ANALYSIS THE EFFECT OF DISTRIBUTION DATA FOR DETERMINE OF HYPOTHETICAL SLOPE STABILITY BY USING LIMIT EQUILIBRIUM METHOD

• Main Author: Ramadi, Muhammad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/40222
• Institution: Institut Teknologi Bandung

Slope stability is a very crucial condition that must be fulfilled for the smooth operation of mining production. In actual conditions, the value of the safety factor is not enough to express a safe theoretical design in a slope stability practice, where failures still occur on slopes with FS > 1 and stable slopes in FS < 1 so that additional analysis needs to be used besides safety factors, namely the probability of failure. This Final Project Report discusses the analysis of the effect of standard deviation of input parameters on the distribution of data in calculating slope stability (FK standard deviation) and its effect also on the probability of failure and stable probability. This analysis was carried out using two-dimensional numerical modeling using Limit Equilibrium Method with used cohesion data and friction angles obtained from the classification of the RMR rock mass system class IV and class V as input parameters. This analysis of probability of failure is based on the average value and standard deviation (? ± ?) of cohesion, friction angles, and specific weights as input parameters to obtain the value of safety factor (FS). Modeling was carried out using Mohr-Coulomb collapse criteria and used several assumptions such as specific weight data, cohesion, friction angle and safety factors normally distributed, the modeled slope has a height of 20 m, etc. A 50x50 grid is used to facilitate the analysis of the slope stability. In the modeling results, generally obtained FK averages and FK standard deviations which are then analyzed the effect of standard deviation of input parameters using the graph of normal distribution so that the trend of FK data distribution has a rising trend at the input standard deviation which is greater and will affect the probability of failure and the stable probability value. The greatest probability of failure is 33% on a stable slope of class V slope of 27° (FK> 1) with a standard deviation of input parameters 25% and the largest stable probability value is 47% on a failure slope of class V slope of 34° (FK <1) with input parameter standard deviation of 25%.