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Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques
Lluís Sanmiquel1, Marc Bascompta2, Josep M Rossell3
1ICL Chair in Sustainable Mining, Polytechnic University of Catalonia, 08034 Barcelona, Spain. lluis.sanmiquel@upc.edu.
Mining safety improved by identifying key accident causes using data mining. Physical overexertion is the leading cause, with specific factors varying between surface and underground operations.
Area of Science:
- Occupational Health and Safety
- Data Mining
- Mining Engineering
Background:
- Occupational accidents pose significant risks in the mining sector.
- Understanding accident patterns is crucial for effective prevention strategies.
- Previous analyses may not have fully leveraged advanced data-mining techniques.
Purpose of the Study:
- To analyze occupational accidents in the Spanish mining sector (2005-2015).
- To identify key variables and association rules contributing to mining accidents.
- To differentiate accident causes between surface and underground mining scenarios.
Main Methods:
- Utilized data from the Spanish Ministry of Employment and Social Safety (2005-2015).
- Applied data-mining techniques using Weka software.
- Determined the 20 most significant association rules based on statistical confidence levels.
Main Results:
- Physical effort or overexertion due to body movement was the most frequent immediate cause and accident type.
- The second most significant causes and accident types differed between surface and underground mining.
- Identified specific characteristics and contexts associated with mining accidents through association rules.
Conclusions:
- Data-mining is an effective tool for uncovering root causes of mining accidents.
- Findings provide actionable insights for targeted safety interventions in mining.
- The study highlights the need for scenario-specific safety measures in mining operations.
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