investigate the effectiveness of the proposed technique, a case study involving a portable ladder fall accident is conducted. The results indicate that the proposed technique can construct the correlational structure model of the risk factors of a portable ladder fall accident. This proves the effectiveness of the proposed technique in modeling the correlational structure of accident risk factors.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.1
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• pp.1-10
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• 2023

This study investigated the accident cases of the U.S. Air Force and the R.O.K. Army. It analyzed the accident factors of the unmanned aircraft system using case analysis on unmanned aircraft system operators of the R.O.K. Air Force. Following the analysis this paper suggested safety operation plans for the R.O.K. Air Force. The risk factors of unmanned aircraft system were summarized by collecting and analyzing accident cases of unmanned aircraft system by the U.S. Air Force, collecting and analyzing accident risk factors of RQ-4 operators of the R.O.K. Air Force. Through the analyzed risk factors, a safety operation plan for the semi-automatic unmanned aircraft system and the fully automatic unmanned aircraft system was presented.

• International Journal of Highway Engineering
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• v.17 no.3
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• pp.117-124
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• 2015

PURPOSES : The purpose of this study is to verify traffic accident injury severity factors for elderly drivers and the relative relationship of these factors. METHODS : To verify the complicated relationship among traffic accident injury severity factors, this study employed a structural equation model (SEM). To develop the SEM structure, only the severity of human injuries was considered; moreover, the observed variables were selected through confirmatory factor analysis (CFA). The number of fatalities, serious injuries, moderate injuries, and minor injuries were selected for observed variables of severity. For latent variables, the accident situation, environment, and vehicle and driver factors were respectively defined. Seven observed variables were selected among the latent variables. RESULTS : This study showed that the vehicle and driver factor was the most influential factor for accident severity among the latent factors. For the observed variable, the type of vehicle, type of accident, and status of day or night for each latent variable were the most relative observed variables for the accident severity factor. To verify the validity of the SEM, several model fitting methods, including ${\chi}^2/df$, GFI, AGFI, CFI, and others, were applied, and the model produced meaningful results. CONCLUSIONS : Based on an analysis of results of traffic accident injury severity for elderly drivers, the vehicle and driver factor was the most influential one for injury severity. Therefore, education tailored to elderly drivers is needed to improve driving behavior of elderly driver.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.40-49
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• 2022

Simple statistical frequency based analysis, such as Pareto analysis, are widely used in conventional accident analysis. However, due to the dynamic and complex nature of construction works, many factors can simultaneously affect or involve the occurrence of accidents in construction projects. Therefore, the identification of the complex relationship between such factors is important to establish relevant and effective safety management policies and/or programs. In this study, characteristic factors and their relationships' contribution to non-fatal accidents in construction projects are analyzed using the association rule mining (ARM) technique. To this end, a total of 59,202 construction accident data are collected from 2015 to 2019 and the ARM is performed to retrieve specific relationships -named as association rules-among classified factors in the data. Characteristics of the retrieved relationships are analyzed and compared with the results of conventional Pareto analysis. Based on the results, it is found that both fall and trip are notable accident forms having characteristic relations with other factors for non-fatal accidents in construction projects. It is also found that small-scale construction, age of 50s, less than 1 month of working period, and architectural construction are important factors for non-fatal accidents in construction projects.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.104-113
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• 2024

Unlike in general manufacturing process, safety management in laboratory-based research area is complicated because the latter generally involves trying untested methods or handling unusual substances in small amounts. Laboratory accidents in South Korea have recently shown an increasing trend. Unfortunately, statistics on such accidents are not officially published by any domestic public agencies. In this study, multivariate analysis was performed on the relationships between variables to develop effective strategies for preventing laboratory accidents. A Cross-Tabulation Analysis of accident-related factors in 179 accident cases revealed that the laboratory type, accident type, and unsafe-act type are all statistically significant, whereas the unsafe condition and management factors differ with the statistical criteria. Furthermore, the results of a Multiple-Correspondence Analysis showed that accidents can be divided largely into three groups having different accident causes and injury types; this confirms the necessity of different strategies to prevent accidents of each type. The findings also reveal differences between the distribution of accident types mentioned in the accident case collection books and actual reported cases. This suggests that an official statistical system administered by a public institution would be necessary for effective prevention of laboratory accidents.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.127-128
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• 2022

