Junao Zhu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Junao Zhu has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Chun Zhu
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
1 shared publication - Dong Li
Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
1 shared publication - Haijiang Yang
Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
1 shared publication - Jiandong Sun
Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing 101601, China
1 shared publication - Kexue Zhang
Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology, North China Institute of Science and Technology, Beijing, China
1 shared publication - Lei Kang
School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model
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.
Open AccessResearch on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model
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.