• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.245-248
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• 2006

This paper presents the results from a survey on root causes that led to fatal accidents in construction projects In Vietnam. The survey was conducted by means of structured questionnaires and interviews with relevant individuals such as Foremen, field engineers, and project managers working in construction companies located in Ho Chi Minh City, the largest city of Vietnam. The survey sample consists of in total 91 fatal accident cases that occurred in construction projects during the years 1996-2005 and were recorded in a report at the Vietnam Department of Labor-Invalids-Social Affairs. The current effort is aimed at determining the essential measures for avoiding fatal accidents that have been increasingly taking place in Vietnam construction firms. The findings from the survey provided a necessary basis for determination of critical factors to be used as safety indexes in developing a checklist for preventing fatal accidents in future construction project

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.41-51
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• 2014

Share of total accidents in construction accidents construction site accident 70% of small embroidery Reducing the rate of Construction of the entire construction accidents decreased overall is a very meaningful work. Disaster reduction continues to increase despite the efforts of a small construction site(Amount less than 2,000,000,000) for Disaster Reduction In order to identify more clearly the cause of the system and provide the urgently needed measures.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.101-108
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• 2013

Industrial disaster caused the deaths of 2,114, construction workers among them was the highest of 621 deaths. In the construction industry, has established a number of safety alternatives to prevent accidents. But until now, the cause of the accident has stopped being superficial analysis, awareness on the root cause of the acciden did not reflect. In this study, we analyze the characteristics and causes in G contractors' safety accidents. And innovation strategy, organization-wide safety management system and detailed tasks to derive essentially was to prevent the occurrence of large construction companies. A lot of business for accident prevention effect was transient and formal, to reflect a management style and organizational culture, and try to prevent construction accidents. we will strive to prevent the disaster from the construction site through the improvement of these.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.245-256
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• 2021

This study is significant in determining the reduction of safety accidents by applying PDCA's theory by subdividing it into a case analysis technique for construction sites by PDCA's theory. For this study, accidents accounted for the largest proportion of each type of disaster in the construction site were considered, and safety accidents were reduced through the PDCA theory through prior research. The analysis method of this study derived improvement plans by applying PDCA techniques to plan, implement, confirm, review, and improve disaster accidents at construction sites. The conclusions of this study are as follows. In the plan, first of all, measures shall be taken to prepare a safety management plan, to verify the implementation of the plan, and to verify the degree of implementation by the field manager. In the implementation, first of all, it is necessary to introduce a safety education history system to strengthen the safety education curriculum to suit the site, as long-term work is impossible for field workers depending on field conditions. First of all, it is necessary to strengthen the installation of safety facilities, including "work scaffolding" and "conducting prevention facilities" at construction sites. In management review and improvement, the risk assessment system for construction sites needs to be expanded first°.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.88-96
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• 2014

Each year, it seems inevitable that major accidents will occur on construction sites. Industrial accidents, sometimes involving foreign laborers, have been constantly increasing. Construction laborers have higher hazard rates and higher work intensity than other industries, which means that they experience more job stress, as a result of the subcontracting structure. Therefore, this study performed an influence factor analysis on job stress and its relevance to industrial accidents involving Korean and foreign construction laborers, and proposed its effectiveness with the job stress and construction accident management measures based on the results of those. A questionnaire to measure job stress was performed targeting Korean and foreign laborers, and the results were analyzed. The results of this study can be utilized as important reference materials in efforts to reduce the job stress of foreign laborers on construction sites, and can be expected to contribute to preventing construction accidents related to job stress in the future.

• 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.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of Distribution Science
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• v.20 no.3
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• pp.23-31
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• 2022

Purpose: The purpose of this study is to analyze the Safety and Logistical Distribution Management system in Thailand's Highway Work Zone based on data from Logistics drivers. Accidents in highway construction zones have caused enormous casualties in Thailand yearly. Statistical data shows evidence of correlation between numbers of accidents and drivers' recklessness. Research design, data, and methodology: In this study, we conducted an in- depth interview with 414 logistics drivers and highway construction workers in Khon Kaen province, Thailand. The data was collected based on 63 questionnaires aiming at capturing factors contributing to the risk of safety and cause of accidents in logistic infrastructures such as Highway work zone. Results: The result reveals two significant factors affecting safety in highway work zone, which includes construction environment and safety management system. Moreover, the result shows that feeling of afraid and confused while driving within the construction zones significantly affecting driver's risk of having an accident. Conclusions: The findings of this study offer that a strategic planning and evaluation of the logistics drivers' satisfaction and construction workers' participation to mitigate highway accidents at construction zones and that drivers' knowledge and perception toward construction safety management plays a significant role in preventing highway accidents at the construction areas.

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.341-350
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• 2019

Construction industry is one of the most dangerous industry. As the construction accidents occur due to the repeated factors found in each accidents, there is a limitation in analyzing all types of occupational accidents by the existing descriptive analysis and statistical test. In this study, we classified safety accidents caused by falling objects among the accident types occurring at construction sites into fatal and nonfatal accidents and deduced the factors. In addition, we deduced the association rules among the safety accidents factors caused by falling objects through the association rule analysis method among the machine learning techniques. Therefore, considering the association rules for fatal and nonfatal accidents proposed in this study, it would be possible to prevent accidents by searching for countermeasures against safety accidents caused by falling objects.