Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

Details

• Personal Author:
  Brune, Jürgen F. ; Fahle L ; Holley EA ; Petruska AJ ; Walton G
• Description:
  Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety Software
• Keywords:
  Author Keywords: Mobile Laser Scanning Change Detection Computer Models Geotechnical Engineering Geotechnical Monitoring Mining Engineering Octree Data Structures Real-time Computation Rock Falls Rock Mechanics Statistical Inference

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• ISSN:
  2072-4292
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Colorado ; OSHA Region 8
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  15
• Issue:
  7
• NIOSHTIC Number:
  nn:20067668
• Citation:
  Remote Sens 2023 Apr; 15(7):1764
• Contact Point Address:
  Lukas Fahle, Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
• Email:
  lukasfahle@mines.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  Colorado School of Mines, Golden
• Peer Reviewed:
  True
• Start Date:
  20160915
• Source Full Name:
  Remote Sensing
• End Date:
  20210914
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:c7ddeae2badefb92d44f179473a5c8e06b9b8f9ee45d9d048584be5a296ce8d88ef8720cd88810edebf2ecdebcc0c86fe4596d9e788556debe889cffcdb02b12
• Download URL:
  https://stacks.cdc.gov/view/cdc/229937/cdc_229937_DS1.pdf
• File Type:
  [PDF - 5.92 MB ]