The Probabilistic Estimation of Rock Masses Properties in Malmberget Mine, Sweden

 

Idris, Musa Adebayo

PhD student
Division of Mining and Geotechnical Engineering
Luleċ University of Technology, SE-971 87 Luleċ, Sweden
e-mail:idris.musa@ltu.se

Basarir, Hakan

Associate Professor
Division of Mining and Geotechnical Engineering
Luleċ University of Technology, SE-971 87 Luleċ, Sweden

Nordlund, Erling

Professor
Division of Mining and Geotechnical Engineering
Luleċ University of Technology, SE-971 87 Luleċ, Sweden
e-mail: erling.nordlund@ltu.se

Wettainen, Thomas

Research Engineer,
Luossavaara-Kiirunavaara AB, SE-983 81 Malmberget, Sweden
e-mail: thomas.wettainen@lkab.com

 

 ABSTRACT

Numerical modeling techniques have been applied in many mining and civil engineering projects. Traditionally, deterministic methods have been used frequently for the estimation of design or input parameters for numerical modeling. Whereas, it is known that the effect of variability and uncertainty sourced from the complex and variable nature of rock cannot be considered by deterministic approaches using single or mean value. In this paper, the authors tried to apply a probabilistic approach to consider the uncertainties and variability in rock properties. This is to make more a realistic assessment of design parameters of rock masses around an instrumented test drift in Malmberget Mine within the content of the “Rock mass - Rock support interaction project” conducted at the Division of Mining and Geotechnical Engineering, Lulea University of Technology. To calculate the design parameters GSI of rock mass, UCS and mi constant of the intact rock are considered as random variables. For each of these random variables ranges were specified depending on the laboratory and field information. Using Monte Carlo simulation method a possible range of each of necessary strength and deformability properties were obtained and presented. The assessed values can be used as preliminary input parameters and considered as basis for further numerical modeling calibration studies.

Keywords: Rock mass classification systems, the GSI system, Rock material strength, Numerical modeling, probabilistic approach, Monte Carlo simulation.

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