Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm
The coal industry has always been a typically high-risk industry with frequent accidents and extremely adverse impacts. Cases of accidents in coal mine ventilation systems serve as a concentrated demonstration of accident hazards and hold significant value for identifying key risk factors that may i...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/adce/9579500 |
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| author | Mingjia Jing Guoxing Zhang Dongjie Yang Hongli Qin |
| author_facet | Mingjia Jing Guoxing Zhang Dongjie Yang Hongli Qin |
| author_sort | Mingjia Jing |
| collection | DOAJ |
| description | The coal industry has always been a typically high-risk industry with frequent accidents and extremely adverse impacts. Cases of accidents in coal mine ventilation systems serve as a concentrated demonstration of accident hazards and hold significant value for identifying key risk factors that may induce disasters in coal mine ventilation systems. This paper collects data from 138 coal mine ventilation accident reports, employs the human factors analysis and classification system (HFACS) and the Apriori association rule algorithm, and utilizes Gephi for visualization of the association rules, in order to reveal the relationships among the causes of ventilation accidents. The results indicate that this study, through a combination of text mining, HFACS, Apriori, and collinear network visualization, conducted in-depth mining and analysis of the text data. Fifty-three major accident causes were identified. Based on the weights among these causes, an accident cause network for coal mine ventilation accidents was constructed using association rule support. Network centrality analysis and core-periphery structure analysis were then performed on the ventilation accident cause network. The results show that (1) the 21 core causes, such as the lack of dedicated personnel to manage local ventilators, serious violations of safety regulations by gas inspectors on duty in tunneling faces, unauthorized serial ventilation, inadequate ventilation systems, and the absence of dedicated return airways, play major roles in the occurrence of accidents and are also at the core in social network analysis, (2) the 32 peripheral causes also indirectly affect the core causes. To effectively reduce the occurrence of coal mine accidents, coal mining enterprises should not only clarify the causal relationships among various inducing factors but also focus on strengthening management, training professionals, and establishing a comprehensive emergency ventilation system. These measures will help solve ventilation system problems and ensure safe mine production and worker health. |
| format | Article |
| id | doaj-art-db6e9d5f73694e0db45ca71c75d47a4b |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-db6e9d5f73694e0db45ca71c75d47a4b2025-08-20T02:32:08ZengWileyAdvances in Civil Engineering1687-80942025-01-01202510.1155/adce/9579500Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori AlgorithmMingjia Jing0Guoxing Zhang1Dongjie Yang2Hongli Qin3School of Management and EconomicsSchool of Management and EconomicsSchool of Safety Science and EngineeringSchool of Safety Science and EngineeringThe coal industry has always been a typically high-risk industry with frequent accidents and extremely adverse impacts. Cases of accidents in coal mine ventilation systems serve as a concentrated demonstration of accident hazards and hold significant value for identifying key risk factors that may induce disasters in coal mine ventilation systems. This paper collects data from 138 coal mine ventilation accident reports, employs the human factors analysis and classification system (HFACS) and the Apriori association rule algorithm, and utilizes Gephi for visualization of the association rules, in order to reveal the relationships among the causes of ventilation accidents. The results indicate that this study, through a combination of text mining, HFACS, Apriori, and collinear network visualization, conducted in-depth mining and analysis of the text data. Fifty-three major accident causes were identified. Based on the weights among these causes, an accident cause network for coal mine ventilation accidents was constructed using association rule support. Network centrality analysis and core-periphery structure analysis were then performed on the ventilation accident cause network. The results show that (1) the 21 core causes, such as the lack of dedicated personnel to manage local ventilators, serious violations of safety regulations by gas inspectors on duty in tunneling faces, unauthorized serial ventilation, inadequate ventilation systems, and the absence of dedicated return airways, play major roles in the occurrence of accidents and are also at the core in social network analysis, (2) the 32 peripheral causes also indirectly affect the core causes. To effectively reduce the occurrence of coal mine accidents, coal mining enterprises should not only clarify the causal relationships among various inducing factors but also focus on strengthening management, training professionals, and establishing a comprehensive emergency ventilation system. These measures will help solve ventilation system problems and ensure safe mine production and worker health.http://dx.doi.org/10.1155/adce/9579500 |
| spellingShingle | Mingjia Jing Guoxing Zhang Dongjie Yang Hongli Qin Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm Advances in Civil Engineering |
| title | Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm |
| title_full | Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm |
| title_fullStr | Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm |
| title_full_unstemmed | Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm |
| title_short | Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm |
| title_sort | research on risk identification of coal mine ventilation systems based on hfacs and apriori algorithm |
| url | http://dx.doi.org/10.1155/adce/9579500 |
| work_keys_str_mv | AT mingjiajing researchonriskidentificationofcoalmineventilationsystemsbasedonhfacsandapriorialgorithm AT guoxingzhang researchonriskidentificationofcoalmineventilationsystemsbasedonhfacsandapriorialgorithm AT dongjieyang researchonriskidentificationofcoalmineventilationsystemsbasedonhfacsandapriorialgorithm AT hongliqin researchonriskidentificationofcoalmineventilationsystemsbasedonhfacsandapriorialgorithm |