Using Machine Learning to Evaluate Coal Geochemical Data with Respect to Dynamic Failures

Details

• Personal Author:
  Hanson, David R. ; Lawson H
• Description:
  In this study, NIOSH researchers conducted a machine learning analysis to examine whether a model could be constructed to assess the probability of dynamic failure occurrence based on geochemical and petrographic data. Random forest, cluster analysis and dimension reduction were all applied. The objective of dimensionality reduction was to explore patterns and groupings in the data and search for relations between compositional parameters. Cluster analyses were performed to determine if an algorithm could find clusters with given class memberships and to what extent misclassifications of dynamic failure status occurred. A random forest analysis performed on data from the Pennsylvania Coal Sample Databank cross-referenced with accident data from the Mine Safety and Health Administration (MSHA) determined that 7 parameters of the 18 examined exerted the most influence on results. Cluster analysis on data after dimensionality reduction resulted in a hierarchal clustering algorithm finding four clusters, with one relatively distinct dynamic failure cluster, and three clusters consisting mostly of control group members but with a small number of dynamic failure members. Description provided by NIOSH
• Subjects:
  • Mining
  • Occupational Health
  • Safety
• Keywords:
  • Mining industry
  • Coal mines
  • Geologic models
  • Structural failure
  • Machine learning
  • Artificial intelligence
• Series:
  Mining Publications
• ISBN:
  9781713872368
• Document Type:
  Text
• Genre:
  Proceedings
• Place as Subject:
  OSHA Region 10 ; 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:
  1-9
• NIOSHTIC Number:
  nn:20067848
• Citation:
  MineXchange: 2023 SME Annual Conference and Expo, February 26-March 1, 2023, Denver, Colorado, preprint 23-026. Englewood, CO: Society for Mining, Metallurgy & Exploration, 2023 Feb; :1-9
• Federal Fiscal Year:
  2023
• NORA Priority Area:
  Mining
• Peer Reviewed:
  False
• Source Full Name:
  MineXchange: 2023 SME Annual Conference and Expo, February 26-March 1, 2023, Denver, Colorado, preprint 23-026
• Download URL:
  https://stacks.cdc.gov/view/cdc/215596/cdc_215596_DS1.pdf
• File Type:
  [PDF - 347.27 KB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:dc7b6e7ee4ccfdf9d87502c2d0eadb41d2b388268b186e22fae509cca3a7b0bc40edd7fa7f820dbff1a53d14ea46033a833724299d42b19c7aeb4bfa2e5eb178