Estimating the Shear Strength Parameters of Rock Mass using Monte Carlo Method

Document Type : research - paper

Authors

1 Student/Shahid Bahonar University of Kerman

2 Ph. D. candidate/Shahid Bahonar University of Kerman

Abstract

The shear strength parameters of rock mass fall among the most important information required for ‎designing and stability analysis of structures excavated in the rocks. Hoek and ‎Brown have suggested a method to estimate the strength and deformability parameters of jointed rock ‎masses. In rock engineering designs, the mean values of Hoek and Brown parameters are often used. ‎In these cases, probability analysis of rock mass properties is highly important. In this paper, based ‎on the results obtained from rock mechanic tests carried out on rock samples of Gol-e-Gohar iron ‎ore mine, the uncertainties in determining the shear strength parameters of the mine rock masses ‎were modeled according to Hoek and Brown failure criteria and using Monte Carlo simulation ‎method. Since determination of shear strength parameters of rock mass based on a specific ‎confidence level results in the required designs of the following steps to be done based on a specific ‎confidence level, the values of these parameters were determined with a specific confidence level. ‎The simulation results showed that the coefficient of variation and consequently, the sparsity of the ‎simulated data can be caused by the equations used for calculating the output parameters. Using the ‎same inputs, higher uncertainties are found in the mathematical model of the equation that ‎produces outputs of greater coefficient of variation compared to the other equations. Therefore, the ‎coefficient of variation and the sparsity of the simulated data is smaller for the internal friction ‎angle (due to the trigonometric equation) compared to the cohesion data. Moreover, the advantage ‎of using distribution functions with the limits of 0 to +∞ rather than distribution functions with two ‎infinite limits for fitting the data with small numerical values and high sparsity was shown though ‎applying the Monte Carlo simulation method. ‎
 

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