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Neural Network of Roof Cutting Blasting Parameters Based on Mines with Different Roof Conditions

Author

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  • Xingen Ma

    (State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Manchao He

    (State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Jiandong Sun

    (School of Safety Engineering, North China Institute of Science & Technology, Hebei 065201, China)

  • Haohao Wang

    (State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Xiaoyu Liu

    (State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Enze Zhen

    (State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

Abstract

The design and construction of roof cutting and blasting is a key part of the roof cutting pressure releasing gob-side entry retaining (RCPRGER) technology. In the existing research, the blasting parameters of roof cutting have been primarily determined by field tests. However, the disadvantages of field tests include a complicated process, which hinders the succession of related procedures, and an unstable roof cutting effect. Therefore, in this work, the authors attempt to use a mathematical analysis method to simplify the design process of the key parameters of roof cutting blasting. First, the mechanics process mechanism of surrounding rocks with roof cutting and pressure releasing is investigated, and the stress evolution process of the surrounding rock is divided into the following six stages: original rock stress state, excavation stress state, supporting stress state, roof cutting stress state, premining stress state, and postmining stress state. Furthermore, the relationship between roof cutting and entry retaining from the perspective of Mohr’s stress circle is discussed. Next, using four typical mines, including the Tashan, Yuanlin, Jinfeng, and Hengyuan coal mines, as examples, the existing design methods of roof cutting and blasting, geological data characteristics of each mine, distribution rule of roof cutting connectivity rate, and explosive charge structure of roof cutting blasting are summarized. Based on these results, the logic of roof cutting blasting design is obtained, the key indices affecting blasting design are determined, and the blasting design is defined as a complex fuzzy problem with multiple factors. Finally, based on the study of the above mechanics mechanism and blasting rule, a three-layer back propagation (BP) neural network, including six input units, nine hidden units, and three output units, is developed with the four typical mines as the sample space. This neural network realizes the rapid determination of the three key parameters pertaining to sealing length, blasthole spacing, and the explosive charge weight of a single hole. Through training, the calculation error is less than 0.48%, which considerably simplifies the design process of the blasting parameters. The charge structure parameters can also be determined according to this method. At present, the construction of this neural network has the shortcomings of limited sample space. This problem can be overcome by supplementing the sample size in the subsequent research and practice, which will improve the efficiency and accuracy of this design method and promote the application and development of the RCPRGER technology. The interdisciplinary research reported in this paper is an attempt that uses an intelligent algorithm to simplify the design process of roof cutting blasting in RCPRGER, and it represents not only an application development of the intelligent algorithm, but also a new step regarding the intelligent design of RCPRGER technology.

Suggested Citation

  • Xingen Ma & Manchao He & Jiandong Sun & Haohao Wang & Xiaoyu Liu & Enze Zhen, 2018. "Neural Network of Roof Cutting Blasting Parameters Based on Mines with Different Roof Conditions," Energies, MDPI, vol. 11(12), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3468-:d:189797
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    References listed on IDEAS

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    1. Ameri, Mohammad & Mokhtari, Hamid & Mostafavi Sani, Mostafa, 2018. "4E analyses and multi-objective optimization of different fuels application for a large combined cycle power plant," Energy, Elsevier, vol. 156(C), pages 371-386.
    2. Xingen Ma & Manchao He & Jiong Wang & Yubing Gao & Daoyong Zhu & Yuxing Liu, 2018. "Mine Strata Pressure Characteristics and Mechanisms in Gob-Side Entry Retention by Roof Cutting under Medium-Thick Coal Seam and Compound Roof Conditions," Energies, MDPI, vol. 11(10), pages 1-25, September.
    3. Xiaojie Yang & Eryu Wang & Yajun Wang & Yubing Gao & Pu Wang, 2018. "A Study of the Large Deformation Mechanism and Control Techniques for Deep Soft Rock Roadways," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
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    Cited by:

    1. Jun Yang & Hongyu Wang & Yajun Wang & Binhui Liu & Shilin Hou & Yu Cheng, 2019. "Stability Analysis of the Entry in a New Mining Approach Influenced by Roof Fracture Position," Sustainability, MDPI, vol. 11(22), pages 1-16, November.
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