The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

Recently, the crisis of demographic extinction is rising in Korea more than any other country, and it is difficult for industrial sites to maintain without 'foreign workers'. Industrial accidents and accident deaths of foreign workers account for 7.6% and 12.3% of the total, through an in-depth analysis of fatal accidents in the manufacturing industry, differences and similarities between fatal accidents of foreign workers and all fatal accidents were confirmed in terms of occurrence type, workplace size, length of service, employment type, etc. In this study, customized countermeasures were found.

PURPOSES : This study empirically analyzes the determinants of fatal accidents based on raw data on traffic accidents occurring in Chungnam in 2020. METHODS : Regression models based on theoretical arguments for fatal traffic accidents are estimated using a binomial logit model. RESULTS : The prediction model for fatal accidents is affected by the degree of urbanization of the region, month and day of the accident, type of accident, and type of law violation. In addition, speeding or illegal U-turns among law violations appear more likely to result in fatal accidents. The road surface conditions at the time of the accident do not show a significant difference in the probability of fatality among traffic accidents. However, the probability of a fatal accident is rather lower in case of a snowy road; this is plausible, as drivers tend to drive more carefully in bad weather conditions. CONCLUSIONS : Among traffic accidents, fatal accidents appear to be affected by the time and place of the accident, type of accident, and weather conditions at the time of the accident. These analysis results suggest policy implications for reducing fatal accidents and can be used as a basis for establishing related policies.

A reliable prediction model of national occupational accident fatality rate can be used to evaluate level of safety and health protection for workers in a country. Moreover, the socio-economic aspects of occupational accidents can be identified through interpretation of a well-organized prediction model. In this paper, we propose a machine learning based relative comparison methods to predict and interpret a national occupational accident fatality rate based on socio-economic indicators. First, we collected 29 years of the relevant data from 11 developed countries. Second, we applied 4 types of machine learning regression models and evaluate their performance. Third, we interpret the contribution of each input variable using Shapley Additive Explanations(SHAP). As a result, Gradient Boosting Regressor showed the best predictive performance. We found that different patterns exist across countries in accordance with different socio-economic variables and occupational accident fatality rate.

본 연구의 목적은 질산, 황산, 암모니아수, 과산화수소에 대한 생태독성평가를 통해서 사고대비물질들에 대한 기초 독성 데이터베이스를 구축하여, 향후 화학사고 발생시 환경 피해에 관한 의사결정에 과학적 근거를 제공하는 데 있다. 이를 위해 본 연구에서는 사고대비물질 중 토양의 물리·화학적 성질을 변화시킬 수 있는 질산, 황산, 암모니아수, 과산화수소를 대상으로 국내 토착 절지동물인 김어리 톡토기 (Paronychiurus kimi)를 이용한 생태독성평가를 수행하였다. 7일간의 급성독성평가와 28일간의 만성독성평가를 수행하였으며, 시험물질 농도에 따른 토양의 pH 변화를 관찰하였다. 토양의 pH는 질산, 황산, 과산화수소, 암모니아수의 농도가 10,000 mg kg-1 soil dry wt.일 때, 각각 2.86, 2.72, 7.18, 9.69이었다. 질산, 황산, 과산화수소, 암모니아수에 대한 만성독성평가 결과, LC50 값은 각각 2,703, 5,414, 3,158, 859 mg kg-1 soil dry wt.이었으며, P. kimi의 산란 수에 대한 EC50 값은 각각 587, 2,148, 1,300, 216 mg kg-1 soil dry wt.이었다. 비록 본 연구에서는 사고대비물질들의 유입에 따른 토양 pH의 변화만이 조사되었지만, 본 연구의 결과는 P. kimi가 사고대비물질에 의해 변화된 토양의 pH뿐만 아니라 사고대비물질의 유입에 의해 감소된 유기물 함량과 생성된 반응 산물에 의해서도 사망률과 산란수에 영향을 받을 수 있음을 의미한다. 대부분의 사고대비물질들이 토양의 특성을 변화시킬 수 있다는 점을 감안할 때, 토양의 특성 변화와 이에 따른 생물 영향을 고려한 화학사고 후 평가 및 복원 방법이 필요하다.

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

In this study, the current regulations to be adequate in industrial site have to be renew in order to prevent the fatal injuries by mobile cranes. Fatal injury analyses were conducted with several accident cases by the mobile cranes. For each accident, the causes of the injuries were examined and proper safety measures were proposed. This study proposed a revision of the standard guideline as an accident prevention measures through in-depth analysis of fatal accidents. First, fatality rate per 10,000 persons by the mobiles cranes was 0.57 in 2011. Second, the in-depth analysis of the mobile crane accidents showed that the main factors of the accidents were the overturn of mobile crane, the fracture of connection part between mobile crane boom and rope, the breaking of rope and the misunderstanding over the signal between a mobile crane driver and workers around a mobile crane. Third, the order of high defection of connecting parts was bucket bolt, bracket and extension parts. Fourth, since there were several cases of overturn of mobile crane by itself in the in-depth analysis of the mobile crane, the over slop-proof device was proposed as the main alternative in this study. Fifth, the revision of standards of safety factor that shall be more than 10, of connecting parts of mobile crane was proposed.