So far, there is no technique of quantitative evaluation on danger related to industrial accidents. Therefore, as an endeavor for obtaining technique of quantitative evaluation, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze feature of industrial accidents, a retrospective analysis was performed in 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work chosen from data related to manufacturing businesses during three years (2002~2004) in Korea. The analysis results were very informative since those enable us to know the most important variables such as occurrence type, company size, and occurrence time which can affect injured people. Also, it is found that classification using QUEST algorithm which was performed in this study is very reliable.

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CHAID algorithm. In this study, data on 10,536 accidents were analyed to create risk groups, Including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002~2004) in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

The main objective of the statistical analysis about industrial accidents is to find out what is the dangerous factor in its own industrial field so that it is possible to prevent or decrease the number of the possible accidents by educating those who work in the fields for safety tools. However, so far, there is no technique of quantitative evaluation on danger. Almost all previous researches as to industrial accidents have only relied on the frequency analysis such as the analysis of the constituent ratio on accidents. As an application of data mining technique, this paper presents analysis on the efficiency of the CHAID algorithm to classify types of industrial accidents data and thereby identifies potential weak points in accident risk grouping.

산업재해 통계분석의 커다란 목적은 각 산업별로 주 위험요인을 도출하고 이에 따른 안전교육의 실시 또는 안전장치 등을 보완함으로써 산업재해를 줄이거나 예방하는데 있다고 볼 수 있다. 그러나 일반 제조업이나 건설업 등에서는 아직까지도 정량적 위험성 평가 기법이 개발되어 있지 않은 실정이다. 따라서 효율적인 위험성 평가 기법의 개발이 필요하다. 본 연구에서는 데이터마이닝 기법을 이용한 산업재해 예방을 위한 최적 알고리즘 선정 방법을 제시한다.

Many researches have been focused on the analysis of industry disasters in order to reduce them. As a similar endeavor, this paper provides a propensity analysis of injured people from various industries using classification and regression tree(CART), a data mining algorithm. The sample for this work was chosen from 25,157data related to various industries during one year ( 2003.2~2004.1 ) at Kangwon-Do in Korea. For the purpose of this paper, eight independent variables (injured date, injured time, injured month, type of Injured person, continuous service period, sex, company size, age)are taken from injured person group. According to the analysis result, it is found that five out of the eight factors that are predicted as significant have salient effects. Factors of season, time/hour, day of the week, or month which disasters happened do not show any significant effect. This paper provides common features of injured people. The provided analysis result will be helpful as a starting point for root cause analysis and reduction of industry disasters and also for development of a guideline of safety management.

고도화된 산업의 발달로 인하여 많은 업종들이 생겨나게 되었고, 그로 인해 크고 작은 사고들이 발생하게 되었다. 사고의 발생은 부상, 사망과 질병 등의 재해자들을 발생시키고, 사고로 인한 재해자의 수는 한 사업장에서도 여러 건씩 발생한다. 이러한 재해자의 공통된 특성 요소와 성향을 분석할 수 있다면, 1%의 재해자라도 더 줄일 수 있으리라 사료된다. 본 논문에서는 업종별 그룹을 통해 재해자들의 특성화 분석을 위하여 총 25,157개의 데이터를 바탕으로 데이터 Mining 기법중의 하나인 Classification and Regression Tree(CART)를 이용하여 분석을 하였다. 종속변수인 10개의 주요 업종을 중심으로 재해자들이 어떠한 성향을 나타내고 있는지 알아보고자 독립변수인 재해자 구분, 재해시간, 근속기간, 성별, 회사 규모와 연령 등 8개 항목을 채택하여 재해자 위험 그룹을 분석하고 그 결과를 제시한다.

해양 구조물은 일반 구조물과는 달리 구조물 속으로 염화물이 침투하기 때문에 시간이 지나면서 빠르게 부식을 하게 되고, 부식이 진행됨에 따라 구조물의 붕괴를 야기 시킨다. 그러므로 염화물의 침투 정도와 부식의 진행 상태를 예측할 수 있다면 사고 예방은 물론, 친환경적 해양 구조물이 될 수 있으리라 사료된다. 본 논문에서는 이러한 해양 구조물의 염화물 침투를 예측하고, 부식으로 인한 구조물 붕괴 및 사고를 예방하고자 Sample Design을 통하여 서로 조건이 다른 시험체를 해수에 잠식시키고, 일정기간이 지난 후 Data를 수집하였다. 수집된 Data는 정규분포(Normal Distribution)를 따르지 않는다는 사실을 알게 되었고, 수집된 데이터를 예측 시스템 구축에 활용하기 위한 준비 단계로서 데이터 선정의 타당성을 검토하기 위해 수명분포분석(Lifetime Distribution Analysis)을 실시한 뒤 그 결과를 제시하였다.

It is not difficult to see the road repairing. There are many and various machines which crush road surface. The efficiency and power of the machine depend on crushing head-shape, crushing interval, crushing load, crushing stress, machine's dropping height and roller's kind. The objective of this study is to select the optimal model among ten models of crushing machine with constraints such as crushing depth, variation, and crushed particle size. And then this paper provides the valid theoretical base on selected model. The data for this study are chosen from the site of construction in Kangnung during three months (2004. 6. 1~2004. 8. 31). The provided methodology in this paper will be fruitful not only for the selection of crushing machine but also for the aspects of construction period, cost, work efficacy according to the condition from the various sites of construction.

It is not difficult to see the road repairing. There are many and various machines that can crush road surface. The efficiency and power of the machine depend on crushing head-shape, crushing interval, crushing load, crushing stress, machine's dropping height and roller's kind. The objective of this study is to select the optimal model among ten models of crushing machine with constraints such as crushing depth, variation, and crushed particle size. The data for this study are chosen from the site of construction in Kangnung during three months (2004. 6. 1 ~ 2004. 8. 31). The provided methodology in this paper will be fruitful not only for the selection of crushing machine but also for the aspects of construction period, cost, work efficacy according to the condition from the various sites of construction.