Neuro-fuzzy prediction model of occupational injuries in mining

被引:1
作者
Ivaz, Jelena S. [1 ]
Petrovic, Dejan V. [1 ]
Stojadinovic, Sasa S. [1 ]
Stojkovic, Pavle Z. [1 ]
Petrovic, Sanja J. [2 ]
Zlatanovic, Dragan M. [1 ]
机构
[1] Univ Belgrade, Tech Fac Bor, Belgrade, Serbia
[2] Min & Met Inst Bor, Mineral Proc, Bor, Serbia
关键词
coal mine; occupational injury prevention; neural networks; fuzzy logic theory; COAL-MINES; ACCIDENTS; SAFETY; BEHAVIORS; INDUSTRY; AGE;
D O I
10.1080/10803548.2024.2401678
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Objectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, machinery and limited workplaces. Methods. A universal model for predicting occupational accidents takes into account influential factors such as organizational aspects, personal and collective protective equipment, on-the-job training and leadership factors. The selected networks achieved a prediction accuracy of >90%. Results. The study successfully identifies potential risks and critical worker groups leading to injuries. The sensitivity analysis provides insights for targeted safety measures and improved organizational practices. Conclusion. This data-driven approach makes a valuable contribution to safety in the mining industry. Implementation of the predictive model can reduce injuries and machine damage, and improve worker well-being.
引用
收藏
页码:24 / 33
页数:10
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