Automatic Extraction of Joint Characteristics from Rock Mass Surface Point Cloud Using Deep Learning

Details

• Personal Author:
  Battulwar R
• Description:
  A methodology for a computerized recognition of joint sets on 3D point cloud models of rock masses using deep learning is presented. The process starts with classifying joints on a 3D rock mass surface through training a deep network architecture and validated using manually labelled datasets. Then, individual joint surfaces are identified using the Density-Based Scan with Noise (DBSCAN) clustering algorithm. Subsequently, the orientations of the identified joint surfaces are computed by fitting least-square planes using the Random Sample Consensus (RANSAC). Finally, the joint planes are classified into different joint sets, and the dip direction and dip angle for each set are calculated. The performance of the proposed methodology has been evaluated using a case study. The results show that the presented procedure is fast, accurate, and robust. Description provided by NIOSH
• Subjects:
  • Automation
  • Occupational Health
  • Safety
• Keywords:
  • Emerging technology
  • Mining engineering
  • Artificial intelligence
  • Rock mechanics
  • Geotechnical engineering
  • Automation
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Dissertation
• Place as Subject:
  Nevada ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-135
• NIOSHTIC Number:
  nn:20068912
• Citation:
  Reno, NV: University of Nevada, Reno, 2021 Dec; :1-135
• Federal Fiscal Year:
  2022
• Performing Organization:
  University of Nevada, Reno
• Peer Reviewed:
  False
• Start Date:
  20190901
• Source Full Name:
  Automatic extraction of joint characteristics from rock mass surface point cloud using deep learning
• End Date:
  20210801
• Download URL:
  https://stacks.cdc.gov/view/cdc/207475/cdc_207475_DS1.pdf
• File Type:
  [PDF - 3.05 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:30eb357d7d8b6c1730a0b343293ca04d4c99abb0f30025d64990781f0bf4358757e66d3c0fb74435dec918a9460d8582af260602187c33bb311ee6122d8dc539