Uncertainties of factors that influence slope stability have been acknowledged by many geotechnical engineers, and these are drawn from the characterization of the geotechnical parameters of the slope. The characterization is associated with a process of identifying the distribution of random variable values used in the design of a single slope of an open mine, and also part of the probabilistic method which is an alternative approach in estimating the stability of a slope with a Failure Probability value (FP). This paper explains the characterization process using the Kolmogorov-Smirnov (K-S) method, which will determine the most appropriate function for the distribution of the random variable. The characterization results can be used in determining the Safety Factor including the level of confidence and Probability of Failure based on the best fitting function.
Slope design has over the years become the domain of geotechnical engineers. While this has benefited the technology of the slope design processes, it has also separated the the mine design engineers from the responsibility of the risk versus reward relationship.
Given the uncertainties that prevail within the geotechnical discipline, there always exists a probability that a slope may not perform as predicted, and in the worst case can result in a catastrophic failure.
The rock slope stability analysis affected by the random input parameters such as internal friction angle, cohesion, water pressure, seismic acceleration and density, which cannot be properly represented by a single value as input parameters in slope stability analysis. The uncertainty in the values of rock mass properties is a major factor in slope stability analysis.
Each input parameter has a certain distribution function. There are several methods used in characterizing a data distribution, namely: Chi-Square Method, Kolmogorov-Smirnov method, and the method of Anderson Darling. This paper uses the Kolmogorov- Smirnov method. The principle of this method is based on a comparison between the experimental cumulative distribution function and cumulative distribution theoretical assumptions.
The 4 assumptions of the cumulative distribution function will be compared with the empirical distribution functions, namely: normal, lognormal, beta, gamma.
The results of the characterization process will be used for the calculation of the safety factor and failure probability of slopes, and the distribution data are obtained from Tutupan Coal Mine, South Kalimantan, Indonesia.