Numerical back analysis of an underground bulk mining operation using distributed optical fiber sensors for model calibration


Nowak S., Sherizadeh T., Esmaeelpour M., Brooks P., Guner D., Karadeniz K. E.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, vol.83, no.3, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 83 Issue: 3
  • Publication Date: 2024
  • Doi Number: 10.1007/s10064-024-03564-6
  • Journal Name: BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Middle East Technical University Affiliated: No

Abstract

Numerical modeling of complex underground engineering projects such as caverns, tunnels, and bulk mining zones is an essential part of the design phase. Large-scale models require significant reductions in complexity from the real-world scenario, which often leads to low confidence in the model output. In this work, a mine-scale numerical model is developed to simulate a room and pillar extraction mining operation. The model inputs are calibrated through the comparison of the model response to pillar extraction in an analogous mine geometry with measured strain values collected using a novel distributed optical fiber strain sensing technique after pillar extraction. Calibration efforts resulted in the identification of the extent of rock mass damage that resulted from the pillar extraction operation. Numerical model inputs were calibrated for geologic strength index (GSI), rock mass damage, discontinuity properties, and applied horizontal stress conditions. A range of calibrated model input parameters are provided, which show good correlation with measured field strain values and are shown to have an accuracy from 78.2 to 89.1% within a 50% tolerance interval of field data. The model calibration presented in this work represents the first step in a calibration process, from which the model will be used for the forward analysis of various mining scenarios, the results of which will inform future calibration and analysis.