• Journal of the Korean Society of Safety
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• v.20 no.1 s.69
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• pp.113-118
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• 2005

This study delivers the actual condition of investment for industrial accident prevention based on survey of 500 construction sites from 'reports far industry safety and health' published by Korea Occupational Safety & Health Agency (KOSHA). The various research techniques were used such as technical statistic analysis for construction industry, cost comparison of industrial accident prevention and accident loss. A formula was deduced to calculate accident loss and accident frequency by accident prevention cost through regression analysis.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.19-30
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• 2005

This study delivers the actual condition of investment for industrial accident prevention based on survey of 526 construction sites. The various research techniques were used such as technical statistic analysis for construction industry, construction and civil engineering works, cost comparison of industrial accident prevention and accident loss. A formula was deduced to calculate accident loss and accident frequency by accident prevention cost through regression analysis.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.9-18
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• 2005

This study delivers the actual condition of investment for industrial accident prevention based on survey of 526 construction sites. The various research techniques were used such as technical statistic analysis for construction industry, construction and civil engineering works, cost comparison of industrial accident prevention and accident loss. A formula was deduced to calculate accident loss and accident frequency by accident prevention cost through regression analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

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.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.8
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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.

• Journal of the Korea Safety Management & Science
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• v.18 no.2
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• pp.19-27
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• 2016

Safety accident rate of small-medium construction site is high. because of lack of safety management system, lack of safety management capacity, lack of investment for safety, Owner's insufficient awareness about safety. In order to improve this, Currently in Occupational Safety and Health Act, Construction site of amounts more than 300 million won less than 120 billion (architectural),150 billion won(civil) mandatory subject to the technical guidance on construction accident prevention. Context of construction accident causes with construction accident rate relationship analysis and case analysis of technical guidance, through a survey of stakeholders in the technical guidance drawn the problems of the construction accident prevention technical guidance system and ways to improve on this.

• 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.19 no.4 s.68
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• pp.94-100
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• 2004

This study delivers the actual condition of investment for industrial accident prevention based on survey of 500 construction sites from 'reports for industry safety and health' published by Korea Occupational Safety & Health Agency (KOSHA). The various research techniques were used such as technical statistic analysis for construction industry, cost comparison of industrial accident prevention and accident loss. A formula was deduced to calculate accident loss and accident frequency by accident prevention cost through regression analysis.

• 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.37 no.2
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• pp.18-27
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• 2022

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.