Journal
MATHEMATICS
Volume 9, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/math9172072
Keywords
roof risk identification; analytic hierarchy process; cloud model; Dice coefficient; fully mechanized mining workface
Categories
Funding
- National Natural Science Foundation of China Youth Fund [51904082]
- National Natural Science Foundation of China Regional fund [52064005, 52164005, 52164002]
- Guizhou Science and Technology Plan Project (Qianke Science Foundation) [[2020] 1y214]
- Guizhou Science and Technology Plan Project (Qianke Science Support) [[2021] General 399]
- Beijing Natural Science Foundation [2204080]
- National Natural Science Foundation of China [52004010]
- Opening Project Fund of Key Laboratory of Mining Disaster Prevention and Control [MDPC202019]
- Scientific and Technological Innovation Talents Team in Guizhou Province [[2019] 5619]
- China Scholarship Council [201706430044]
Ask authors/readers for more resources
The comprehensive standard cloud model proposed in this study has strong visibility and easily discovers the key parts of risk indexes, solving the problems of ambiguity and quantitative identification in traditional roof risk evaluation methods.
Roof accidents seriously affect the safe and efficient mining of the working faces. Therefore, it is necessary to assess and identify the possible and influencing factors on the occurrence of roof risk in a fully mechanized mining workface. In this study, based on the analytic hierarchy process and fuzzy comprehensive evaluation, a comprehensive standard cloud model was established through constructing a quantitative grade interval and calculating the weight of each index to achieve the aim of a roof risk assessment and identification. The accuracy of risk assessment was ensured by using the comprehensive analyses of various aspects, such as cloud digital features, risk assessment cloud image and standard cloud image. This showed that the main influencing factors on the occurrence of roof accidents were roof separation distance, weighting intensity and rib spalling followed by the coal body stress concentration, initial support force and geological conditions. Taking 42,115 fully mechanized working faces in the Yushen coal mining area as an engineering background, this model was adopted to assess and identify the risk of roof accidents through generating comprehensive assessment cloud images and introducing the Dice coefficient to calculate the similarity degree. The results showed that the overall risk of roof accidents in 42,115 working faces was regarded as grade II (general risk) through the overall index of comprehensive risk evaluation and a similarity degree of 0.8606. The impact of roof condition was mainly influenced by the risk of roof accidents, while the support status, personal working status and coal body condition had a limited effect on the risk of roof accidents. The comprehensive standard cloud model proposed in this study had strong visibility and discovered the key parts of risk indexes easily to solve the problems of ambiguity and quantitative identification in traditional roof risk evaluation methods. Therefore, this model was worth promoting, because it laid the foundation for the intelligent identification and early warning system of roof accident risk in a fully mechanized mining workface.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available