Neural Network Technology For Strata Strength Characterization

Details

• Personal Author:
  Utt, Walter K.
• Corporate Authors:
  National Institute for Occupational Safety and Health. Spokane Research Center
• Description:
  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 froth the Spokane Research Laboratory of- the National Institute for Occupational Safety and Health are applying neural network technology to rite 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.
• Subjects:
  Mining
• Source:
  Proceedings of the International Joint Conference on Neural Networks, Mount Royal, New Jersey. Middleton, WI: International Neural Network Society, 1999 Jan; :CD-ROM
• Document Type:
  Text
• Genre:
  Conference Publications
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• NIOSHTIC Number:
  nn:20024659
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:2e2d7ef86961b5f3036eb4d6c6ce101400b6afcc30ddaeb6990e1ceb85e132239429f51dfc9f5f5ca6b36f4ab8b7802a1f580fbcb1fc5a29398d5c54b80cc15f
• Download URL:
  https://stacks.cdc.gov/view/cdc/9128/cdc_9128_DS1.pdf
• File Type:
  [PDF - 572.69 KB ]