Geomechanics and Engineering

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Lattice-spring-based synthetic rock mass model calibration using response surface methodology

  • Mariam, Al-E'Bayat (Department of Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology) ;
  • Taghi, Sherizadeh (Department of Mining and Explosives Engineering, Missouri University of Science and Technology) ;
  • Dogukan, Guner (Department of Mining and Explosives Engineering, Missouri University of Science and Technology) ;
  • Mostafa, Asadizadeh (Department of Mining and Explosives Engineering, Missouri University of Science and Technology)
  • Received : 2021.11.28
  • Accepted : 2022.12.07
  • Published : 2022.12.10
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Abstract

The lattice-spring-based synthetic rock mass model (LS-SRM) technique has been extensively employed in large open-pit mining and underground projects in the last decade. Since the LS-SRM requires a complex and time-consuming calibration process, a robust approach was developed using the Response Surface Methodology (RSM) to optimize the calibration procedure. For this purpose, numerical models were designed using the Box-Behnken Design technique, and numerical simulations were performed under uniaxial and triaxial stress states. The model input parameters represented the models' micro-mechanical (lattice) properties and the macro-scale properties, including uniaxial compressive strength (UCS), elastic modulus, cohesion, and friction angle constitute the output parameters of the model. The results from RSM models indicate that the lattice UCS and lattice friction angle are the most influential parameters on the macro-scale UCS of the specimen. Moreover, lattice UCS and elastic modulus mainly control macro-scale cohesion. Lattice friction angle (flat joint fiction angle) and lattice elastic modulus affect the macro-scale friction angle. Model validation was performed using physical laboratory experiment results, ranging from weak to hard rock. The results indicated that the RSM model could be employed to calibrate LS-SRM numerical models without a trial-and-error process.

Keywords

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