Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model

Kexue Zhang, Junao Zhu, Manchao He, Yaodong Jiang, Chun Zhu, Dong Li, Lei Kang, Jiandong Sun, Zhiheng Chen, Xiaoling Wang, Haijiang Yang, Yongwei Wu and Xingcheng Yan
Additional contact information
Kexue Zhang: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Junao Zhu: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Manchao He: State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Yaodong Jiang: State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Chun Zhu: School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Dong Li: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Lei Kang: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Jiandong Sun: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Zhiheng Chen: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Xiaoling Wang: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Haijiang Yang: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Yongwei Wu: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
Xingcheng Yan: Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China

Energies, 2022, vol. 15, issue 9, 1-14

Abstract: Coal seam impact risk assessment is the premise of coal mine safety, which can reduce the occurrence of underground impact pressure accidents and directly affect the safety, coal production, economic and social benefits of coal mining enterprises. In order to evaluate the impact risk of coal seams more reasonably and comprehensively, and consider the weights of different influencing factors on the impact risk of coal seams, the neural network model is proposed to evaluate the impact risk of coal seams. Mining depth, impact tendency, geological structure and mining technology are selected as the influencing factors of coal seam impact risk. Each influencing factor contains different evaluation indices, a total of 18. The 18 evaluation indices and the impact risk level are normalized and quantified. The BP neural network model for evaluating coal seam impact risk level is established, and the impact risk of 2-1 coal seams in a mine in Inner Mongolia is comprehensively evaluated and analyzed in this study. The results show that the BP neural network model can represent coal seam impact risk level well. The application of the BP neural network model to evaluate coal seam impact risk level has the characteristics of high precision, fast calculation speed and less artificial calculation, which provides an efficient and convenient method for the evaluation of coal seam impact risk.

Keywords: coal seam; coal bump; impact risk; comprehensive evaluation; BP neural network; intelligent (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/9/3292/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/9/3292/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:9:p:3292-:d:806639

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().