The construction industry has a higher disaster rate than other industries, so safety education and management are highly important. In order to reduce the construction accident rate, it is necessary to study the key safety management factors reflecting the characteristics of the construction industry, where there are differences in processes and manpower input for each process, and a small number of managers. Therefore, in this study, key safety management factors for each Process of construction were derived through cross-analysis between safety accident types and accident occurrence objects through disaster case data. The extracted key safety management factors are expected to provide useful information for safety education and supervision of construction sites.

• Journal of the Korean Society of Safety
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• v.17 no.1
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• pp.94-98
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• 2002

This study aimed to search for the fundamental accident causes using a categorical analysis, a kind of statistical methods. As the analysis methods, correlation analysis, independence test and logistic regression analysis were used. And the SPSS package, a general-purpose mathematical library, was used to obtain statistical characteristics. As the result of this study, the accident causes associated with factor of 'lost working days' were factors such as 'employed periods', 'sex', 'type of accident', 'month'. In case of applying independence test method, the most important cause was the factor of 'month'. In case that logistic regression analysis method was applied, the cause contributed to the increase structure'. 'less than 6 month'. On the basis of these results, the plan for accident prevention and the proper investment for accident prevention expenditure could be carried out in each workshop.

• Journal of the Ergonomics Society of Korea
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• v.20 no.2
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• pp.1-28
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• 2001

This paper deals with the selection of the important Influencing Factors (IFs) under accident management situations in nuclear power plants for use in the assessment of human errors. In order to achieve this goal, we collected two types of IF taxonomies, one is the full set IF list mainly developed for human error analysis. and the other is the IFs for human reliability analysis (HRA) in probabilistic safety assessment (PSA). Five sets of IF taxonomy among the full set IF list and ten sets of IF taxonomy among HRA methodologies were collected in the study. From the review and analysis of BRA IFs, we could obtain some insights for the selection of HRA IFs. By considering the situational characteristics of the accident management domain, candidate IFs are chosen. Finally, those IFs are structured hierarchically to be appropriate for the use in the assessment of human error under accident management situation. Three nuclear accidents such as TMI. Chernobyl and JCO were analysed to validate the proposed taxonomy.

• Journal of the Korean Society of Safety
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• v.14 no.1
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• pp.150-157
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• 1999

Developed in the study is the analysis of human factors involved in accidents of Korean Construction Projects. The 556 cases are sampled from a survey material 'Serious Accident Cases in the Construction Projects' edited by Korea Industrial Safety Corporation in 1995 through 1997. The analysis of these cases shows that existing classification system is not good for practical applications in real spots since they are difficult for engineers to understand. In this paper, human factors are classified into three groups by extracting the analysis and arrangement of the cases. Futhermore, We'll propose here a new type of accident which is subdivided. If engineers use this type of accident, they will be probably able to analyze efficiently human factors which are involved in accidents and relatied with how it happen. In the case of fall and upset accidents, personal factors seem to be critical when workers are horizontally moving in an unstable manner, and vertically moving without boarding facilities. Futhermore work environmental factors are important when workers release some materials and are performing building and dismantling tasks.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.188-205
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• 2023

Objectives: Methanol poisoning accidents in smartphone parts manufacturing facilities were analyzed using a systemic accident analysis method, and the necessity and possibility of the application of this systemic method in the field of occupational health were reviewed. Methods: A STAMP model for accident analysis was created based on the previously published literature. CAST analysis was performed to find the causal factors of the components and between the components. Results: The STAMP model visually showed the abstract and complex system control structure. The CAST analysis results could include all the causal factors from the previously published literature, and presented them holistically. Additional causal factors that were not presented in the literature were found. Conclusions: The holistic accident analysis results in this study will be helpful to establish comprehensive measures to prevent methanol or other chemical poisoning accidents. Therefore, it will be necessary to use systemic accident analysis methods in the field of occupational health.