Volume 45 Issue 12
Turn off MathJax
Article Contents
Abstract
Related Articles
LIU Wei, WU Jiwen, HU Ru, BI Yaoshan, ZHAI Xiaorong, XU Feng. Quantitative evaluation of mine structure complexity and its coupling analysis with water bursting[J]. Journal of Mine Automation, 2019, 45(12): 17-22. DOI: 10.13272/j.issn.1671-251x.2019040102
Citation: LIU Wei, WU Jiwen, HU Ru, BI Yaoshan, ZHAI Xiaorong, XU Feng. Quantitative evaluation of mine structure complexity and its coupling analysis with water bursting[J]. Journal of Mine Automation, 2019, 45(12): 17-22. DOI: 10.13272/j.issn.1671-251x.2019040102
shu

Quantitative evaluation of mine structure complexity and its coupling analysis with water bursting

  • School of Earth and Environment, Anhui University of Science and Technology, Huainan 232000, China

More Information
    Graphical Abstract
    • Abstract
  • Most mine inrush accidents are closely related to mine geological structure, so accurate evaluation of mine structure complexity is of great significance for mine water disaster prevention and control. Taking Xuzhuang Coal Mine as a research object, fault fractal dimension value, fault intensity index and folding plane deformation coefficient were selected as evaluation indexes affecting mine structure complexity. The independent weight coefficient method was used to determine weight of each evaluation index. The quantitative evaluation model of mine structure complexity was established by ArcGIS to divide the study area into the most complex structure area, more complex structure area, middle complex structure area and simple complex structure area, and coupling relationship between mine structure complexity and mine water bursting was analyzed. The results showed that the study area is dominated by the most complex structure and mine structure complexity is mainly controlled by large faults and folds. The water bursting points are more densely distributed with increase of mine structure complexity when fault fractal dimension value, fault intensity index and folding plane deformation coefficient become greater. It indicates that mine structure has an obvious control effect on water bursting.
    • FullText(HTML)
    • References (0)
    • Related Articles

  • Related Articles

    [1]LI Dianze, XU Huajie, ZHANG Bo. Rock fracture type recognition based on deep feature learning of microseismic signals[J]. Journal of Mine Automation, 2025, 51(3): 156-164. DOI: 10.13272/j.issn.1671-251x.2024080043
    [2]GUO Xiaoyuan, ZHU Meiqiang, TIAN Jun, ZHU Beibei. Detection and recognition of unsafe behaviors of underground coal miners based on deep learning[J]. Journal of Mine Automation, 2025, 51(3): 138-147. DOI: 10.13272/j.issn.1671-251x.2025030011
    [3]ZUO Mingming, ZHANG Xi, YANG Zihao, SUN Qifei, ZHANG Mengchao, ZHANG Yuan, LI Hu. Intelligent monitoring method for conveyor belt misalignment based on deep learning[J]. Journal of Mine Automation, 2024, 50(12): 166-172, 182. DOI: 10.13272/j.issn.1671-251x.2024030043
    [4]CHEN Wanhui, GUO Rui, HAN Wei, SONG Yongming, LIANG Yanxiang, LIU Yao, WANG Jiaming, XU Na, MENG Bo. Research on intelligent design of coal mine roadway support scheme[J]. Journal of Mine Automation, 2024, 50(8): 76-83, 90. DOI: 10.13272/j.issn.1671-251x.2024060044
    [5]LAN Yongqing, QIAO Yuandong, CHENG Hongming, LEI Lixing, LUO Huafeng. Gas concentration prediction model based on SSA-LSTM[J]. Journal of Mine Automation, 2024, 50(2): 90-97. DOI: 10.13272/j.issn.1671-251x.2023090067
    [6]LI Jincai, FU Wenlong, WANG Renming, CHEN Xing, MENG Jiaxin. Intelligent fault diagnosis of rolling bearings based on deep network[J]. Journal of Mine Automation, 2022, 48(4): 78-88. DOI: 10.13272/j.issn.1671-251x.2022010008
    [7]HU Jinghao, GAO Yan, ZHANG Hongjuan, JIN Baoquan. Research on the identification method of non-coal foreign object ofbelt conveyor based on deep learning[J]. Journal of Mine Automation, 2021, 47(6): 57-62. DOI: 10.13272/j.issn.1671-251x.2021020041
    [8]ZHANG Mengchao, ZHOU Manshan, ZHANG Yuan, YU Yan, LI Hu. Damage detection method for mine conveyor belt based on deep learning[J]. Journal of Mine Automation, 2021, 47(6): 51-56. DOI: 10.13272/j.issn.1671-251x.2021040010
    [9]LI Changwen, CHENG Zeyin, ZHANG Xiaogang, DING Hua. Fault diagnosis of shearer rocker gear based on deep residual network[J]. Journal of Mine Automation, 2021, 47(3): 71-78. DOI: 10.13272/j.issn.1671-251x.2020110043
    [10]TANG Shiyu, ZHU Aichun, ZHANG Sai, CAO Qingfeng, CUI Ran, HUA Gang. Target detection of underground personnel based on deep convolutional neural network[J]. Journal of Mine Automation, 2018, 44(11): 32-36. DOI: 10.13272/j.issn.1671—251x.2018050068

  • Cited by

    Periodical cited type(1)

    1. 刘温良，彭国兰. 基于聚类算法的散货码头装卸车作业任务的智能识别系统. 福建技术师范学院学报. 2024(05): 64-74 .

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views PDF downloads Cited by(1)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1172563    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return