• DocumentCode
    1802903
  • Title

    Neural network technology for strata strength characterization

  • Author

    Utt, Walter K.

  • Author_Institution
    Res. Lab., Nat. Inst. for Occupational Safety & Health, Spokane, WA, USA
  • Volume
    6
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    3806
  • Abstract
    The process of drilling and bolting the roof is currently one of the most dangerous jobs in underground mining, resulting in about 1,000 accidents with injuries each year in the United States. To increase the safety of underground miners, researchers from the Spokane Research Laboratory of the National Institute for Occupational Safety and Health are applying neural network technology to the classification of mine roof strata in terms of relative strength. In this project, the feasibility of using a monitoring system on a roof drill to assess the integrity of a mine roof and warn a roof drill operator when a weak layer is encountered is being studied. Using measurements taken while a layer is being drilled, one can convert the data to suitably scaled features and classify the strength of the layer with a neural network. The feasibility of using a drill monitoring system to estimate the strength of successive layers of rock was demonstrated in the laboratory
  • Keywords
    computerised monitoring; mining; pattern classification; safety systems; self-organising feature maps; structural engineering computing; Spokane Research Laboratory; drill monitoring; neural network; pattern classification; roof bolting; roof drilling; safety; self organising feature map; strata strength; underground mining; Accidents; Data acquisition; Drilling; Fasteners; Health and safety; Injuries; Laboratories; Monitoring; Neural networks; Occupational safety;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.830760
  • Filename
    830760