Development of targeted safety hazard management plans utilizing multidimensional association rule mining
Investigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approa...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024167072 |
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| author | Xingbang Qiang Guoqing Li Yuksel Asli Sari Chunchao Fan Jie Hou |
| author_facet | Xingbang Qiang Guoqing Li Yuksel Asli Sari Chunchao Fan Jie Hou |
| author_sort | Xingbang Qiang |
| collection | DOAJ |
| description | Investigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approach mainly focuses on hazard description data, often underutilizing critical information such as hazard time and location. To address this, we integrate topic mining with association rule mining to uncover intrinsic association patterns among various attributes of mine safety hazards. Through a systematic analysis of standardized mining hazard attributes, five key analytical dimensions were identified: Hazard Type, Level, Time, Location, and Responsible Units. A topic mining model, utilizing the Biterm Topic Model, was constructed to reduce dimensionality and aggregate hazard description data. Evaluation indicators such as Standard Lift and Difference Degree were proposed, resulting in a multidimensional association rule mining model for mining safety hazards. In this research, 1387 valid rules were extracted based on hazard inspection data from an underground gold mine in China. The analysis revealed relatively strong associations between hazard location and hazard type, responsible unit, as well as hazard level, with association degrees of 1.934, 1.412, and 1.240, respectively. Additionally, 15 rules with a high degree of differentiation were identified to explore interesting correlations among different attributes. Based on this, corresponding control measures and improvement plans were developed for 19 locations. The results demonstrate that a multidimensional partition-based association rule mining approach for mining safety hazards can significantly enhance the specificity of safety training and improve the efficiency of safety hazard investigation. |
| format | Article |
| id | doaj-art-1aee1d5a26334434beca209ffbedc542 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-1aee1d5a26334434beca209ffbedc5422025-08-20T02:49:56ZengElsevierHeliyon2405-84402024-12-011023e4067610.1016/j.heliyon.2024.e40676Development of targeted safety hazard management plans utilizing multidimensional association rule miningXingbang Qiang0Guoqing Li1Yuksel Asli Sari2Chunchao Fan3Jie Hou4School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China; The Robert M. Buchan Department of Mining, Queen's University, Kingston K7L 3N6, CanadaSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China; Corresponding author. School of Civil and Resource Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing 100083, China.The Robert M. Buchan Department of Mining, Queen's University, Kingston K7L 3N6, Canada; Corresponding author. The Robert M. Buchan Department of Mining, Queen's University, 25 Union Street, Kingston K7L 3N6, Canada.School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaInvestigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approach mainly focuses on hazard description data, often underutilizing critical information such as hazard time and location. To address this, we integrate topic mining with association rule mining to uncover intrinsic association patterns among various attributes of mine safety hazards. Through a systematic analysis of standardized mining hazard attributes, five key analytical dimensions were identified: Hazard Type, Level, Time, Location, and Responsible Units. A topic mining model, utilizing the Biterm Topic Model, was constructed to reduce dimensionality and aggregate hazard description data. Evaluation indicators such as Standard Lift and Difference Degree were proposed, resulting in a multidimensional association rule mining model for mining safety hazards. In this research, 1387 valid rules were extracted based on hazard inspection data from an underground gold mine in China. The analysis revealed relatively strong associations between hazard location and hazard type, responsible unit, as well as hazard level, with association degrees of 1.934, 1.412, and 1.240, respectively. Additionally, 15 rules with a high degree of differentiation were identified to explore interesting correlations among different attributes. Based on this, corresponding control measures and improvement plans were developed for 19 locations. The results demonstrate that a multidimensional partition-based association rule mining approach for mining safety hazards can significantly enhance the specificity of safety training and improve the efficiency of safety hazard investigation.http://www.sciencedirect.com/science/article/pii/S2405844024167072Mine safetySafety hazardAssociation rule miningTopic mining |
| spellingShingle | Xingbang Qiang Guoqing Li Yuksel Asli Sari Chunchao Fan Jie Hou Development of targeted safety hazard management plans utilizing multidimensional association rule mining Heliyon Mine safety Safety hazard Association rule mining Topic mining |
| title | Development of targeted safety hazard management plans utilizing multidimensional association rule mining |
| title_full | Development of targeted safety hazard management plans utilizing multidimensional association rule mining |
| title_fullStr | Development of targeted safety hazard management plans utilizing multidimensional association rule mining |
| title_full_unstemmed | Development of targeted safety hazard management plans utilizing multidimensional association rule mining |
| title_short | Development of targeted safety hazard management plans utilizing multidimensional association rule mining |
| title_sort | development of targeted safety hazard management plans utilizing multidimensional association rule mining |
| topic | Mine safety Safety hazard Association rule mining Topic mining |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024167072 |
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