Semantic Network Analysis Using Construction Accident Cases to Understand Workers' Unsafe Acts
Unsafe acts by workers are a direct cause of accidents in the labor-intensive construction industry. Previous studies have reviewed past accidents and analyzed their causes to understand the nature of the human error involved. However, these studies focused their investigations on only a small numbe...
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| Veröffentlicht in: | International journal of environmental research and public health 2021-12, Vol.18 (23), p.12660 |
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| container_title | International journal of environmental research and public health |
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| creator | Kang, Suhyun Cho, Sunyoung Yun, Sungmin Kim, Sangyong |
| description | Unsafe acts by workers are a direct cause of accidents in the labor-intensive construction industry. Previous studies have reviewed past accidents and analyzed their causes to understand the nature of the human error involved. However, these studies focused their investigations on only a small number of construction accidents, even though a large number of them have been collected from various countries. Consequently, this study developed a semantic network analysis (SNA) model that uses approximately 60,000 construction accident cases to understand the nature of the human error that affects safety in the construction industry. A modified human factor analysis and classification system (HFACS) framework was used to classify major human error factors-that is, the causes of the accidents in each of the accident summaries in the accident case data-and an SNA analysis was conducted on all of the classified data to analyze correlations between the major factors that lead to unsafe acts. The results show that an overwhelming number of accidents occurred due to unintended acts such as perceptual errors (PERs) and skill-based errors (SBEs). Moreover, this study visualized the relationships between factors that affected unsafe acts based on actual construction accident case data, allowing for an intuitive understanding of the major keywords for each of the factors that lead to accidents. |
| doi_str_mv | 10.3390/ijerph182312660 |
| format | Article |
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Previous studies have reviewed past accidents and analyzed their causes to understand the nature of the human error involved. However, these studies focused their investigations on only a small number of construction accidents, even though a large number of them have been collected from various countries. Consequently, this study developed a semantic network analysis (SNA) model that uses approximately 60,000 construction accident cases to understand the nature of the human error that affects safety in the construction industry. A modified human factor analysis and classification system (HFACS) framework was used to classify major human error factors-that is, the causes of the accidents in each of the accident summaries in the accident case data-and an SNA analysis was conducted on all of the classified data to analyze correlations between the major factors that lead to unsafe acts. The results show that an overwhelming number of accidents occurred due to unintended acts such as perceptual errors (PERs) and skill-based errors (SBEs). 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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Accident prevention Accidents Accidents, Occupational Causality Classification Construction accidents & safety Construction Industry Construction site accidents Error analysis Factor analysis Human error Humans Network analysis Occupational safety Onsite Perceptual errors Semantic Web Semantics Statistical analysis Statistical methods Systems Analysis |
| title | Semantic Network Analysis Using Construction Accident Cases to Understand Workers' Unsafe Acts |
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