Dynamic Failure Predictions Using Machine Learning and Geochemical Data

Details

• Personal Author:
  Lawson, Heather E. ; Meuzelaar, Tom ; Alex, Alice ; Wright, Sam J. S. ; Warren, D. Morgan ; Hanson, David R.
• Description:
  Dynamic coal failure events remain a safety concern in underground coal mining due to their sudden nature and high fatality rate. Previous research utilized Principal Component Analysis on field samples revealing strong empirical correlations between positive failure history and low sulfur content (S) and high volatile matter (VM). Further studies indicated that bumping coals tend to be less mature and less well-cleated than non-failure coals. However, these in situ studies faced the challenge of distinguishing intrinsic coal properties from external geological and stress-related influences. This study augments previous findings through laboratory experimental testing of coal samples that included unconfined compressive strength, chemical composition analysis, and qualitative observations on cleating, combined with compositional data analysis and machine learning techniques, to isolate and understand coal's inherent bursting capacity. These results further confirmed the relationship between coal chemistry and failure classification. Description provided by NIOSH
• Subjects:
  • Artificial Intelligence
  • Coal
  • Coal Mining
  • Mining
  • Occupational Health
  • Safety
• Keywords:
  • Underground mines
  • Coal mines
  • Mining industry
  • Ground monitoring
  • Machine learning
  • Geotechnical engineering
  • Failure analysis
• Document Type:
  Text
• Genre:
  Proceedings
• Place as Subject:
  Atlanta Region OSHA ; Colorado ; Denver Region OSHA ; Georgia ; San Francisco Region OSHA ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  SMRD - Spokane Mining Research Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  15 pdf pages
• NIOSHTIC Number:
  nn:20071825
• Citation:
  MineXchange: 2026 SME Annual Conference & Expo, February 22-25, 2026, Salt Lake City, Utah, preprint 26-027. Englewood, CO: Society for Mining, Metallurgy & Exploration (SME), 2026 Feb; :1-13
• Federal Fiscal Year:
  2026
• Peer Reviewed:
  False
• Download URL:
  https://stacks.cdc.gov/view/cdc/260048/cdc_260048_DS1.pdf
• File Type:
  [PDF - 1.19 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f01938e69e04a0944a6f6402bb0f9e272285ad7d2907850aaa9ccbbbb942f588de1472081a003b92bfda96b88b6fb59d3965def85dd7901f28e6814e3bd5143c