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 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.

Authors

Institutions

Publication Details

Journal
Advances in Civil Engineering
Published
2025-01-01
DOI
https://doi.org/10.1155/adce/9579500
Citations
7
Primary Topic
Safety and Risk Management
Type
article
Field-Weighted Citation Impact
19.35

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm

Haide Qin, Guoxing Zhang, Dongjie Yang, Mingjia Jing
7 citations
Advances in Civil Engineering
Safety and Risk Management
19.35
article

Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm

Haide Qin, Guoxing Zhang, Dongjie Yang, Mingjia Jing
article en
7 citations

Abstract

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.

Advances in Civil EngineeringVol. 2025(1)
North China University of Water Resources and Electric Power (CN), Henan Polytechnic University (CN)
Henan University of Technology, Henan University, Henan Provincial Science and Technology Research Project
Openalex Percentile: Top 1%
Safety and Risk Management
19.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.