• 한국안전학회지
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• 제32권5호
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• pp.179-185
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• 2017

Accident analysis is an essential process to make basic data for accident prevention. Most researches depend on survey data and accident statistics to analyze accidents, but these kinds of data are not sufficient for systematic and detailed analysis. We, in this paper, propose an accident classification model that extracts task type, original cause materials, accident type, and the number of deaths from accident reports. The classification model is a support vector machine (SVM) with word occurrence features, and these features are selected based on mutual information. Experiment shows that the proposed model can extract task type, original cause materials, accident type, and the number of deaths with almost 100% accuracy. We also develop an accident ontology to express the information extracted by the classification model. Finally, we illustrate how the proposed classification model and ontology effectively works for the accident analysis. The classification model and ontology are expected to effectively analyze various accidents.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• 한국안전학회지
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• 제34권3호
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• pp.42-49
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• 2019

This study statistically analyzed the laboratory accidents by investigating 806 laboratory accident survey reports which were officially submitted to government from 2013 to June 2017. After comparing domestic and foreign accident classification criteria, the laboratory accidents were classified using KCD7(Korean Standard Classification of Diseases) and OIICS(Occupational Injury and Illness Classification System) criteria. For the type and part of injury, KCD7 classification criteria was adopted. And, for the cause and occurrence type of accidents, OIICS was adopted to analyze the laboratory accidents. Most of injuries happened to the wrist and hand caused by sharp materials or chemical materials. The analysis of accident cause showed that accidents resulted in medical practice and accidents from handtools and chemical materials such as acid and alkali frequently occurred. The major occurrence types of laboratory accidents was body exposure to the chemical materials such as hydrochloric acid and sulfuric acid. In addition, the accidents resulted in destroy of grasped object or falling object were frequently reported.

• 한국가스학회지
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• 제16권5호
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• pp.21-27
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• 2012

대학 실험실이나 연구시설에서 실험실 사고에 의해 인적 및 물적 손실이 꾸준히 발생되고 있어서 실험실 사고예방의 필요성이 제기되고 있다. 실험실 사고예방을 위해 무엇보다 중요한 것은 사고의 체계적인 분석이다. 국내에서 산업재해에 관한 분석 연구는 수행되어 왔으나, 실험실 사고 분석에 관한 연구는 수행되지 않았다. 본 연구에서는 실험실 사고예방 대책 수립을 위해 설문지를 개발하였고, 설문지는 대학과 연구기관에 전자우편이나 방문조사로 사고사례를 수집하였다. 사고사례 데이터는 계절별, 사고 분류형태, 발생형태, 기인물 및 상해종류 별로 분석 고찰하였다. 이 연구결과는 실험실 종사자의 안전 확보와 사고예방을 위한 기초자료로 활용하고자 하였다.

• 대한건축학회논문집:구조계
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• 제35권8호
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• pp.157-168
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• 2019

Modular Construction is regarded as having enhanced safety compared to traditional construction since most of modular manufacturing process in plants. Unlike general consideration for safety in modular construction, several industrial accident data and studies have pointed out that the accident rate of modular construction is not enough less as much as the practitioners have expected. It means that there is a clear need for improvement of safety management in modular construction. To enhance safety, it is necessary to identify the type and cause of accident through accident cases in order to prevent safety accident in advance. In this consideration, this study analyzed the types and causes of accidents through root cause analysis procedure with accident cases of U.S. OSHA. The classification was carried out in the order of process type, accident type and cause of accident. By following the classification criteria in this study, the causal factor was derived and the root cause map was created. Based on the analysis results, cross-analysis was conducted and it is shown that activity characteristics of modular construction are related to safety accidents. In addition, prevention methods to reduce safety accident by major activity are presented in terms of organizational, educational and technical aspects. This study contributes that the result can be used as the basic safety management in the manufacturing and construction process of modular construction.

• 한국안전학회지
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• 제20권3호
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• pp.112-119
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• 2005

In road tunnel, in order to prevents an accident and minimize the damage of an accident in the case of fire, safety facilities and equipments are integral parts. The type and amount of safety facilities are based on tunnel type and length, traffic flow rate, etc. Therefore many countries use a tunnel classification system that categories tunnel into groups, and specifies the necessary emergency equipment for each group. In this study, for the purpose of classifying tunnel based on tunnel ist investigated the domestic and foreign standards and regulations for safety of road tunnel. As a results, we suggest the method of classification of tunnel by traffic performance, tunnel grade, the volume of traffic, fraction of HGV, rules or regulations for transports of dangerous good through tunnel.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.1180-1186
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• 2007

This paper contains that the current emergency response procedure through the railroad accident type at domestic and foreign, and the response program which is more systematic as a result of improving weaknesses. Because the response program at home is less sufficient than developed country's, we have made an investigation into system of classification in America, British, Germany, Japan for organizing separate emergency response code. we have researched the recognition response of five senses for minimizing the accident damage in a way of spreading the emergency response type analyzed through the accident procedure according to the feature of five senses by the first recognizer. Based on the first recognitions response of five senses, the scenario has been established according to each types and the criteria of accident has been researched through classifying the recognition of five senses response level, early response level, self-response level, outside support level.

• 한국재난정보학회 논문집
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• 제9권1호
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• pp.30-49
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• 2013

본 연구에서는 토지이용에 기반을 두는 새로운 교통사고 예측모형을 개발하였다. 다양한 지역의 특성을 반영할 수 있는 변수에 대한 시장분할 및 추가변수 도입을 토대로 Data Mining 기법의 하나인 의사나무결정법(Classification and Regression Tree)을 활용하여 새로운 유형별 교통사고 예측모형을 개발하였다. 분석결과를 살펴보면 주민등록인구수, 통근 등 활동변수와 활동의 대상이 되는 도로규모, 유발시설 등이 교통사고를 설명하는 변수로 도출되었다.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• 농업과학연구
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• 제45권4호
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• pp.889-903
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• 2018

The purposes of this study were to classify consumer types and to analyze purchase behavior depending on food safety attitudes related to the Fukushima nuclear accident. Based on the consumer survey data, factor analysis and cluster analysis were done to classify consumer types. Applying factor analysis, six factors with a total variance of 73.4% were extracted from 22 basic variables. The 6 factors were termed 'anxiety from ingesting agricultural and fishery products after the nuclear accident', 'importance of food safety when purchasing food', 'reduced rate of purchases of agricultural and fishery products after the accident', 'importance of food prices', 'socio-demographic characteristics' and 'family composition'. Using cluster analysis, consumers were classified into 5 types using the factor scores of 6 factors. The main results are as follows. For every consumer type, the anxiety about imported agricultural and fishery products was higher than that for domestic products after the nuclear accident. The food safety attitudes of consumers were mainly formed by the socio-demographic characteristics. Consumer types with high-education and high-income responded flexibly to the food safety concerning the accident; especially, the consumer type with a high-income and kids under 19 responded very reasonably to the accident. However, consumer types with a low-income and aged over 50 or less than 30 years were the least responsive to the accident, mainly due to financial restrictions. Thus, we suggest some food safety strategies and policy implications based on consumer type for food safety after the accident.